File restructure #1
This commit is contained in:
@ -1,235 +0,0 @@
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*! version 1.0.2 13oct2009 caplog by roywada@hotmail.com
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*! captures a log file, possibly for use with logout or dataout
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program define caplog
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version 6
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local logfile : log
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version 7
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* invisible to Stata 7
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local Version7 ""
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cap local Version7 `c(stata_version)'
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if "`Version7'"=="" {
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* it is version 7
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local bind ""
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*noi di in yel "limited functions under Stata 7"
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}
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else if `Version7'>=8.2 {
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version 8.2
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local bind "bind"
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}
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*qui log query
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*if `"`r(status)'"'=="on" {
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* qui log close
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* local filename `"`r(filename)'"'
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*}
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if `"`logfile'"'~="" {
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di ""
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qui log close
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local filename `"`logfile'"'
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}
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* embbed to avoid log being open
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_caplog `0'
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cap log close
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*** c_locals coming back
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* clickables
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if "`tempfile'"~="tempfile" {
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if "`smcl'"=="" {
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local cl_text `"{browse `"`using1'"'}"'
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noi di as txt `"`cl_text'"'
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}
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else {
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local cl_text `"{stata `"view `using1'"':`using1'}"'
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noi di as txt `"`cl_text'"'
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}
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}
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cap log close
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*if `"`filename'"'~="" {
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* log using `"`filename'"', append
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*}
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end
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********************************************************************************************
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program define _caplog
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version 7
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local Version7 ""
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cap local Version7 `c(stata_version)'
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if "`Version7'"=="" {
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* it is version 7
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local bind ""
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*noi di in yel "limited functions under Stata 7"
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}
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else if `Version7'>=8.2 {
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version 8.2
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local bind "bind"
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}
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* encase the colon in file name in quotes, avoiding string function length limits
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local behind `"`0'"'
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local 0 ""
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gettoken front behind: behind, parse(" ,")
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local 0 ""
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local done 0
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while `"`front'"'~="" & `done'==0 {
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if `"`front'"'=="using" {
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gettoken rest behind: behind, parse(" ,")
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* strip off quotes
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gettoken first second: rest, parse(" ")
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cap local rest: list clean local(rest)
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* take off colon at the end
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local goldfish ""
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if index(`"`rest'"',":")~=0 {
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local end=substr(`"`rest'"',length(`"`rest'"'),length(`"`rest'"'))
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if "`end'"==":" {
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local rest=substr(`"`rest'"',1,`=length(`"`rest'"')-1')
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local goldfish " : "
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}
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}
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* colon reattached with a space at the end
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* .txt attached here, SMCL TO BE FIXED LATER
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local rabbit `"""'
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if index(`"`rest'"', ".")==0 {
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local using `"`rabbit'`rest'.txt`rabbit'`goldfish'"'
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}
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else {
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local using `"`rabbit'`rest'`rabbit'`goldfish'"'
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}
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local 0 `"`0' using `using' `behind'"'
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local done 1
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}
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else {
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local 0 `"`0' `front'"'
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gettoken front behind: behind, parse(" ,")
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}
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}
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gettoken first second : 0, parse(":") `bind' match(par) quotes
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local 0 `"`first'"'
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while `"`first'"'~=":" & `"`first'"'~="" {
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gettoken first second : second, parse(":") `bind' match(par) quotes
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}
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if `"`0'"'==":" {
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* colon only when shorthand combined with prefix
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local 0
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}
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else {
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local _0 `"`0'"'
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}
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*** shorthand syntax if [using] is missing
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syntax using/ [, replace append tempfile text smcl subspace]
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if "`smcl'"=="smcl" {
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if index(`"`using'"', ".txt")~=0 {
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local temp=substr(`"`using'"',1,length(`"`using'"')-4)
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local using `"`temp'.smcl"'
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}
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}
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if "`text'"~="" & "`smcl'"~="" {
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di "cannot choose both {opt text} and {opt smcl}"
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exit 198
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}
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if "`text'"=="" & "`smcl'"=="" {
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local text "text"
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}
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cap confirm file `"`using'"'
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if !_rc & "`replace'"~="replace" & "`append'"~="append" {
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* it exists
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noi di in red `"`using' exists; specify {opt replace} or {opt append}"'
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exit 198
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}
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* goes with `second'
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if `"`second'"'~="" {
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local _colon ":"
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}
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qui {
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if "`subspace'"=="subspace" {
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* fix the gaps in the value labels
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ds8
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foreach var in `r(varlist)'{
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local temp : var label `var'
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local temp = subinstr(`"`temp'"'," ","_",.)
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label var `var' `"`temp'"'
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}
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}
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}
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* regular stuff
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if `"`using'"'~="" {
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* prefix use using file
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qui log using `"`using'"', `replace' `append' `text' `smcl'
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`second'
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}
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else {
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* prefix use temp file
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qui log using `"`using'"', `replace' `append' `text' `smcl'
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`second'
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}
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* clickables
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c_local smcl "`smcl'"
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c_local using1 `"`using'"'
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c_local tempfile `"`tempfile'"'
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end
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********************************************************************************************
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*** ripped from outreg2 Mar 2009
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program define ds8
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* get you the list of variable like -ds- does for version 8
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version 7.0
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qui ds
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if "`r(varlist)'"=="" {
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local dsVarlist ""
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foreach var of varlist _all {
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local dsVarlist "`dsVarlist' `var'"
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}
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c_local dsVarlist `dsVarlist'
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}
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else {
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c_local dsVarlist `r(varlist)'
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}
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end
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/*
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* version 1.0.1 May2009 caplog by roywada@hotmail.com
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smcl accepted
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version control fixed
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1.0.2 close the log file at the end to avoid the possibility of it being left open
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@ -1,64 +0,0 @@
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{smcl}
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{* 13oct2009}{...}
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{cmd:help caplog}
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{hline}
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{title:Title}
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{p2colset 5 16 22 2}{...}
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{p2col :{hi: caplog} {hline 2}}captures a ASCII log file (for use with {cmd:{help logout}}){p_end}
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{marker s_Syntax}
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{title:Syntax}
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{p 4 4 6}
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{cmdab:caplog} using filename [, {it:options}] : command
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{marker s_Description}
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{title:Description}
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{p 4 4 6}
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{cmd:caplog} provides a fast and easy way to capture text-based log file, possibly for use with
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{cmd:{help logout}}. The default is text log files.
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{title:Command}
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{p 4 12 6}Any command accepted, i.e. { help estimation commands}, tabulation, summary, descrition, etc.
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{marker s_Options}
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{title:Options}
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{dlgtab:Main}
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{p 4 12 6}{opt replace} Replace pre-exiting log files.{p_end}
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{p 4 12 6}{opt append} Append. {p_end}
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{p 4 12 6}{opt smcl} Save smcl file instead of text file. {p_end}
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{marker s_0}
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{title:Examples}
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{p 4 4 6}{cmd:* table}{p_end}
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{p 4 4 6}{stata sysuse auto, clear}{p_end}
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{p 4 4 6}{stata `"caplog using mystuff.txt, replace: table trunk rep78 , c(n mpg mean mpg sd mpg median mpg)"'}{p_end}
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{p 4 4 6}{stata `"caplog using mystuff.txt, append: table trunk rep78 , c(n mpg mean mpg sd mpg median mpg)"'}{p_end}
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{title:Remarks}
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{p 4 12 6}PREVIOUSLY OPENED LOG FILES WILL BE CLOSED BY CAPLOG.{p_end}
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{p 4 12 6}version 7: do not include colon characters (:) in the file path. Use -cd- instead.{p_end}
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{title:Author}
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{p 4 4 6}Roy Wada{p_end}
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{p 4 4 6}roywada@hotmail.com{p_end}
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@ -1,167 +0,0 @@
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*! 1.0.1 - allow either y() or choice(); yrank() or rank()
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* 1.0.0 - revise option names: case->casevar; id->case
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* 0.2.4 - small text formatting error
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* 0.2.3 - change _optnum to altnum and add as option
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* 0.2.2 - allow id() to specify new variable instead of using _id - 15Jul2005
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* 0.2.1 - change specification of csv from varlist to case() - 11Jul2005
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program define case2alt
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version 9
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syntax , [y(varname numeric) YRank(name) case(name) ///
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Generate(name) REplace CASEVars(namelist) Alt(namelist) noNames altnum(name) ///
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Choice(name) Rank(name)] // allow choice() and rank() to sub for y() and yrank()
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** CHANGE 9/8/05: allow choice() instead of y() and allow rank() instead of yrank()
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if "`choice'" != "" {
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local y "`choice'"
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local choice ""
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}
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if "`rank'" != "" {
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local yrank "`rank'"
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local rank ""
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}
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* either y() or yrank() must be specified
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if "`y'" == "" & "`yrank'" == "" {
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di as err "option y() or yrank() must be specified"
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exit 198
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}
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* figure out if rankdata is being specified
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local rankdata "yes"
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if "`y'" != "" {
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local rankdata "no"
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}
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if "`rankdata'" == "no" & "`generate'" == "" & "`replace'" == "" {
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di as txt "(note: " as res "choice " as txt "used for outcome since no variable specified by gen())"
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confirm new variable choice
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local generate "choice"
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}
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local choice "`generate'"
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if "`case'" == "" {
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di as txt "(note: variable " as res "_id" as txt " used since case() not specified)"
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confirm new variable _id
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gen _id = _n
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local case "_id"
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}
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if "`altnum'" == "" {
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di as txt "(note: variable " as res "_altnum" as txt " used since altnum() not specified)"
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confirm new variable _altnum
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local altnum "_altnum"
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}
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capture confirm variable `case'
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if _rc != 0 {
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gen `case' = _n
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}
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if "`rankdata'" == "no" {
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_pecats `y'
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local catval = r(catvals)
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local catnms = r(catnms)
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local numcats = r(numcats)
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forvalues i = 1(1)`numcats' {
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local y`i' : word `i' of `catval'
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local ynm`i' : word `i' of `catnms'
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confirm integer number `y`i''
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gen _ytemp`y`i'' = 1
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}
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local ytemp "_ytemp"
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}
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if "`rankdata'" == "yes" {
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capture label drop `altnum'
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foreach rvar of varlist `yrank'* {
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local rvarnum = subinstr("`rvar'", "`yrank'", "", 1)
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local rvarnumchk = real("`rvarnum'")
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if "`rvarnum'" == "`rvarnumchk'" {
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local rvarlabel : variable label `rvar'
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label define `altnum' `rvarnum' "`rvarlabel'", modify
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}
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}
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}
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qui reshape long `ytemp' `yrank' `alt', i(`case') j(`altnum')
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capture drop _ytemp
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if "`rankdata'" == "no" {
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tempvar ychosen
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gen `ychosen' = `y'
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if "`replace'" == "replace" {
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drop `y'
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local choice "`y'"
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}
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gen `choice' = (`altnum' == `ychosen') if `altnum' < .
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}
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if "`rankdata'" == "yes" {
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label define `altnum', modify
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label values `altnum' `altnum'
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_pecats `altnum'
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local catval = r(catvals)
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local catnms = r(catnms)
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local numcats = r(numcats)
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forvalues i = 1(1)`numcats' {
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local y`i' : word `i' of `catval'
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local ynm`i' : word `i' of `catnms'
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confirm integer number `y`i''
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}
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* if generate option specified, rename stub to that
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if "`generate'" != "" {
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rename `yrank' `generate'
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local yrank "`generate'"
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}
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}
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forvalues i = 1(1)`numcats' {
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local name = "y`y`i''"
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if "`names'" != "nonames" & "`ynm`i''" != "`y`i''" {
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local name "`ynm`i''"
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}
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qui gen ytemp`y`i'' = (`altnum' == `y`i'') if `altnum' < .
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label variable ytemp`y`i'' "`ynm`i''"
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if "`casevars'" != "" {
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foreach var of varlist `casevars' {
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qui gen `name'X`var' = `var' * ytemp`y`i''
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}
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}
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local ylist "`ylist' `name'*"
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clonevar `name' = ytemp`y`i''
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drop ytemp`y`i''
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}
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* output
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if "`rankdata'" == "no" {
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di _n as txt "choice indicated by:" as res " `choice'"
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}
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if "`rankdata'" == "yes" {
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di _n as txt "ranks indicated by:" as res " `yrank'"
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}
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di as txt "case identifier:" as res " `case'"
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di as txt "case-specific interactions:" as res "`ylist'"
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if "`alt'" != "" {
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di as txt "alternative-specific variables:" as res " `alt'"
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}
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end
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@ -1,121 +0,0 @@
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{smcl}
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{* 05Dec2005jf}
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{hline}
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help for {hi:case2alt}{right:03Nov2005}
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{hline}
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||||
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{p 4 4 2}
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{title:Convert data from one observation per case to one observation per alternative per case}
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{p 4 12 2}{cmd:case2alt} {cmd:,}
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{{cmdab:choice(}{it:varname}{cmd:)} | {cmdab:rank(}{it:stubname}{cmd:)}}
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[{cmdab:a:lt(}{it:stubnames}{cmd:)}
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{cmdab:casev:ars(}{it:varlist}{cmd:)}
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{cmdab:case(}{it:varname}{cmd:)}
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{cmdab:g:enerate(}{it:newvar}{cmd:)}
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||||
{cmdab:rep:lace}
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||||
{cmdab:altnum(}{it:varname}{cmd:)}
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{cmdab:non:ames}]
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{title:Description}
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||||
{p 4 4 2}
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{cmd:case2alt} is intended to be used to configure data for the estimation of estimation
|
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of regression models for alternative-specific data, such as {helpb clogit},
|
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{helpb rologit} or {helpb asmprobit}. {cmd:case2alt} presumes that you have data
|
||||
where each observation corresponds to an individual case and that you want to convert
|
||||
the data to the form in which each observation corresponds to an alternative for a specific case.
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||||
|
||||
{p 4 4 2}
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||||
Imagine that you have data with an outcome that has four alternatives, with values 1, 2, 3 and 8.
|
||||
{cmd:case2alt} will reshape the data so that there are n*4 observations. If you specify an
|
||||
identifying variable with the {cmd:casevars()} option, this variable will continue to identify
|
||||
unique cases; otherwise, new variable _id will identify cases.
|
||||
|
||||
{p 4 4 2}
|
||||
A new variable, called either _altnum or the name specified in {cmd:altnum()},
|
||||
will identify the alternatives within a case. Additionally, however, {cmd:case2alt}
|
||||
will create dummy variables y{it:value} that also identify alternatives.
|
||||
In our example the new dummy variables y1, y2, y3 and y8 will be created.
|
||||
Interactions will also be created with these dummies and any variables
|
||||
specified in {cmd:case()}. For the variable educ, {cmd:case2alt} will create
|
||||
variables y1_educ, y2_educ, y3_educ, and y8_educ.
|
||||
|
||||
{p 4 4 2}
|
||||
If we have simple choice variable, then {cmd:case2alt} will create an outcome
|
||||
variable based on {cmd:y()} that contains 1 in the observation corresponding to
|
||||
the selected alternative and 0 for other alternatives. We can specify the name of
|
||||
this new outcome variable using the {cmd:gen()} option, or we can have it be the
|
||||
same as {opt y()} using the {cmd:replace} option, or (by default) we
|
||||
can have it be named choice.
|
||||
|
||||
{p 4 4 2}
|
||||
After using {cmd:case2alt}, we would be able to estimate models like {helpb clogit} by typing, e.g.,:
|
||||
|
||||
{p 4 4 2}
|
||||
{cmd:. clogit choice y1* y2* y3*, group(_id)}
|
||||
|
||||
{p 4 4 2}
|
||||
Alternative-specific variables are specified using the {cmd:alt()} option.
|
||||
The contents of {cmd:alt()} should be {it:stubnames}, corresponding to a series
|
||||
of variables that contain the alternative-specific values.
|
||||
Specifying {cmd:alt(time)}, in our example, would imply that there are
|
||||
variables time1, time2, time3 and time8 in our case-level data.
|
||||
|
||||
{p 4 4 2}
|
||||
Case-specific variables are specified using the {cmd:casevars()} option,
|
||||
where the contents should be a {varlist}. Neither the outcome nor id variable
|
||||
should be included in {cmd:casevars()}.
|
||||
|
||||
{p 4 4 2}
|
||||
If we have ranked data, we can specify the ranked outcome with the
|
||||
{cmd:rank()} outcome. The content of rank should again be a stubname.
|
||||
Specifying {cmd:rank(}rank{cmd:)} in our example would assume there are
|
||||
variables rank1, rank2, rank3, rank8 that contain the relevant information
|
||||
on the ranks of each alternative.
|
||||
|
||||
{title:Options}
|
||||
|
||||
{p 4 8 2}{opth choice(varname)} or {opth rank(stubname)} is required. {varname} is the
|
||||
variable that indicates the value of the selected alternative.
|
||||
In the case of ranked outcome, {it:stubname} with {opt rank()} will contain
|
||||
the stub for the names of variables that contain information about ranks of alternatives.
|
||||
|
||||
{p 4 8 2}{opth case(varname)} indicates the variable, either existing or to be
|
||||
created, that identifies individual cases.
|
||||
If {varname} is unspecified, a new variable named _id will be created.
|
||||
|
||||
{p 4 8 2}{cmd:alt(}{it:stubnames}{cmd:)} contains the {it:stubnames} for
|
||||
alternative-specific variables. This requires that variables {it:stubname}# exist
|
||||
for each value of an alternative.
|
||||
|
||||
{p 4 8 2}{opth casevars(varlist)} contains the names of the case-specific variables
|
||||
(not including the id or outcome variable).
|
||||
|
||||
{p 4 8 2}{opth gen:erate(newvar)} and {opt replace} are used to name the variable that
|
||||
contain 1 for the selected alternative and 0 for non-selected alternatives.
|
||||
The variable will be named {newvar} if {newvar} is specified; the name of
|
||||
the variable specified in {cmd: y()} if {opt replace} is specified; and will be named {it:choice} otherwise.
|
||||
In the case of ranked data, the ranks will be contained in variable specified as the
|
||||
stub in {opt yrank()} and {opt gen:erate()} or {opt replace} will be ignored.
|
||||
|
||||
{p 4 8 2}{opth altnum(varname)} indicates the name of the new variable used to indicate
|
||||
the alternatives. _altnum will be used if altnum() is not specified.
|
||||
|
||||
{p 4 8 2}{opt non:ames} indicates that the case-specific interactions should be named
|
||||
y# instead of using the value labels of the outcome variable.
|
||||
|
||||
{title:Example}
|
||||
|
||||
{p 4 4 2}{cmd:. use "http://www.stata-press.com/data/lfr/nomocc2.dta", clear}{break}
|
||||
{cmd:. mlogit occ white ed exper}{break}
|
||||
{cmd:. case2alt, choice(ed) casevars(white ed exper) replace nonames}{break}
|
||||
{cmd:. clogit occ y1* y2* y3* y4*, group(_id)}{break}
|
||||
|
||||
{p 4 4 2}{cmd:. case2alt, rank(rank92) casevars(hntest) alt(rank04) case(id)}
|
||||
|
||||
{title:Authors}
|
||||
|
||||
Jeremy Freese and J. Scott Long
|
||||
www.indiana.edu/~jslsoc/spost.htm
|
||||
spostsup@indiana.edu
|
||||
@ -1,679 +0,0 @@
|
||||
*! Confirmatory factor analysis with a single factor: v.2.2
|
||||
*! Stas Kolenikov, skolenik-gmail-com
|
||||
|
||||
program define cfa1, eclass
|
||||
version 9.1
|
||||
|
||||
if replay() {
|
||||
if ("`e(cmd)'" != "cfa1") error 301
|
||||
Replay `0'
|
||||
}
|
||||
|
||||
else Estimate `0'
|
||||
end
|
||||
|
||||
program Estimate `0', eclass
|
||||
|
||||
syntax varlist(numeric min=3) [if] [in] [aw pw / ] ///
|
||||
, [unitvar FREE POSvar FROM(str) CONSTRaint(numlist) LEVel(int $S_level) ///
|
||||
ROBust VCE(string) CLUster(passthru) SVY SEArch(passthru) * ]
|
||||
* syntax: cfa1 <list of the effect indicators>
|
||||
* untivar is for the identification condition of the unit variance of the latent variable
|
||||
|
||||
unab varlist : `varlist'
|
||||
tokenize `varlist'
|
||||
local q: word count `varlist'
|
||||
marksample touse
|
||||
preserve
|
||||
qui keep if `touse'
|
||||
* weights!
|
||||
global CFA1N = _N
|
||||
|
||||
/*
|
||||
* we'll estimate the means instead
|
||||
qui foreach x of varlist `varlist' {
|
||||
sum `x', meanonly
|
||||
replace `x' = `x'-r(mean)
|
||||
* deviations from the mean
|
||||
}
|
||||
*/
|
||||
|
||||
if "`weight'" != "" {
|
||||
local mywgt [`weight'=`exp']
|
||||
}
|
||||
|
||||
if "`robust'`cluster'`svy'`weight'"~="" {
|
||||
local needed 1
|
||||
}
|
||||
else {
|
||||
local needed 0
|
||||
}
|
||||
|
||||
Parse `varlist' , `unitvar'
|
||||
local toml `r(toml)'
|
||||
if "`from'" == "" {
|
||||
local from `r(tostart)', copy
|
||||
}
|
||||
|
||||
* identification
|
||||
constraint free
|
||||
global CFA1constr `r(free)'
|
||||
if "`unitvar'" ~= "" {
|
||||
* identification by unit variance
|
||||
constraint $CFA1constr [phi]_cons = 1
|
||||
}
|
||||
else if "`free'"=="" {
|
||||
* identification by the first variable
|
||||
constraint $CFA1constr [`1']_cons = 1
|
||||
}
|
||||
else {
|
||||
* identification imposed by user
|
||||
global CFA1constr
|
||||
}
|
||||
local nconstr : word count `constraint'
|
||||
|
||||
global CFA1PV = ("`posvar'" != "")
|
||||
|
||||
if "`posvar'" ~= "" {
|
||||
di as text _n "Fitting the model without restrictions on error variances..."
|
||||
}
|
||||
|
||||
* variance estimation
|
||||
local vce = trim("`vce'")
|
||||
if "`vce'" == "boot" local vce bootstrap
|
||||
if "`vce'" == "sbentler" {
|
||||
global CFA1SBV = 1
|
||||
local vce
|
||||
}
|
||||
else {
|
||||
if index("robustoimopg","`vce'") {
|
||||
local vce vce(`vce')
|
||||
}
|
||||
else {
|
||||
di as err "`vce' estimation is not supported"
|
||||
if "`vce'" == "bootstrap" | "`vce'" == "boot" {
|
||||
di as err "try {help bootstrap} command directly"
|
||||
}
|
||||
exit 198
|
||||
}
|
||||
}
|
||||
|
||||
tempname ilog1 ilog2
|
||||
Tryit ml model lf cfa1_lf `toml' `mywgt', constraint($CFA1constr `constraint') ///
|
||||
init (`from') maximize nooutput `options' `search' ///
|
||||
`svy' `cluster' `robust' `vce'
|
||||
/*
|
||||
ml model lf cfa1_lf `toml' `mywgt', ///
|
||||
constraint($CFA1constr `constraint') `svy' `robust' `cluster' ///
|
||||
maximize
|
||||
* ml check
|
||||
* ml search, rep(5)
|
||||
ml init `from', copy
|
||||
cap noi ml maximize , `options'
|
||||
*/
|
||||
mat `ilog1' = e(ilog)
|
||||
|
||||
local nz = 0
|
||||
if $CFA1PV {
|
||||
* determine if refitting the model is needed
|
||||
tempname ll_unr
|
||||
scalar `ll_unr' = e(ll)
|
||||
forvalues i=1/`q' {
|
||||
if [``i''_v]_cons <0 {
|
||||
constraint free
|
||||
local ccc = `r(free)'
|
||||
const define `ccc' [``i''_v]_cons = 0
|
||||
local zerolist `zerolist' `ccc'
|
||||
local ++nz
|
||||
}
|
||||
}
|
||||
local zerolist = trim("`zerolist'")
|
||||
global CFA1constr $CFA1constr `zerolist'
|
||||
if "`zerolist'" ~= "" {
|
||||
di as text _n "Fitting the model with some error variances set to zero..."
|
||||
Tryit ml model lf cfa1_lf `toml' `mywgt' , constraint($CFA1constr `constraint') ///
|
||||
init (`from') maximize nooutput `options' `search' ///
|
||||
`svy' `robust' `cluster' `vce'
|
||||
mat `ilog2' = e(ilog)
|
||||
|
||||
}
|
||||
|
||||
* adjust degrees of freedom!
|
||||
}
|
||||
|
||||
|
||||
* we better have this before Satorra-Bentler
|
||||
if "`unitvar'" ~= "" {
|
||||
ereturn local normalized Latent Variance
|
||||
}
|
||||
else if "`free'"=="" {
|
||||
ereturn local normalized `1'
|
||||
}
|
||||
|
||||
|
||||
* work out Satorra-Bentler estimates
|
||||
if "$CFA1SBV"!="" {
|
||||
* repost Satorra-Bentler covariance matrix
|
||||
tempname SBVar SBV Delta Gamma
|
||||
cap SatorraBentler
|
||||
if _rc {
|
||||
di as err "Satorra-Bentler standard errors are not supported for this circumstance; revert to vce(oim)"
|
||||
global CFA1SBV
|
||||
}
|
||||
else {
|
||||
mat `SBVar' = r(SBVar)
|
||||
mat `Delta' = r(Delta)
|
||||
mat `Gamma' = r(Gamma)
|
||||
mat `SBV' = r(SBV)
|
||||
ereturn repost V = `SBVar'
|
||||
ereturn matrix SBGamma = `Gamma', copy
|
||||
ereturn matrix SBDelta = `Delta', copy
|
||||
ereturn matrix SBV = `SBV', copy
|
||||
ereturn local vce SatorraBentler
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
* get the covariance matrix and the number of observations!
|
||||
***********************************************************
|
||||
tempname lambda vars phi S Sindep Sigma trind eb
|
||||
|
||||
qui mat accum `S' = `varlist', dev nocons
|
||||
mat `S' = `S' / $CFA1N
|
||||
|
||||
* implied matrix
|
||||
mat `eb' = e(b)
|
||||
mat `lambda' = `eb'[1,1..`q']
|
||||
mat `vars' = `eb'[1,`q'+1..2*`q']
|
||||
scalar `phi' = `eb'[1,3*`q'+1]
|
||||
mat `Sigma' = `lambda''*`phi'*`lambda' + diag(`vars')
|
||||
mat `Sindep' = diag(vecdiag(`S'))
|
||||
|
||||
* test against independence
|
||||
mat `trind' = trace( syminv(`Sindep') * `S' )
|
||||
local trind = `trind'[1,1]
|
||||
ereturn scalar ll_indep = -0.5 * `q' * $CFA1N * ln(2*_pi) - 0.5 * $CFA1N * ln(det(`Sindep')) - 0.5 * $CFA1N * `trind'
|
||||
ereturn scalar lr_indep = 2*(e(ll)-e(ll_indep))
|
||||
ereturn scalar df_indep = `q'-`nz'-`nconstr'
|
||||
ereturn scalar p_indep = chi2tail(e(df_indep),e(lr_indep))
|
||||
|
||||
* goodness of fit test
|
||||
ereturn scalar ll_u = -0.5 * `q' * $CFA1N * ln(2*_pi) - 0.5 * $CFA1N * ln(det(`S')) - 0.5 * `q' * $CFA1N
|
||||
ereturn scalar lr_u = -2*(e(ll)-e(ll_u))
|
||||
ereturn scalar df_u = `q'*(`q'+1)*.5 - (2*`q' - `nz' - `nconstr')
|
||||
* wrong if there are any extra constraints in -constraint- command!!!
|
||||
ereturn scalar p_u = chi2tail(e(df_u),e(lr_u))
|
||||
ereturn matrix ilog1 `ilog1'
|
||||
cap ereturn matrix ilog2 `ilog2'
|
||||
|
||||
* Satorra-Bentler corrections
|
||||
if "$CFA1SBV"!="" {
|
||||
* compute the corrected tests, too
|
||||
* Satorra-Bentler 1994
|
||||
tempname U trUG2 Tdf
|
||||
mat `U' = `SBV' - `SBV'*`Delta'*syminv(`Delta''*`SBV'*`Delta')*`Delta''*`SBV'
|
||||
ereturn matrix SBU = `U'
|
||||
mat `U' = trace( e(SBU)*`Gamma' )
|
||||
ereturn scalar SBc = `U'[1,1]/e(df_u)
|
||||
ereturn scalar Tscaled = e(lr_u)/e(SBc)
|
||||
ereturn scalar p_Tscaled = chi2tail( e(df_u), e(Tscaled) )
|
||||
|
||||
mat `trUG2' = trace( e(SBU)*`Gamma'*e(SBU)*`Gamma')
|
||||
ereturn scalar SBd = `U'[1,1]*`U'[1,1]/`trUG2'[1,1]
|
||||
ereturn scalar Tadj = ( e(SBd)/`U'[1,1]) * e(lr_u)
|
||||
ereturn scalar p_Tadj = chi2tail( e(SBd), e(Tadj) )
|
||||
|
||||
* Yuan-Bentler 1997
|
||||
* weights!
|
||||
ereturn scalar T2 = e(lr_u)/(1+e(lr_u)/e(N) )
|
||||
ereturn scalar p_T2 = chi2tail( e(df_u), e(T2) )
|
||||
}
|
||||
|
||||
if "`posvar'" ~= "" {
|
||||
ereturn scalar lr_zerov = 2*(`ll_unr' - e(ll))
|
||||
ereturn scalar df_zerov = `nz'
|
||||
local replay_opt posvar llu(`ll_unr')
|
||||
}
|
||||
ereturn local cmd cfa1
|
||||
|
||||
Replay , `replay_opt' level(`level')
|
||||
Finish
|
||||
restore
|
||||
ereturn repost, esample(`touse')
|
||||
|
||||
end
|
||||
|
||||
program define Tryit
|
||||
|
||||
cap noi `0'
|
||||
local rc=_rc
|
||||
if `rc' {
|
||||
Finish
|
||||
exit `rc'
|
||||
}
|
||||
|
||||
end
|
||||
|
||||
program define Finish
|
||||
|
||||
* finishing off
|
||||
constraint drop $CFA1constr
|
||||
global CFA1S
|
||||
global CFA1N
|
||||
global CFA1PV
|
||||
global CFA1theta
|
||||
global CFA1arg
|
||||
global CFA1data
|
||||
global CFA1constr
|
||||
global CFA1vars
|
||||
global CFA1SBV
|
||||
|
||||
end
|
||||
|
||||
program define Replay
|
||||
|
||||
syntax, [posvar llu(str) level(passthru)]
|
||||
|
||||
di _n as text "Log likelihood = " as res e(ll) _col(59) as text "Number of obs = " as res e(N)
|
||||
di as text "{hline 13}{c TT}{hline 64}"
|
||||
di as text " {c |} Coef. Std. Err. z P>|z| [$S_level% Conf. Interval]"
|
||||
di as text "{hline 13}{c +}{hline 64}"
|
||||
|
||||
tempname vce
|
||||
mat `vce' = e(V)
|
||||
local q = colsof(`vce')
|
||||
local q = (`q'-1)/3
|
||||
local a : colfullnames(`vce')
|
||||
tokenize `a'
|
||||
|
||||
di as text "Lambda{col 14}{c |}"
|
||||
forvalues i = 1/`q' {
|
||||
gettoken v`i' : `i' , parse(":")
|
||||
_diparm `v`i'' , label("`v`i''") prob `level'
|
||||
}
|
||||
di as text "Var[error]{col 14}{c |}"
|
||||
forvalues i = 1/`q' {
|
||||
_diparm `v`i''_v , label("`v`i''") prob `level'
|
||||
}
|
||||
di as text "Means{col 14}{c |}"
|
||||
forvalues i = 1/`q' {
|
||||
_diparm `v`i''_m , label("`v`i''") prob `level'
|
||||
}
|
||||
di as text "Var[latent]{col 14}{c |}"
|
||||
_diparm phi , label("phi1") prob
|
||||
|
||||
di as text "{hline 13}{c +}{hline 64}"
|
||||
di as text "R2{col 14}{c |}"
|
||||
forvalues i = 1/`q' {
|
||||
di as text %12s "`v`i''" "{col 14}{c |}{col 20}" ///
|
||||
as res %6.4f (_b[`v`i'':_cons]^2*_b[phi:_cons]) / ///
|
||||
(_b[`v`i'':_cons]^2*_b[phi:_cons] + _b[`v`i''_v:_cons])
|
||||
}
|
||||
|
||||
|
||||
di as text "{hline 13}{c BT}{hline 64}"
|
||||
|
||||
if e(df_u)>0 {
|
||||
di as text _n "Goodness of fit test: LR = " as res %6.3f e(lr_u) ///
|
||||
as text _col(40) "; Prob[chi2(" as res %2.0f e(df_u) as text ") > LR] = " as res %6.4f e(p_u)
|
||||
}
|
||||
else {
|
||||
di as text "No degrees of freedom to perform the goodness of fit test"
|
||||
}
|
||||
di as text "Test vs independence: LR = " as res %6.3f e(lr_indep) ///
|
||||
as text _col(40) "; Prob[chi2(" as res %2.0f e(df_indep) as text ") > LR] = " as res %6.4f e(p_indep)
|
||||
|
||||
if "`e(vce)'" == "SatorraBentler" & e(df_u)>0 {
|
||||
* need to report all those corrected statistics
|
||||
|
||||
di as text _n "Satorra-Bentler Tbar" _col(26) "= " as res %6.3f e(Tscaled) ///
|
||||
as text _col(40) "; Prob[chi2(" as res %2.0f e(df_u) as text ") > Tbar] = " as res %6.4f e(p_Tscaled)
|
||||
|
||||
di as text "Satorra-Bentler Tbarbar" _col(26) "= " as res %6.3f e(Tadj) ///
|
||||
as text _col(40) "; Prob[chi2(" as res %4.1f e(SBd) as text ") > Tbarbar] = " as res %6.4f e(p_Tadj)
|
||||
|
||||
di as text "Yuan-Bentler T2" _col(26) "= " as res %6.3f e(T2) ///
|
||||
as text _col(40) "; Prob[chi2(" as res %2.0f e(df_u) as text ") > T2] = " as res %6.4f e(p_T2)
|
||||
}
|
||||
|
||||
if "`posvar'" ~= "" {
|
||||
* just estimated?
|
||||
if "`llu'" == "" {
|
||||
di as err "cannot specify -posvar- option, need to refit the whole model"
|
||||
}
|
||||
else {
|
||||
if e(df_zerov)>0 {
|
||||
di as text "Likelihood ratio against negative variances: LR = " as res %6.3f e(lr_zerov)
|
||||
di as text "Conservative Prob[chi2(" as res %2.0f e(df_zerov) as text ") > LR] = " ///
|
||||
as res %6.4f chi2tail(e(df_zerov),e(lr_zerov))
|
||||
}
|
||||
else {
|
||||
di as text "All variances are non-negative, no need to test against zero variances"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
end
|
||||
|
||||
program define Parse , rclass
|
||||
* takes the list of variables and returns the appropriate ml model statement
|
||||
syntax varlist , [unitvar]
|
||||
|
||||
global CFA1arg
|
||||
global CFA1theta
|
||||
global CFA1vars
|
||||
local q : word count `varlist'
|
||||
|
||||
* lambdas
|
||||
forvalues i = 1/`q' {
|
||||
local toml `toml' (``i'': ``i'' = )
|
||||
local tostart `tostart' 1
|
||||
global CFA1arg $CFA1arg g_``i''
|
||||
global CFA1theta $CFA1theta l_`i'
|
||||
global CFA1vars $CFA1vars ``i''
|
||||
}
|
||||
|
||||
* variances
|
||||
forvalues i = 1/`q' {
|
||||
local toml `toml' (``i''_v: )
|
||||
local tostart `tostart' 1
|
||||
global CFA1arg $CFA1arg g_``i''_v
|
||||
global CFA1theta $CFA1theta v_`i'
|
||||
}
|
||||
|
||||
* means
|
||||
forvalues i = 1/`q' {
|
||||
local toml `toml' (``i''_m: )
|
||||
qui sum ``i'', mean
|
||||
local mean = r(mean)
|
||||
local tostart `tostart' `mean'
|
||||
global CFA1arg $CFA1arg g_``i''_m
|
||||
global CFA1theta $CFA1theta m_`i'
|
||||
}
|
||||
|
||||
* variance of the factor
|
||||
local toml `toml' (phi: )
|
||||
local tostart `tostart' 1
|
||||
global CFA1arg $CFA1arg g_Phi
|
||||
global CFA1theta $CFA1theta phi
|
||||
|
||||
* done!
|
||||
return local toml `toml'
|
||||
return local tostart `tostart'
|
||||
|
||||
end
|
||||
|
||||
|
||||
**************************** Satorra-Bentler covariance matrix code
|
||||
|
||||
program SatorraBentler, rclass
|
||||
version 9.1
|
||||
syntax [, noisily]
|
||||
* assume the maximization completed, the results are in memory as -ereturn data-
|
||||
* we shall just return the resulting matrix
|
||||
|
||||
if "`e(normalized)'" == "" {
|
||||
di as err "cannot compute Satorra-Bentler variance estimator with arbitrary identification... yet"
|
||||
exit 198
|
||||
}
|
||||
|
||||
* assume sample is restricted to e(sample)
|
||||
* preserve
|
||||
* keep if e(sample)
|
||||
|
||||
* get the variable names
|
||||
tempname VV bb
|
||||
mat `VV' = e(V)
|
||||
local q = rowsof(`VV')
|
||||
local p = (`q'-1)/3
|
||||
local eqlist : coleq `VV'
|
||||
tokenize `eqlist'
|
||||
forvalues k=1/`p' {
|
||||
local varlist `varlist' ``k''
|
||||
}
|
||||
|
||||
* compute the implied covariance matrix
|
||||
tempname Lambda Theta phi Sigma
|
||||
mat `bb' = e(b)
|
||||
mat `Lambda' = `bb'[1,1..`p']
|
||||
mat `Theta' = `bb'[1,`p'+1..2*`p']
|
||||
scalar `phi' = `bb'[1,`q']
|
||||
mat `Sigma' = `Lambda''*`phi'*`Lambda' + diag(`Theta')
|
||||
|
||||
* compute the empirical cov matrix
|
||||
tempname SampleCov
|
||||
qui mat accum `SampleCov' = `varlist' , nocons dev
|
||||
* weights!!!
|
||||
mat `SampleCov' = `SampleCov' / (r(N)-1)
|
||||
|
||||
* compute the matrix Gamma
|
||||
`noisily' di as text "Computing the Gamma matrix of fourth moments..."
|
||||
tempname Gamma
|
||||
SBGamma `varlist'
|
||||
mat `Gamma' = r(Gamma)
|
||||
return add
|
||||
|
||||
* compute the duplication matrix
|
||||
* Dupl `p'
|
||||
* let's call it from within SBV!
|
||||
|
||||
* compute the V matrix
|
||||
`noisily' di as text "Computing the V matrix..."
|
||||
SBV `SampleCov' `noisily'
|
||||
tempname V
|
||||
mat `V' = r(SBV)
|
||||
return add
|
||||
|
||||
* compute the Delta matrix
|
||||
`noisily' di as text "Computing the Delta matrix..."
|
||||
tempname Delta
|
||||
mata : SBDelta("`bb'","`Delta'")
|
||||
|
||||
*** put the pieces together now
|
||||
|
||||
tempname DeltaId
|
||||
|
||||
* enact the constraints!
|
||||
SBconstr `bb'
|
||||
mat `DeltaId' = `Delta' * diag( r(Fixed) )
|
||||
|
||||
* those should be in there, but it never hurts to fix!
|
||||
if "`e(normalized)'" == "Latent Variance" {
|
||||
* make the last column null
|
||||
mat `DeltaId' = ( `DeltaId'[1...,1...3*`p'] , J(rowsof(`Delta'), 1, 0) )
|
||||
}
|
||||
else if "`e(normalized)'" ~= "" {
|
||||
* normalization by first variable
|
||||
local idvar `e(normalized)'
|
||||
if "`idvar'" ~= "`1'" {
|
||||
di as err "cannot figure out the identification variable"
|
||||
exit 198
|
||||
}
|
||||
mat `DeltaId' = ( J(rowsof(`Delta'), 1, 0) , `DeltaId'[1...,2...] )
|
||||
}
|
||||
local dcnames : colfullnames `bb'
|
||||
local drnames : rownames `Gamma'
|
||||
mat colnames `DeltaId' = `dcnames'
|
||||
mat rownames `DeltaId' = `drnames'
|
||||
return matrix Delta = `DeltaId', copy
|
||||
|
||||
tempname VVV
|
||||
mat `VVV' = ( `DeltaId'' * `V' * `DeltaId' )
|
||||
mat `VVV' = syminv(`VVV')
|
||||
mat `VVV' = `VVV' * ( `DeltaId'' * `V' * `Gamma' * `V' * `DeltaId' ) * `VVV'
|
||||
|
||||
* add the covariance matrix for the means, which is just Sigma/_N
|
||||
* weights!
|
||||
tempname CovM
|
||||
mat `CovM' = ( J(2*`p',colsof(`bb'),0) \ J(`p',2*`p',0) , `Sigma', J(`p',1,0) \ J(1, colsof(`bb'), 0) )
|
||||
|
||||
mat `VVV' = (`VVV' + `CovM')/_N
|
||||
return matrix SBVar = `VVV'
|
||||
|
||||
end
|
||||
* of satorrabentler
|
||||
|
||||
program define SBGamma, rclass
|
||||
syntax varlist
|
||||
unab varlist : `varlist'
|
||||
tokenize `varlist'
|
||||
|
||||
local p: word count `varlist'
|
||||
|
||||
forvalues k=1/`p' {
|
||||
* make up the deviations
|
||||
* weights!!!
|
||||
qui sum ``k'', meanonly
|
||||
tempvar d`k'
|
||||
qui g double `d`k'' = ``k'' - r(mean)
|
||||
local dlist `dlist' `d`k''
|
||||
}
|
||||
|
||||
local pstar = `p'*(`p'+1)/2
|
||||
forvalues k=1/`pstar' {
|
||||
tempvar b`k'
|
||||
qui g double `b`k'' = .
|
||||
local blist `blist' `b`k''
|
||||
}
|
||||
|
||||
|
||||
* convert into vech (z_i-bar z)(z_i-bar z)'
|
||||
mata : SBvechZZtoB("`dlist'","`blist'")
|
||||
|
||||
* blist now should contain the moments around the sample means
|
||||
* we need to get their covariance matrix
|
||||
|
||||
tempname Gamma
|
||||
qui mat accum `Gamma' = `blist', dev nocons
|
||||
* weights!
|
||||
mat `Gamma' = `Gamma'/(_N-1)
|
||||
mata : Gamma = st_matrix( "`Gamma'" )
|
||||
|
||||
* make nice row and column names
|
||||
forvalues i=1/`p' {
|
||||
forvalues j=`i'/`p' {
|
||||
local namelist `namelist' ``i''_X_``j''
|
||||
}
|
||||
}
|
||||
mat colnames `Gamma' = `namelist'
|
||||
mat rownames `Gamma' = `namelist'
|
||||
|
||||
return matrix Gamma = `Gamma'
|
||||
|
||||
end
|
||||
* of computing Gamma
|
||||
|
||||
program define SBV, rclass
|
||||
args A noisily
|
||||
tempname D Ainv V
|
||||
local p = rowsof(`A')
|
||||
`noisily' di as text "Computing the duplication matrix..."
|
||||
mata : Dupl(`p',"`D'")
|
||||
mat `Ainv' = syminv(`A')
|
||||
mat `V' = .5*`D''* (`Ainv' # `Ainv') * `D'
|
||||
return matrix SBV = `V'
|
||||
end
|
||||
* of computing V
|
||||
|
||||
* need to figure out whether a constraint has the form parameter = value,
|
||||
* and to nullify the corresponding column
|
||||
program define SBconstr, rclass
|
||||
args bb
|
||||
tempname Iq
|
||||
mat `Iq' = J(1,colsof(`bb'),1)
|
||||
tokenize $CFA1constr
|
||||
while "`1'" ~= "" {
|
||||
constraint get `1'
|
||||
local constr `r(contents)'
|
||||
gettoken param value : constr, parse("=")
|
||||
* is the RHS indeed a number?
|
||||
local value = substr("`value'",2,.)
|
||||
confirm number `value'
|
||||
* parse the square brackets and turn them into colon
|
||||
* replace the opening brackets with nothing, and closing brackets, with :
|
||||
local param = subinstr("`param'","["," ",1)
|
||||
local param = subinstr("`param'","]",":",1)
|
||||
local param = trim("`param'")
|
||||
local coln = colnumb(`bb',"`param'" )
|
||||
mat `Iq'[1,`coln']=0
|
||||
|
||||
mac shift
|
||||
}
|
||||
return matrix Fixed = `Iq'
|
||||
end
|
||||
|
||||
|
||||
cap mata : mata drop SBvechZZtoB()
|
||||
cap mata : mata drop Dupl()
|
||||
cap mata : mata drop SBDelta()
|
||||
|
||||
mata:
|
||||
void SBvechZZtoB(string dlist, string blist) {
|
||||
// view the deviation variables
|
||||
st_view(data=.,.,tokens(dlist))
|
||||
// view the moment variables
|
||||
// blist=st_local("blist")
|
||||
st_view(moments=.,.,tokens(blist))
|
||||
// vectorize!
|
||||
for(i=1; i<=rows(data); i++) {
|
||||
B = data[i,.]'*data[i,.]
|
||||
moments[i,.] = vech(B)'
|
||||
}
|
||||
}
|
||||
|
||||
void Dupl(scalar p, string Dname) {
|
||||
pstar = p*(p+1)/2
|
||||
Ipstar = I(pstar)
|
||||
D = J(p*p,0,.)
|
||||
for(k=1;k<=pstar;k++) {
|
||||
D = (D, vec(invvech(Ipstar[.,k])))
|
||||
}
|
||||
st_matrix(Dname,D)
|
||||
}
|
||||
|
||||
void SBDelta(string bbname, string DeltaName) {
|
||||
bb = st_matrix(bbname)
|
||||
p = (cols(bb)-1)/3
|
||||
Lambda = bb[1,1..p]
|
||||
Theta = bb[1,p+1..2*p]
|
||||
phi = bb[1,cols(bb)]
|
||||
Delta = J(0,cols(bb),.)
|
||||
for(i=1;i<=p;i++) {
|
||||
for(j=i;j<=p;j++) {
|
||||
DeltaRow = J(1,cols(Delta),0)
|
||||
for(k=1;k<=p;k++) {
|
||||
// derivative wrt lambda_k
|
||||
DeltaRow[k] = (k==i)*Lambda[j]*phi + (j==k)*Lambda[i]*phi
|
||||
// derivative wrt sigma^2_k
|
||||
DeltaRow[p+k] = (i==k)*(j==k)
|
||||
}
|
||||
DeltaRow[cols(Delta)] = Lambda[i]*Lambda[j]
|
||||
Delta = Delta \ DeltaRow
|
||||
}
|
||||
}
|
||||
st_matrix(DeltaName,Delta)
|
||||
}
|
||||
|
||||
end
|
||||
* of mata piece
|
||||
|
||||
***************************** end of Satorra-Bentler covariance matrix code
|
||||
|
||||
exit
|
||||
|
||||
History:
|
||||
v.1.0 -- May 19, 2004: basic operation, method d0
|
||||
v.1.1 -- May 19, 2004: identification by -constraint-
|
||||
common -cfa1_ll-
|
||||
from()
|
||||
v.1.2 -- May 21, 2004: method -lf-, robust
|
||||
constraint free
|
||||
v.1.3 -- unknown
|
||||
v.1.4 -- Feb 15, 2005: pweights, arbitrary constraints
|
||||
v.2.0 -- Feb 28, 2006: update to version 9 using Mata
|
||||
v.2.1 -- Apr 11, 2006: whatever
|
||||
v.2.2 -- Apr 13, 2006: Satorra-Bentler standard errors and test corrections
|
||||
-vce- option
|
||||
Apr 14, 2006: degrees of freedom corrected for # constraints
|
||||
July 5, 2006: minor issue with -from(, copy)-
|
||||
@ -1,159 +0,0 @@
|
||||
{smcl}
|
||||
{.-}
|
||||
help for {cmd:cfa1} {right:author: {browse "http://stas.kolenikov.name/":Stas Kolenikov}}
|
||||
{.-}
|
||||
|
||||
{title:Confirmatory factor analysis with a single factor}
|
||||
|
||||
{p 8 27}
|
||||
{cmd:cfa1}
|
||||
{it:varlist}
|
||||
[{cmd:if} {it:exp}] [{cmd:in} {it:range}]
|
||||
[{cmd:aw|pw =} {it:weight}]
|
||||
[{cmd:,}
|
||||
{cmd:unitvar}
|
||||
{cmd:free}
|
||||
{cmdab:pos:var}
|
||||
{cmdab:constr:aint(}{it:numlist}{cmd:)}
|
||||
{cmdab:lev:el(}{it:#}{cmd:)}
|
||||
{cmdab:rob:ust}
|
||||
{cmd:vce(robust|oim|opg|sbentler}{cmd:)}
|
||||
{cmd:cluster(}{it:varname}{cmd:)}
|
||||
{cmd:svy}
|
||||
{cmdab:sea:rch(}{it:searchspec}{cmd:)}
|
||||
{cmd:from(}{it:initspecs}{cmd:)}
|
||||
{it:ml options}
|
||||
]
|
||||
|
||||
{title:Description}
|
||||
|
||||
{p}{cmd:cfa1} estimates simple confirmatory factor
|
||||
analysis model with a single factor. In this model,
|
||||
each of the variables is assumed to be an indicator
|
||||
of an underlying unobserved factor with a linear
|
||||
dependence between them:
|
||||
|
||||
{center:{it:y_i = m_i + l_i xi + delta_i}}
|
||||
|
||||
{p}where {it:y_i} is the {it:i}-th variable
|
||||
in the {it:varlist}, {it:m_i} is its mean,
|
||||
{it:l_i} is the latent variable loading,
|
||||
{it:xi} is the latent variable/factor,
|
||||
and {it:delta_i} is the measurement error.
|
||||
|
||||
{p}The model is estimated by the maximum likelihood
|
||||
procedure.
|
||||
|
||||
{p}As with all latent variable models, a number
|
||||
of identifying assumptions need to be made about
|
||||
the latent variable {it:xi}. It is assumed
|
||||
to have mean zero, and its scale is determined
|
||||
by the first variable in the {it:varlist}
|
||||
(i.e., l_1 is set to equal 1). Alternatively,
|
||||
identification can be achieved by setting the
|
||||
variance of the latent variable to 1 (with option
|
||||
{it:unitvar}). More sophisticated identification
|
||||
conditions can be achieved by specifying option
|
||||
{it:free} and then providing the necessary
|
||||
{it:constraint}.
|
||||
|
||||
|
||||
{title:Options}
|
||||
|
||||
{ul:Identification:}
|
||||
|
||||
{p 0 4}{cmd:unitvar} specifies identification by setting
|
||||
the variance of the latent variable to 1.
|
||||
|
||||
{p 0 4}{cmd:free} requests to relax all identifying constraints.
|
||||
In this case, the user is responsible for provision
|
||||
of such constraints; otherwise, the estimation process
|
||||
won't converge.
|
||||
|
||||
{p 0 4}{cmdab:pos:var} specifies that if one or more of the
|
||||
measurement error variances were estimated to be
|
||||
negative (known as Heywood cases), the model
|
||||
needs to be automatically re-estimated by setting
|
||||
those variances to zero. The likelihood ratio test
|
||||
is then reported comparing the models with and without
|
||||
constraints. If there is only one offending estimate,
|
||||
the proper distribution to refer this likelihood
|
||||
ratio to is a mixture of chi-squares; see
|
||||
{help j_chibar:chi-bar test}. A conservative
|
||||
test is provided by a reference to the chi-square
|
||||
distribution with the largest degrees of freedom.
|
||||
The p-value is then overstated.
|
||||
|
||||
{p 0 4}{cmdab:constr:aint(}{it:numlist}{cmd:)} can be used
|
||||
to supply additional constraints. The degrees of freedom
|
||||
of the model may be wrong, then.
|
||||
|
||||
{p 0 4}{cmdab:lev:el(}{it:#}{cmd:)} -- see
|
||||
{help estimation_options##level():estimation options}
|
||||
|
||||
{ul:Standard error estimation:}
|
||||
|
||||
{p 0 4}{cmd:vce(oim|opg|robust|sbentler}
|
||||
specifies the way to estimate the standard errors.
|
||||
See {help vce_option}. {cmd:vce(sbentler)} is an
|
||||
additional Satorra-Bentler estimator popular in
|
||||
structural equation modeling literature that relaxes
|
||||
the assumption of multivariate normality while
|
||||
keeping the assumption of proper structural specification.
|
||||
|
||||
{p 0 4}{cmd:robust} is a synonum for {cmd:vce(robust)}.
|
||||
|
||||
{p 0 4}{cmd:cluster(}{it:varname}{cmd:)}
|
||||
|
||||
{p 0 4}{cmd:svy} instructs {cmd:cfa1} to respect the complex
|
||||
survey design, if one is specified.
|
||||
|
||||
{ul:Maximization options: see {help maximize}}
|
||||
|
||||
{title:Returned values}
|
||||
|
||||
{p}Beside the standard {help estcom:estimation results}, {cmd:cfa1}
|
||||
also performs the overall goodness of fit test with results
|
||||
saved in {cmd:e(lr_u)}, {cmd:e(df_u)} and {cmd:e(p_u)}
|
||||
for the test statistic, its goodness of fit, and the resulting
|
||||
p-value. A test vs. the model with the independent data
|
||||
is provided with the {help ereturn} results with {cmd:indep}
|
||||
suffix. Here, under the null hypothesis,
|
||||
the covariance matrix is assumed diagonal.
|
||||
|
||||
{p}When {cmd:sbentler} is specified, Satorra-Bentler
|
||||
standard errors are computed and posted as {cmd:e(V)},
|
||||
with intermediate matrices saved in {cmd:e(SBU)},
|
||||
{cmd:e(SBV)}, {cmd:e(SBGamma)} and {cmd:e(SBDelta)}.
|
||||
Also, a number of corrected overall fit test statistics
|
||||
is reported and saved: T-scaled ({cmd:ereturn} results
|
||||
with {cmd:Tscaled} suffix) and T-adjusted
|
||||
({cmd:ereturn} resuls with {cmd:Tadj} suffix;
|
||||
also, {cmd:e(SBc)} and {cmd:e(SBd)} are the
|
||||
scaling constants, with the latter also
|
||||
being the approximate degrees of freedom
|
||||
of the chi-square test)
|
||||
from Satorra and Bentler (1994), and T-double
|
||||
bar from Yuan and Bentler (1997)
|
||||
(with {cmd:T2} suffix).
|
||||
|
||||
|
||||
{title:References}
|
||||
|
||||
{p 0 4}{bind:}Satorra, A. and Bentler, P. M. (1994)
|
||||
Corrections to test statistics and standard errors in covariance structure analysis,
|
||||
in: {it:Latent variables analysis}, SAGE.
|
||||
|
||||
{p 0 4}{bind:}
|
||||
Yuan, K. H., and Bentler, P. M. (1997)
|
||||
Mean and Covariance Structure Analysis: Theoretical and Practical Improvements.
|
||||
{it:JASA}, {bf:92} (438), pp. 767--774.
|
||||
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{p 0 21}{bind:}Online: help for {help factor}
|
||||
|
||||
{title:Contact}
|
||||
|
||||
Stas Kolenikov, kolenikovs {it:at} missouri.edu
|
||||
@ -1,69 +0,0 @@
|
||||
*! Log likelihood for cfa1: linear form; v.2.1
|
||||
program define cfa1_lf
|
||||
|
||||
args lnf $CFA1theta
|
||||
* $CFA1theta contains all the names needed:
|
||||
* $CFA1theta == l_1 ... l_q v_1 ... v_q m_1 ... m_q phi
|
||||
|
||||
gettoken lnf allthenames : 0
|
||||
|
||||
tempvar lnl
|
||||
qui g double `lnl' = .
|
||||
|
||||
nobreak mata: CFA1_NormalLKHDr( "`allthenames'", "$CFA1vars", "`lnl'")
|
||||
|
||||
qui replace `lnf' = `lnl'
|
||||
|
||||
end
|
||||
|
||||
*! NormalLKHDr: normal likelihood with normal deviates in variables
|
||||
*! v.2.1 Stas Kolenikov skolenik@gmail.com
|
||||
cap mata: mata drop CFA1_NormalLKHDr()
|
||||
mata:
|
||||
void CFA1_NormalLKHDr(
|
||||
string parnames, // the parameter names
|
||||
string varnames, // the variables
|
||||
string loglkhd // where the stuff is to be returned
|
||||
) {
|
||||
|
||||
// declarations
|
||||
real matrix data, lnl, parms // views of the data
|
||||
real matrix lambda, means, vars, phi // parameters
|
||||
real matrix Sigma, WorkSigma, InvWorkSigma, SS // the covariance matrices and temp matrix
|
||||
real scalar p, n // dimension, no. obs
|
||||
|
||||
// get the data in
|
||||
st_view(data=., ., tokens(varnames) )
|
||||
st_view(lnl=., ., tokens(loglkhd) )
|
||||
st_view(parms=., 1, tokens(parnames) )
|
||||
|
||||
n=rows(data)
|
||||
p=cols(data)
|
||||
|
||||
// get the parameters in
|
||||
lambda= parms[1,1..p]
|
||||
vars = parms[1,p+1..2*p]
|
||||
means = parms[1,2*p+1..3*p]
|
||||
phi = parms[1,3*p+1]
|
||||
|
||||
Sigma = lambda'*lambda*phi + diag(vars)
|
||||
|
||||
SS = cholesky(Sigma)
|
||||
InvWorkSigma = solvelower(SS,I(rows(SS)))
|
||||
InvWorkSigma = solveupper(SS',InvWorkSigma)
|
||||
ldetWS = 2*ln(dettriangular(SS))
|
||||
|
||||
for( i=1; i<=n; i++ ) {
|
||||
lnl[i,1] = -.5*(data[i,.]-means)*InvWorkSigma*(data[i,.]-means)' - .5*ldetWS - .5*p*ln(2*pi())
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
end
|
||||
|
||||
|
||||
exit
|
||||
|
||||
History:
|
||||
v.2.0 March 10, 2006 -- re-written for Stata 9 and Mata
|
||||
v.2.1 March 10, 2006 -- everything is moved to Mata
|
||||
@ -1,169 +0,0 @@
|
||||
*!Data management utility: check for existence of variables in a dataset.
|
||||
*!Version 1.1 by Amadou B. DIALLO.
|
||||
*!This version 1.2 . Updated by Amadou B. DIALLO and Jean-Benoit HARDOUIN.
|
||||
*!Authors: Amadou Bassirou DIALLO (Poverty Division, World Bank) and Jean-Benoit HARDOUIN (Regional Health Observatory of Orl<72>ans).
|
||||
|
||||
program checkfor2 , rclass
|
||||
version 8
|
||||
syntax anything [if] [in] [, noList Tolerance(real 0) TAble noSUm GENMiss(namelist min=1 max=1) MISsing(string)]
|
||||
|
||||
marksample touse
|
||||
tempname rat
|
||||
local av
|
||||
local unav
|
||||
local manymissings
|
||||
local avnum
|
||||
|
||||
quietly count if `touse'
|
||||
local tot = r(N)
|
||||
|
||||
qui isvar `anything'
|
||||
local badlist `r(badlist)'
|
||||
local varlist `r(varlist)'
|
||||
|
||||
di _n
|
||||
if "`table'"!="" {
|
||||
if "`badlist'"!="" {
|
||||
di _col(4) in green "{hline 39}"
|
||||
di _col(4)in green "Unavailable variables: "
|
||||
foreach i of local badlist {
|
||||
di _col(4) in ye "`i'"
|
||||
}
|
||||
di _col(4) in green "{hline 39}"
|
||||
di
|
||||
}
|
||||
di _col(4) in green "{hline 39}"
|
||||
display _col(4) in gr "Existing" _col(15) in gr "Rate of"
|
||||
display _col(4) in gr "Variable" _col(14) "missings" _col(26) "Type" _col(34) "Available"
|
||||
di _col(4) in green "{hline 39}"
|
||||
}
|
||||
|
||||
tokenize `varlist'
|
||||
local nbvar : word count `varlist'
|
||||
|
||||
forvalues i=1/`nbvar' {
|
||||
capture assert missing(``i'') if `touse'
|
||||
local ty: type ``i''
|
||||
local tty = substr("`ty'", 1, 3)
|
||||
if !_rc {
|
||||
if "`table'"=="" {
|
||||
display in ye "``i''" in gr " is empty in the database." in ye " ``i''" in gr " is not added to the available list."
|
||||
}
|
||||
else {
|
||||
display _col(4) in gr "`=abbrev("``i''",8)'" _col(15) in ye "100.00%" _col(26) "`ty'"
|
||||
}
|
||||
local manymissings `manymissings' ``i''
|
||||
}
|
||||
else {
|
||||
if "`table'"=="" {
|
||||
display in ye "``i''" in gr " exists and is not empty."
|
||||
}
|
||||
*Consider type
|
||||
if "`tty'" == "str" {
|
||||
qui count if (``i'' == ""|``i''=="`missing'") & `touse'
|
||||
local num = r(N)
|
||||
scalar `rat' = (`num'/`tot')*100
|
||||
}
|
||||
else {
|
||||
local avnum `avnum' ``i''
|
||||
capture confirm number `missing'
|
||||
if _rc!=0 {
|
||||
quietly count if ``i'' >= . & `touse'
|
||||
}
|
||||
else {
|
||||
quietly count if (``i'' >= .|``i''==`missing') & `touse'
|
||||
}
|
||||
local num = r(N)
|
||||
scalar `rat' = (`num'/`tot')*100
|
||||
}
|
||||
if "`table'"=="" {
|
||||
display in ye "``i''" in gr " has " in ye r(N) in gr " missings."
|
||||
display in gr "Ratio number of missings of" in ye " ``i''" in gr " to total number of observations: " in ye %6.2f `rat' "%"
|
||||
}
|
||||
|
||||
if `rat' <= `tolerance' {
|
||||
local av `av' ``i''
|
||||
if "`table'"=="" {
|
||||
display in ye "``i''" in gr " is added to the available list."
|
||||
}
|
||||
else {
|
||||
display _col(4) in gr "`=abbrev("``i''",8)'" in ye _col(15) %6.2f `rat' "%" _col(26) "`ty'" _col(34) "X"
|
||||
}
|
||||
}
|
||||
else {
|
||||
local manymissings `manymissings' ``i''
|
||||
if "`table'"=="" {
|
||||
display in ye "``i''" in gr " has too many missings, compared to the tolerance level."
|
||||
display in ye "``i''" in gr " is not added to the available list."
|
||||
}
|
||||
else {
|
||||
display _col(4) in gr "`=abbrev("``i''",8)'" _col(15) in ye %6.2f `rat' "%" _col(26) "`ty'"
|
||||
}
|
||||
}
|
||||
}
|
||||
if "`table'"=="" {
|
||||
di
|
||||
}
|
||||
}
|
||||
|
||||
if "`table'"!="" {
|
||||
di _col(4) in green "{hline 39}"
|
||||
}
|
||||
|
||||
return local available `av'
|
||||
return local unavailable `badlist'
|
||||
return local manymissings `manymissings'
|
||||
|
||||
if "`avnum'" ~= ""&"`sum'"=="" {
|
||||
display _newline
|
||||
display in ye _col(14) "Unweighted summary statistics for available variables:" _n
|
||||
capture confirm number `missing'
|
||||
if _rc!=0 {
|
||||
summarize `avnum' if `touse'
|
||||
}
|
||||
else {
|
||||
foreach i of local avnum {
|
||||
summarize `i' if `touse'&`i'!=`missing'
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if "`list'"== "" {
|
||||
display _newline
|
||||
display in ye _d(97) "_"
|
||||
display _newline
|
||||
if "`badlist'"~="" {
|
||||
display in gr "Unavailable variables: " in ye _col(45) "`badlist'" _n
|
||||
}
|
||||
if "`av'"~="" {
|
||||
display in gr "Available variables: " in ye _col(45) "`av'" _n
|
||||
}
|
||||
if "`manymissings'"~="" {
|
||||
display in gr "Available variables but with too missings: " in ye _col(45) "`manymissings'" _n
|
||||
}
|
||||
display in ye _d(97) "_"
|
||||
}
|
||||
|
||||
if "`genmiss'" !="" {
|
||||
capture confirm variable `genmiss'
|
||||
if _rc!=0 {
|
||||
qui gen `genmiss' = 0
|
||||
local nbav : word count `av'
|
||||
tokenize `av'
|
||||
forvalues i=1/`nbav' {
|
||||
local ty: type ``i''
|
||||
local tty = substr("`ty'", 1, 3)
|
||||
if "`tty'" == "str" {
|
||||
qui replace `genmiss'=`genmiss'+1 if ``i''=="."
|
||||
}
|
||||
else {
|
||||
qui replace `genmiss'=`genmiss'+1 if ``i''>=.
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
di in green "The variable" in ye " `genmiss' " in green "already exists".
|
||||
}
|
||||
}
|
||||
|
||||
end
|
||||
@ -1,88 +0,0 @@
|
||||
{smcl}
|
||||
{hline}
|
||||
help for {cmd:checkfor2} {right:Amadou B. DIALLO}
|
||||
{right:Jean-Benoit HARDOUIN}
|
||||
{hline}
|
||||
|
||||
{title:Module to check whether a variable exists or not in a dataset.}
|
||||
|
||||
{p 4 8 2}{cmd:checkfor2} {it:anything} [{cmd:,}
|
||||
{cmdab:t:olerance}({it:#}) {cmdab:ta:ble} {cmdab:nol:ist} {cmdab:nosu:m}
|
||||
{cmdab:genm:iss}({it:newvarname}) {cmdab:mis:sing}({it:string})]
|
||||
|
||||
{title:Description}
|
||||
|
||||
{p 4 4 2}{cmd:checkfor2} is a data management routine to check for existence of variables
|
||||
within a (usually big) data set.
|
||||
|
||||
{p 4 4 2}{cmd:checkfor2} searchs through the data whether each variable exists.
|
||||
The variables are clustered between unavailable variables, available variables with
|
||||
a little amount of missing values and available variables with too many missing values.
|
||||
|
||||
{p 4 4 2}{cmd:isvar} must be installed ({stata ssc install isvar:ssc install isvar}).
|
||||
|
||||
{title:Options}
|
||||
|
||||
{p 4 4 2}{it:anything} is composed of variable names or lists of variables,
|
||||
|
||||
{p 4 4 2}{cmd:tolerance} is the tolerance level (in percentage) to consider a variable as available, with default 0,
|
||||
|
||||
{p 4 4 2}{cmd:nolist} avoids displaying availability status at the end of the process,
|
||||
|
||||
{p 4 4 2}{cmd:nosum} avoids displaying summary statistics of available variables,
|
||||
|
||||
{p 4 4 2}{cmd:table} displays the results in a table (instead as text),
|
||||
|
||||
{p 4 4 2}{cmd:genmiss} creates a new variable containing the number of missing values among the available variables,
|
||||
|
||||
{p 4 4 2}{cmd:missing} defines a specific value or string considered as a missing value.
|
||||
|
||||
|
||||
{title:Saved results}
|
||||
|
||||
{p 4 4 2} {cmd:r(unavailable)} names of unavailable variables.{p_end}
|
||||
|
||||
{p 4 4 2} {cmd:r(available)} names of available variables with a small amount of missing values.{p_end}
|
||||
|
||||
{p 4 4 2} {cmd:r(manymissings)} names of variables present but with too missings.{p_end}
|
||||
|
||||
{title:Examples}
|
||||
|
||||
{p 4 4 2}{cmd:. use mydata, clear }{p_end}
|
||||
|
||||
{p 4 4 2}{cmd:. checkfor2 x y z , mis(99) genmiss(countmiss) }{p_end}
|
||||
|
||||
{p 4 4 2}{cmd:. su `r(available)' }{p_end}
|
||||
|
||||
{p 4 4 2}{cmd:. tab countmiss }{p_end}
|
||||
|
||||
{p 4 4 2}{cmd:. u bigdataset in 1/100, clear // Big data set}{p_end}
|
||||
|
||||
{p 4 4 2}{cmd:. checkfor2 v1 v2 v3 xx yy , nosum tol(5) tab}{p_end}
|
||||
|
||||
{p 4 4 2}{cmd:. use `r(available)' using bigdataset, clear }{p_end}
|
||||
|
||||
{title:Remarks}
|
||||
|
||||
{p 4 4 2}{cmd:checkfor2} and its primary version ({cmd:checkfor}) have been primarily written for comparable surveys such as the Demography and
|
||||
Health Surveys (DHS) or the Multiple Indicator Cluster Surveys (MICS). But this could easily applied
|
||||
to any other survey.
|
||||
|
||||
{title:Authors}
|
||||
|
||||
{p 4 4 2}Amadou Bassirou DIALLO.
|
||||
Poverty and Health Division, PREM, The World Bank.{p_end}
|
||||
{p 4 4 2}Email: {browse "mailto:adiallo5@worldbank.org":adiallo5@worldbank.org}
|
||||
|
||||
{p 4 4 2}Jean-Benoit HARDOUIN.
|
||||
Regional Health Observatory of Orl<72>ans, France.{p_end}
|
||||
{p 4 4 2}Email: {browse "mailto:jean-benoit.hardouin@orscentre.org":jean-benoit.hardouin@orscentre.org}
|
||||
|
||||
{title:Aknowledgements}
|
||||
|
||||
{p 4 4 2}We would like to thank Christophe Rockmore and also Nick Cox
|
||||
and Kit Baum for their comments.
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{p 4 13 2}Online: help for {help checkfor}, {help isvar}, {help nmissing}, {help npresent}, {help missing} and {help dropmiss} if installed.{p_end}
|
||||
@ -1,140 +0,0 @@
|
||||
program def choplist, rclass
|
||||
*! NJC 1.4.0 13 December 2000
|
||||
* NJC 1.3.0 29 June 2000
|
||||
* NJC 1.2.0 7 June 2000
|
||||
* NJC 1.1.0 22 Dec 1999
|
||||
* NJC 1.0.0 20 Dec 1999 after discussion with Kit Baum
|
||||
version 6.0
|
||||
gettoken list 0 : 0, parse(",")
|
||||
if "`list'" == "" | "`list'" == "," {
|
||||
di in r "nothing in list"
|
||||
exit 198
|
||||
}
|
||||
syntax , [ Pos(str) Value(str asis) Length(str) Char(int 0) /*
|
||||
*/ Noisily Global(str) ]
|
||||
|
||||
if "`global'" != "" {
|
||||
tokenize `global'
|
||||
args global1 global2 global3
|
||||
if "`global3'" != "" {
|
||||
di in r "global( ) must contain at most 2 names"
|
||||
exit 198
|
||||
}
|
||||
if (length("`global1'") > 8) | (length("`global2'") > 8) {
|
||||
di in r "global name must be <=8 characters"
|
||||
exit 198
|
||||
}
|
||||
}
|
||||
|
||||
local nopts = /*
|
||||
*/ ("`pos'" != "") + (`"`value'"' != "") + /*
|
||||
*/ ("`length'" != "") + (`char' != 0)
|
||||
if `nopts' != 1 {
|
||||
di in r "must specify pos( ), value( ), length( ) or char( )"
|
||||
exit 198
|
||||
}
|
||||
|
||||
* as string <= contains quote
|
||||
local asstr = index(`"`value'"', `"""')
|
||||
|
||||
tokenize `list'
|
||||
local n : word count `list'
|
||||
|
||||
if "`length'" != "" | `char' != 0 {
|
||||
local i = 1
|
||||
while `i' <= `n' {
|
||||
local len = length("``i''")
|
||||
if `len' > 80 {
|
||||
di in r "cannot handle word length > 80"
|
||||
exit 498
|
||||
}
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
|
||||
local i = 1
|
||||
if "`pos'" != "" {
|
||||
local negsign = index("`pos'", "-")
|
||||
if `negsign' {
|
||||
local pos1 = substr("`pos'",1,`negsign' - 1)
|
||||
local pos2 = substr("`pos'",`negsign', .)
|
||||
local pos2 = `n' + 1 + `pos2'
|
||||
local pos "`pos1'`pos2'"
|
||||
capture confirm integer number `pos'
|
||||
if _rc == 0 { local pos ">= `pos'" }
|
||||
}
|
||||
else {
|
||||
capture confirm integer number `pos'
|
||||
if _rc == 0 { local pos "<= `pos'" }
|
||||
}
|
||||
while `i' <= `n' {
|
||||
if `i' `pos' { local list1 "`list1' ``i''" }
|
||||
else local list2 "`list2' ``i''"
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
else if "`value'" != "" {
|
||||
capture confirm number `value'
|
||||
if _rc == 0 { local value "<= `value'" }
|
||||
if `asstr' {
|
||||
while `i' <= `n' {
|
||||
if "``i''" `value' {
|
||||
local list1 `"`list1' ``i''"'
|
||||
}
|
||||
else local list2 `"`list2' ``i''"'
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
else {
|
||||
while `i' <= `n' {
|
||||
if ``i'' `value' {
|
||||
local list1 "`list1' ``i''"
|
||||
}
|
||||
else local list2 "`list2' ``i''"
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
}
|
||||
else if "`length'" != "" {
|
||||
capture confirm number `length'
|
||||
if _rc == 0 { local length "<= `length'" }
|
||||
while `i' <= `n' {
|
||||
if length("``i''") `length' {
|
||||
local list1 "`list1' ``i''"
|
||||
}
|
||||
else local list2 "`list2' ``i''"
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
else {
|
||||
if `char' >= 0 {
|
||||
while `i' <= `n' {
|
||||
local one = substr("``i''",1,`char')
|
||||
local two = substr("``i''",`char'+1,.)
|
||||
local list1 "`list1' `one'"
|
||||
local list2 "`list2' `two'"
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
else if `char' < 0 {
|
||||
while `i' <= `n' {
|
||||
local one = substr("``i''",`char',.)
|
||||
local ltwo = length("``i''") + `char'
|
||||
local two = substr("``i''",1,`ltwo')
|
||||
local list1 "`list1' `one'"
|
||||
local list2 "`list2' `two'"
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if "`noisily'" != "" {
|
||||
di in g "list 1: " in y `"`list1'"'
|
||||
di in g "list 2: " in y `"`list2'"'
|
||||
}
|
||||
if "`global1'" != "" { global `global1' `"`list1'"' }
|
||||
if "`global2'" != "" { global `global2' `"`list2'"' }
|
||||
return local list1 `"`list1'"'
|
||||
return local list2 `"`list2'"'
|
||||
end
|
||||
|
||||
@ -1,2 +0,0 @@
|
||||
.h listutil
|
||||
|
||||
@ -1,66 +0,0 @@
|
||||
*! version 1.0.4 PR 30sep2005
|
||||
* Based on private version 6.1.4 of frac_chk, PR 25aug2004
|
||||
program define cmdchk, sclass
|
||||
version 7
|
||||
local cmd `1'
|
||||
mac shift
|
||||
local cmds `*'
|
||||
sret clear
|
||||
if substr("`cmd'",1,3)=="reg" {
|
||||
local cmd regress
|
||||
}
|
||||
if "`cmds'"=="" {
|
||||
tokenize clogit cnreg cox ereg fit glm logistic logit poisson probit /*
|
||||
*/ qreg regress rreg weibull xtgee streg stcox stpm stpmrs /*
|
||||
*/ ologit oprobit mlogit nbreg
|
||||
}
|
||||
else tokenize `cmds'
|
||||
sret local bad 0
|
||||
local done 0
|
||||
while "`1'"!="" & !`done' {
|
||||
if "`1'"=="`cmd'" {
|
||||
local done 1
|
||||
}
|
||||
mac shift
|
||||
}
|
||||
if !`done' {
|
||||
sret local bad 1
|
||||
*exit
|
||||
}
|
||||
/*
|
||||
dist=0 (normal), 1 (binomial), 2 (poisson), 3 (cox), 4 (glm),
|
||||
5 (xtgee), 6 (ereg/weibull), 7 (stcox, streg, stpm, stpmrs).
|
||||
*/
|
||||
if "`cmd'"=="logit" | "`cmd'"=="probit" /*
|
||||
*/ |"`cmd'"=="clogit"| "`cmd'"=="logistic" /*
|
||||
*/ |"`cmd'"=="mlogit"| "`cmd'"=="ologit" | "`cmd'"=="oprobit" {
|
||||
sret local dist 1
|
||||
}
|
||||
else if "`cmd'"=="poisson" {
|
||||
sret local dist 2
|
||||
}
|
||||
else if "`cmd'"=="cox" {
|
||||
sret local dist 3
|
||||
}
|
||||
else if "`cmd'"=="glm" {
|
||||
sret local dist 4
|
||||
}
|
||||
else if "`cmd'"=="xtgee" {
|
||||
sret local dist 5
|
||||
}
|
||||
else if "`cmd'"=="cnreg" | "`cmd'"=="ereg" | "`cmd'"=="weibull" | "`cmd'"=="nbreg" {
|
||||
sret local dist 6
|
||||
}
|
||||
else if "`cmd'"=="stcox" | "`cmd'"=="streg" | "`cmd'"=="stpm" | "`cmd'"=="stpmrs" {
|
||||
sret local dist 7
|
||||
}
|
||||
else if substr("`cmd'",1,2)=="st" {
|
||||
sret local dist 7
|
||||
}
|
||||
else sret local dist 0
|
||||
|
||||
sret local isglm = (`s(dist)'==4)
|
||||
sret local isqreg = ("`cmd'"=="qreg")
|
||||
sret local isxtgee= (`s(dist)'==5)
|
||||
sret local isnorm = ("`cmd'"=="regress"|"`cmd'"=="fit"|"`cmd'"=="rreg")
|
||||
end
|
||||
@ -1,33 +0,0 @@
|
||||
program def collist
|
||||
*! NJC 1.0.0 22 Dec 1999
|
||||
version 6
|
||||
gettoken list 0 : 0, parse(",")
|
||||
if "`list'" == "" | "`list'" == "," {
|
||||
di in r "nothing in list"
|
||||
exit 198
|
||||
}
|
||||
syntax [ , Format(str) NUmber ]
|
||||
|
||||
tokenize `list'
|
||||
|
||||
if "`number'" != "" {
|
||||
local n : word count `list'
|
||||
local ndigits = length("`n'")
|
||||
local number = 0
|
||||
}
|
||||
|
||||
* asstr >0 string format
|
||||
* asstr 0 numeric format
|
||||
* asstr -1 default
|
||||
local asstr = cond("`format'" != "",index("`format'", "s"), -1)
|
||||
|
||||
while "`1'" != "" {
|
||||
if "`number'" != "" {
|
||||
local number = `number' + 1
|
||||
local num : di %`ndigits'.0f `number' ". "
|
||||
}
|
||||
if `asstr' { di in g "`num'" `format' in y `"`1'"' }
|
||||
else di in g "`num'" `format' in y `1'
|
||||
mac shift
|
||||
}
|
||||
end
|
||||
@ -1,2 +0,0 @@
|
||||
.h listutil
|
||||
|
||||
@ -1,433 +0,0 @@
|
||||
*! 3.0.7 9aug2007 TJS & NJC fixed Stata 10 bug by adding missing -version-
|
||||
* 3.0.6 14may2006 TJS & NJC fixed Stata 9 bug with -by()-
|
||||
// 3.0.5 22aug2005 Stata 8 - fixed legend(off)
|
||||
// 3.0.4 27jan2005 TJS & NJC rewrite for Stata 8 + more for graphs
|
||||
// 3.0.3 6oct2004 TJS & NJC rewrite for Stata 8
|
||||
// 3.0.2 6oct2004 TJS & NJC rewrite for Stata 8
|
||||
// 3.0.1 4oct2004 TJS & NJC rewrite for Stata 8 + fixes + noref
|
||||
// 3.0.0 27jul2004 TJS & NJC rewrite for Stata 8
|
||||
// 3.0.0 19jul2004 TJS & NJC rewrite for Stata 8
|
||||
// 2.2.9 14jan2003 TJS & NJC handling of [ ] within connect()
|
||||
// 2.2.8 2jan2003 TJS & NJC handling of [ ] within symbol()
|
||||
// 2.2.7 30jan2002 TJS & NJC rho_se corrected (SJ2-2: st0015)
|
||||
// 2.2.6 10dec2001 TJS & NJC bug fixes (labels, diag line)
|
||||
// 2.2.5 23apr2001 TJS & NJC sortpreserve
|
||||
// 2.2.4 24jan2001 TJS & NJC l1title for loa
|
||||
// 2.2.3 8sep2000 TJS & NJC bug fixes & mods (STB-58: sg84.3)
|
||||
// 2.2.0 16dec1999 TJS & NJC version 6 changes (STB-54: sg84.2)
|
||||
// 2.1.6 18jun1998 TJS & NJC STB-45 sg84.1
|
||||
// 2.0.2 6mar1998 TJS & NJC STB-43 sg84
|
||||
//
|
||||
// syntax: concord vary varx [fw] [if] [in] [,
|
||||
// BY(varname) Summary LEvel(real `c(level)')
|
||||
// CCC(str) LOA(str) QNORMD(str) ]
|
||||
|
||||
program concord, rclass sortpreserve
|
||||
// Syntax help
|
||||
if "`1'" == "" {
|
||||
di "{p}{txt}Syntax is: {inp:concord} " ///
|
||||
"{it:vary varx} [{it:weight}] " ///
|
||||
"[{inp:if} {it:exp}] [{inp:in} {it:range}] " ///
|
||||
"[, {inp:by(}{it:byvar}{inp:)} " ///
|
||||
"{inp:summary level(}{it:#}{inp:)} " ///
|
||||
"{inp:ccc}[{inp:(noref} {it:ccc_options}{inp:)}] " ///
|
||||
"{inp:loa}[{inp:(noref regline} {it:loa_options}{inp:)}] " ///
|
||||
"{inp:qnormd}[{inp:(}{it:qnormd_options}{inp:)}] {p_end}"
|
||||
exit 0
|
||||
}
|
||||
|
||||
// Setup
|
||||
version 8
|
||||
syntax varlist(numeric min=2 max=2) ///
|
||||
[fw] ///
|
||||
[if] [in] ///
|
||||
[ , BY(varname) ///
|
||||
Summary ///
|
||||
LEvel(real `c(level)') ///
|
||||
ccc(str asis) ///
|
||||
CCC2 ///
|
||||
loa(str asis) ///
|
||||
LOA2 ///
|
||||
qnormd(str asis) ///
|
||||
QNORMD2 * ]
|
||||
|
||||
marksample touse
|
||||
qui count if `touse'
|
||||
if r(N) == 0 error 2000
|
||||
|
||||
tokenize `varlist'
|
||||
|
||||
// Set temporary names
|
||||
tempvar d d2 db dll dul m byg kk bylabel
|
||||
tempname dsd zv k xb yb sx2 sy2 r rp sxy p u sep z zp
|
||||
tempname t set ll ul llt ult rdm Fdm zt ztp sd1 sd2 sl
|
||||
|
||||
// Set up wgt
|
||||
if "`weight'" != "" local wgt "[`weight'`exp']"
|
||||
|
||||
// Generate CI z-value and label from Level()
|
||||
if `level' < 1 local level = `level' * 100
|
||||
scalar `zv' = -1 * invnorm((1 - `level' / 100) / 2)
|
||||
local rl = `level'
|
||||
local level : di %7.0g `level'
|
||||
local level = ltrim("`level'")
|
||||
|
||||
// Generate BY groups
|
||||
qui {
|
||||
bysort `touse' `by' : gen byte `byg' = _n == 1 if `touse'
|
||||
if "`by'" != "" gen `kk' = _n if `byg' == 1
|
||||
replace `byg' = sum(`byg')
|
||||
local byn = `byg'[_N]
|
||||
|
||||
// Generate `by' labels -- if required
|
||||
if "`by'" != "" {
|
||||
capture decode `by', gen(`bylabel')
|
||||
if _rc != 0 {
|
||||
local type : type `by'
|
||||
gen `type' `bylabel' = `by'
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Print title
|
||||
di
|
||||
di as txt "Concordance correlation coefficient (Lin, 1989, 2000):"
|
||||
|
||||
// Do calculations
|
||||
forval j = 1/`byn' { /* start of loop for each `by' group */
|
||||
di
|
||||
if "`by'" != "" {
|
||||
sort `kk'
|
||||
di as txt "{hline}"
|
||||
di as txt "-> `by' = " `bylabel'[`j'] _n
|
||||
local byl : di "`by' = " `bylabel'[`j']
|
||||
}
|
||||
|
||||
// LOA (Bland & Altman) calculations
|
||||
qui {
|
||||
gen `d' = `1' - `2'
|
||||
gen `d2' = `d'^2
|
||||
su `d' if `byg' == `j' `wgt'
|
||||
gen `db' = r(mean)
|
||||
scalar `dsd' = r(sd)
|
||||
gen `dll' = `db' - `zv' * `dsd'
|
||||
gen `dul' = `db' + `zv' * `dsd'
|
||||
gen `m' = (`1' + `2') / 2
|
||||
}
|
||||
|
||||
// Concordance calculations
|
||||
qui su `1' if `byg' == `j' `wgt'
|
||||
scalar `k' = r(N)
|
||||
scalar `yb' = r(mean)
|
||||
scalar `sy2' = r(Var) * (`k' - 1) / `k'
|
||||
scalar `sd1' = r(sd)
|
||||
|
||||
qui su `2' if `byg' == `j' `wgt'
|
||||
scalar `xb' = r(mean)
|
||||
scalar `sx2' = r(Var) * (`k' - 1) / `k'
|
||||
scalar `sd2' = r(sd)
|
||||
|
||||
qui corr `1' `2' if `byg' == `j' `wgt'
|
||||
scalar `r' = r(rho)
|
||||
scalar `sl' = sign(`r') * `sd1' / `sd2'
|
||||
|
||||
scalar `rp' = min(tprob(r(N) - 2, r(rho) * sqrt(r(N) - 2) ///
|
||||
/ sqrt(1 - r(rho)^2)) ,1)
|
||||
scalar `sxy' = `r' * sqrt(`sx2' * `sy2')
|
||||
scalar `p' = 2 * `sxy' / (`sx2' + `sy2' + (`yb' - `xb')^2)
|
||||
scalar `u' = (`yb' - `xb') / (`sx2' * `sy2')^.25
|
||||
|
||||
// --- variance, test, and CI for asymptotic normal approximation
|
||||
// scalar `sep' = sqrt(((1 - ((`r')^2)) * (`p')^2 * (1 -
|
||||
// ((`p')^2)) / (`r')^2 + (4 * (`p')^3 * (1 - `p') * (`u')^2
|
||||
// / `r') - 2 * (`p')^4 * (`u')^4 / (`r')^2 ) / (`k' - 2))
|
||||
// Corrected se: per Lin (March 2000) Biometrics 56:325-5.
|
||||
#delimit ;
|
||||
scalar `sep' = sqrt(((1 - ((`r')^2)) * (`p')^2 * (1 -
|
||||
((`p')^2)) / (`r')^2 + (2 * (`p')^3 * (1 - `p') * (`u')^2
|
||||
/ `r') - .5 * (`p')^4 * (`u')^4 / (`r')^2 ) / (`k' - 2));
|
||||
#delimit cr
|
||||
scalar `z' = `p' / `sep'
|
||||
scalar `zp' = 2 * (1 - normprob(abs(`z')))
|
||||
scalar `ll' = `p' - `zv' * `sep'
|
||||
scalar `ul' = `p' + `zv' * `sep'
|
||||
|
||||
// --- statistic, variance, test, and CI for inverse hyperbolic
|
||||
// tangent transform to improve asymptotic normality
|
||||
scalar `t' = ln((1 + `p') / (1 - `p')) / 2
|
||||
scalar `set' = `sep' / (1 - ((`p')^2))
|
||||
scalar `zt' = `t' / `set'
|
||||
scalar `ztp' = 2 * (1 - normprob(abs(`zt')))
|
||||
scalar `llt' = `t' - `zv' * `set'
|
||||
scalar `ult' = `t' + `zv' * `set'
|
||||
scalar `llt' = (exp(2 * `llt') - 1) / (exp(2 * `llt') + 1)
|
||||
scalar `ult' = (exp(2 * `ult') - 1) / (exp(2 * `ult') + 1)
|
||||
|
||||
// Print output
|
||||
di as txt " rho_c SE(rho_c) Obs [" _c
|
||||
if index("`level'",".") {
|
||||
di as txt %6.1f `level' "% CI ] P CI type"
|
||||
}
|
||||
else di as txt " `level'% CI ] P CI type"
|
||||
|
||||
di as txt "{hline 63}"
|
||||
di as res %6.3f `p' %10.3f `sep' %8.0f `k' %10.3f `ll' _c
|
||||
di as res %7.3f `ul' %9.3f `zp' as txt " asymptotic"
|
||||
di as res _dup(24) " " %10.3f `llt' %7.3f `ult' %9.3f `ztp' _c
|
||||
di as txt " z-transform"
|
||||
|
||||
di _n as txt "Pearson's r =" as res %7.3f `r' _c
|
||||
di as txt " Pr(r = 0) =" as res %6.3f `rp' _c
|
||||
di as txt " C_b = rho_c/r =" as res %7.3f `p' / `r'
|
||||
di as txt "Reduced major axis: Slope = " as res %9.3f `sl' _c
|
||||
di as txt " Intercept = " as res %9.3f `yb'-`xb'*`sl'
|
||||
di _n as txt "Difference = `1' - `2'"
|
||||
di _n as txt " Difference" _c
|
||||
if index("`level'", ".") {
|
||||
di _col(33) as txt %6.1f `level' "% Limits Of Agreement"
|
||||
}
|
||||
else di _col(33) as txt " `level'% Limits Of Agreement"
|
||||
di as txt " Average Std Dev. (Bland & Altman, 1986)"
|
||||
di as txt "{hline 63}"
|
||||
di as res %10.3f `db' %12.3f `dsd' _c
|
||||
di as res " " %11.3f `dll' %11.3f `dul'
|
||||
|
||||
qui corr `d' `m' if `byg' == `j' `wgt'
|
||||
scalar `rdm' = r(rho)
|
||||
di _n as txt "Correlation between difference and mean =" _c
|
||||
local fmt = cond(r(rho) < 0, "%7.3f", "%6.3f")
|
||||
di as res `fmt' r(rho)
|
||||
|
||||
su `d2' if `byg' == `j' `wgt', meanonly
|
||||
local sumd2 = r(sum)
|
||||
qui reg `d' `m' if `byg' == `j' `wgt'
|
||||
scalar `Fdm' = ((`sumd2' - e(rss)) / 2) / (e(rss) / e(df_r))
|
||||
di _n as txt "Bradley-Blackwood F = " ///
|
||||
as res %4.3f `Fdm' ///
|
||||
as txt " (P = " %6.5f ///
|
||||
as res 1 - F(2, e(df_r), `Fdm') ///
|
||||
as txt ")"
|
||||
|
||||
if "`summary'" != "" su `1' `2' if `byg' == `j' `wgt'
|
||||
|
||||
// setup local options for passing to graph routines
|
||||
if "`byl'" != "" local byls byl("`byl'")
|
||||
if "`level'" != "" local levs level(`level')
|
||||
|
||||
// set more if needed
|
||||
if (`"`loa'`loa2'"' != "" & `"`qnormd'`qnormd2'"' != "") | ///
|
||||
(`"`loa'`loa2'"' != "" & `"`ccc'`ccc2'"' != "") | ///
|
||||
(`"`ccc'`ccc2'"' != "" & `"`qnormd'`qnormd2'"' != "") {
|
||||
|
||||
local moreflag "more"
|
||||
}
|
||||
|
||||
// loa graph
|
||||
if `"`loa'`loa2'"' != "" {
|
||||
gphloa `2' `1' `dll' `db' `dul' `d' `m' `byg' ///
|
||||
`wgt', j(`j') byn(`byn') `byls' `levs' `loa' `options'
|
||||
|
||||
`moreflag'
|
||||
}
|
||||
|
||||
// qnormd graph
|
||||
if `"`qnormd'`qnormd2'"' != "" {
|
||||
gphqnormd `2' `1' `d' `byg' ///
|
||||
`wgt', j(`j') byn(`byn') `byls' `levs' `qnormd' `options'
|
||||
|
||||
`moreflag'
|
||||
}
|
||||
|
||||
// ccc graph
|
||||
if `"`ccc'`ccc2'"' != "" {
|
||||
local sll = `sl'
|
||||
local xbl = `xb'
|
||||
local ybl = `yb'
|
||||
gphccc `1' `2' `byg' `wgt', j(`j') ///
|
||||
xb(`xbl') yb(`ybl') sl(`sll') byn(`byn') `byls' `ccc' `options'
|
||||
}
|
||||
|
||||
if `byn' > 1 {
|
||||
capture drop `d'
|
||||
capture drop `d2'
|
||||
capture drop `db'
|
||||
capture drop `dll'
|
||||
capture drop `dul'
|
||||
capture drop `m'
|
||||
}
|
||||
|
||||
} /* end of loop for each `by' group */
|
||||
|
||||
// save globals
|
||||
if `byn' == 1 {
|
||||
return scalar N = `k'
|
||||
return scalar rho_c = `p'
|
||||
return scalar se_rho_c = `sep'
|
||||
return scalar asym_ll = `ll'
|
||||
return scalar asym_ul = `ul'
|
||||
return scalar z_tr_ll = `llt'
|
||||
return scalar z_tr_ul = `ult'
|
||||
return scalar C_b = `p' / `r'
|
||||
return scalar diff = `db'
|
||||
return scalar sd_diff = `dsd'
|
||||
return scalar LOA_ll = `dll'
|
||||
return scalar LOA_ul = `dul'
|
||||
return scalar rdm = `rdm'
|
||||
return scalar Fdm = `Fdm'
|
||||
|
||||
// double save globals
|
||||
// now undocumented as of 3.0.0
|
||||
global S_1 = `k'
|
||||
global S_2 = `p'
|
||||
global S_3 = `sep'
|
||||
global S_4 = `ll'
|
||||
global S_5 = `ul'
|
||||
global S_6 = `llt'
|
||||
global S_7 = `ult'
|
||||
global S_8 = `p' / `r'
|
||||
global S_9 = `db'
|
||||
global S_10 = `dsd'
|
||||
global S_11 = `dll'
|
||||
global S_12 = `dul'
|
||||
}
|
||||
end
|
||||
|
||||
program gphloa
|
||||
// loa graph
|
||||
version 8
|
||||
syntax varlist(numeric min=2) [fw] ///
|
||||
[ , J(int 1) BYN(int 1) BYL(str) REGline LEvel(real `c(level)') ///
|
||||
plot(str asis) noREF * ]
|
||||
|
||||
tokenize `varlist'
|
||||
args two one dll db dul d m byg
|
||||
if "`weight'" != "" local wgt "[`weight'`exp']"
|
||||
|
||||
if `"`byl'"' != "" local t2title `"t2title(`byl')"'
|
||||
|
||||
local name2 : variable label `2'
|
||||
local name1 : variable label `1'
|
||||
local lnth = length(`"`name2'"') + length(`"`name1'"')
|
||||
if `"`name2'"' == `""' | `lnth' > 50 local name2 "`2'"
|
||||
if `"`name1'"' == `""' | `lnth' > 50 local name1 "`1'"
|
||||
|
||||
qui if "`regline'" != "" {
|
||||
tempvar fit
|
||||
regress `d' `m' if `byg' == `j' `wgt'
|
||||
predict `fit'
|
||||
}
|
||||
|
||||
if "`ref'" == "" {
|
||||
local ord 2 3
|
||||
if "`regline'" != "" local ord 2 3 4
|
||||
local zero yli(0, lstyle(refline)) yscale(range(0)) ylabel(0, add) ///
|
||||
legend(on order(`ord') label(2 observed average agreement) ///
|
||||
label(3 `"`level'% limits of agreement"') label(4 regression line)) ///
|
||||
caption("y=0 is line of perfect average agreement")
|
||||
}
|
||||
|
||||
graph twoway line `dll' `db' `dul' `fit' `m' if `byg' == `j', ///
|
||||
clcolor(red purple red green) sort ///
|
||||
|| scatter `d' `m' if `byg' == `j' ///
|
||||
, ms(oh) `t2title' ///
|
||||
yti(`"Difference of `name2' and `name1'"') ///
|
||||
xti(`"Mean of `name2' and `name1'"') ///
|
||||
caption("`level'% Limits Of Agreement") legend(off) `zero' ///
|
||||
`options' ///
|
||||
|| `plot'
|
||||
|
||||
if `byn' > 1 more
|
||||
end
|
||||
|
||||
program gphqnormd, sort
|
||||
// normal prob plot
|
||||
// note: logic pilfered from qnorm
|
||||
version 8
|
||||
syntax varlist(numeric min=2) [fw] ///
|
||||
[ , J(int 1) BYN(int 1) BYL(str) LEvel(real `c(level)') ///
|
||||
plot(str asis) * ]
|
||||
|
||||
args two one d byg
|
||||
if "`weight'" != "" local wgt "[`weight'`exp']"
|
||||
else local exp 1
|
||||
|
||||
local name2 : variable label `2'
|
||||
local name1 : variable label `1'
|
||||
local lnth = length(`"`name2'"') + length(`"`name1'"')
|
||||
if `"`name2'"' == `""' | `lnth' > 50 local name2 "`2'"
|
||||
if `"`name1'"' == `""' | `lnth' > 50 local name1 "`1'"
|
||||
|
||||
tempvar Z Psubi touse2
|
||||
mark `touse2' if `byg' == `j'
|
||||
qui {
|
||||
gsort -`touse2' `d'
|
||||
gen `Psubi' = sum(`touse2' * `exp')
|
||||
replace `Psubi' = cond(`touse2' == 0, ., `Psubi'/(`Psubi'[_N] + 1))
|
||||
su `d' if `touse2' == 1 `wgt'
|
||||
gen float `Z' = invnorm(`Psubi') * r(sd) + r(mean)
|
||||
label var `Z' "Inverse Normal"
|
||||
local xttl : var label `Z'
|
||||
local yttl `"Difference of `name2' and `name1'"'
|
||||
}
|
||||
|
||||
if `"`byl'"' != "" local t2title `"t2title(`byl')"'
|
||||
|
||||
graph twoway ///
|
||||
(scatter `d' `Z', ///
|
||||
sort ///
|
||||
ytitle(`"`yttl'"') ///
|
||||
xtitle(`"`xttl'"') ///
|
||||
`t2title' ///
|
||||
`options' ///
|
||||
) ///
|
||||
(function y=x, ///
|
||||
range(`Z') ///
|
||||
n(2) ///
|
||||
clstyle(refline) ///
|
||||
yvarlabel("Reference") ///
|
||||
yvarformat(`fmt') ///
|
||||
) ///
|
||||
, legend(off) ///
|
||||
|| `plot'
|
||||
|
||||
if `byn' > 1 more
|
||||
end
|
||||
|
||||
program gphccc
|
||||
version 8
|
||||
//-----------------------------------------------------
|
||||
// ccc graph
|
||||
// ----------------------------------------------------
|
||||
syntax varlist(numeric min=2) [fw] [ , J(int 1) XB(real 0) noREF ///
|
||||
YB(real 0) SL(real 0) BYN(int 1) BYL(str) plot(str asis) LEGEND(str) * ]
|
||||
|
||||
tokenize `varlist'
|
||||
tempvar byg rmaxis
|
||||
local byg `3'
|
||||
if "`weight'" != "" local wgt "[`weight'`exp']"
|
||||
|
||||
local yttl : variable label `1'
|
||||
if `"`yttl'"' == "" local yttl "`1'"
|
||||
local xttl : variable label `2'
|
||||
if `"`xttl'"' == "" local xttl "`2'"
|
||||
|
||||
if "`ref'" == "" local lopc || function y = x, ra(`2') clstyle(refline) ///
|
||||
legend(on order(2 "reduced major axis" 3 "line of perfect concordance"))
|
||||
|
||||
if "`legend'" != "" {
|
||||
local legnd "legend(`legend')"
|
||||
}
|
||||
|
||||
// Graph concordance plot
|
||||
qui gen `rmaxis' = `sl' * (`2' - `xb') + `yb'
|
||||
|
||||
graph twoway scatter `1' `rmaxis' `2' ///
|
||||
if `byg' == `j' `wgt', ///
|
||||
sort connect(none line) ms(oh none) ///
|
||||
yti(`"`yttl'"') xti(`"`xttl'"') legend(off) `lopc' ///
|
||||
`options' ///
|
||||
|| `plot'
|
||||
|
||||
if `byn' > 1 more
|
||||
end
|
||||
|
||||
@ -1,183 +0,0 @@
|
||||
/* Dialog (Version 3.0.1) by: T. J. Steichen, steichen@triad.rr.com
|
||||
|
||||
concord VERSION 3.0.3 6oct2004
|
||||
|
||||
syntax: concord vary varx [fw] [if] [in] [,
|
||||
BY(varname) Summary LEvel(real 95) ccc[(noREF ccc_graph_options)]
|
||||
loa[(noREF REGline loa_graph_options)] qnormd[(qnormd_graph_options}] ]
|
||||
|
||||
|
||||
Install in User Statistics menu via:
|
||||
. window menu append item "stUserStatistics" "&concord (Concordance correlation coefficient)" "db concord3"
|
||||
. window menu refresh
|
||||
To permanently install, place the commands in your -profile.do- file.
|
||||
*/
|
||||
|
||||
VERSION 8.0
|
||||
|
||||
INCLUDE _std_small
|
||||
INCLUDE header
|
||||
|
||||
HELP hlp1, view("help concord")
|
||||
RESET res1
|
||||
|
||||
DIALOG main, label("concord - Concordance correlation coefficient") tabtitle("Main")
|
||||
BEGIN
|
||||
TEXT tx_yvar _lft _top 150 ., ///
|
||||
label("Y Variable:")
|
||||
VARNAME vn_yvar @ _ss @ ., ///
|
||||
label("Y Variable")
|
||||
TEXT tx_xvar 180 _top 150 ., ///
|
||||
label("X Variable:")
|
||||
VARNAME vn_xvar @ _ss @ ., ///
|
||||
label("X Variable")
|
||||
|
||||
CHECKBOX cb_by 10 70 50 ., ///
|
||||
label("By:") ///
|
||||
onclickon(main.vn_by.enable) ///
|
||||
onclickoff(main.vn_by.disable)
|
||||
VARNAME vn_by 60 70 270 ., ///
|
||||
label("By Variable") ///
|
||||
option("by")
|
||||
|
||||
CHECKBOX cb_summary 10 100 300 ., ///
|
||||
label("Show Summary") ///
|
||||
option("summary")
|
||||
|
||||
CHECKBOX cb_level 10 130 50 ., ///
|
||||
label("Level:") ///
|
||||
onclickon(main.ed_level.enable) ///
|
||||
onclickoff(main.ed_level.disable)
|
||||
EDIT ed_level 60 @ 40 ., ///
|
||||
label("Level") ///
|
||||
numonly default(global S_level) ///
|
||||
option("level")
|
||||
END
|
||||
|
||||
INCLUDE ifin
|
||||
INCLUDE weights_f
|
||||
|
||||
DIALOG graph, tabtitle("Graph")
|
||||
BEGIN
|
||||
CHECKBOX cb_ccc 20 10 150 ., ///
|
||||
label("Concordance") ///
|
||||
onclickon(script ccc_on) ///
|
||||
onclickoff(script ccc_off) ///
|
||||
option("ccc")
|
||||
CHECKBOX cb_ccc_ref 40 30 140 ., ///
|
||||
label("No CCC Reference Line")
|
||||
CHECKBOX cb_ccc_opt 40 50 90 ., ///
|
||||
label("CCC Options:") ///
|
||||
onclickon(graph.ed_ccc_opt.enable) ///
|
||||
onclickoff(graph.ed_ccc_opt.disable)
|
||||
EDIT ed_ccc_opt 130 50 200 ., ///
|
||||
label("CCC Opts")
|
||||
|
||||
CHECKBOX cb_loa 20 70 150 ., ///
|
||||
label("Limits of Agreement") ///
|
||||
onclickon(script loa_on) ///
|
||||
onclickoff(script loa_off) ///
|
||||
option("loa")
|
||||
CHECKBOX cb_loa_ref 40 90 140 ., ///
|
||||
label("No LOA Reference Line")
|
||||
CHECKBOX cb_loa_reg 40 110 140 ., ///
|
||||
label("Regression Line")
|
||||
CHECKBOX cb_loa_opt 40 130 90 ., ///
|
||||
label("LOA Options:") ///
|
||||
onclickon(graph.ed_loa_opt.enable) ///
|
||||
onclickoff(graph.ed_loa_opt.disable)
|
||||
EDIT ed_loa_opt 130 130 200 ., ///
|
||||
label("LOA Opts")
|
||||
|
||||
CHECKBOX cb_qnormd 20 150 150 ., ///
|
||||
label("Differences Normal plot") ///
|
||||
onclickon(graph.cb_qnormd_opt.enable) ///
|
||||
onclickoff(graph.cb_qnormd_opt.disable) ///
|
||||
option("qnormd")
|
||||
CHECKBOX cb_qnormd_opt 40 170 90 ., ///
|
||||
label("QND Options:") ///
|
||||
onclickon(graph.ed_qnormd_opt.enable) ///
|
||||
onclickoff(graph.ed_qnormd_opt.disable)
|
||||
EDIT ed_qnormd_opt 130 170 200 ., ///
|
||||
label("QND Opts")
|
||||
END
|
||||
|
||||
SCRIPT ccc_on
|
||||
BEGIN
|
||||
graph.cb_ccc_ref.enable
|
||||
graph.cb_ccc_opt.enable
|
||||
END
|
||||
|
||||
SCRIPT ccc_off
|
||||
BEGIN
|
||||
graph.cb_ccc_ref.disable
|
||||
graph.cb_ccc_opt.disable
|
||||
END
|
||||
|
||||
SCRIPT loa_on
|
||||
BEGIN
|
||||
graph.cb_loa_ref.enable
|
||||
graph.cb_loa_reg.enable
|
||||
graph.cb_loa_opt.enable
|
||||
END
|
||||
|
||||
SCRIPT loa_off
|
||||
BEGIN
|
||||
graph.cb_loa_ref.disable
|
||||
graph.cb_loa_reg.disable
|
||||
graph.cb_loa_opt.disable
|
||||
END
|
||||
|
||||
PROGRAM command
|
||||
BEGIN
|
||||
put "concord "
|
||||
varlist main.vn_yvar main.vn_xvar
|
||||
INCLUDE _weights_pr
|
||||
INCLUDE _ifin_pr
|
||||
beginoptions
|
||||
optionarg main.vn_by
|
||||
option main.cb_summary
|
||||
optionarg main.ed_level
|
||||
if graph.cb_ccc {
|
||||
put "ccc"
|
||||
if graph.cb_ccc_ref | graph.cb_ccc_opt {
|
||||
put "("
|
||||
}
|
||||
if graph.cb_ccc_ref {
|
||||
put "noref "
|
||||
}
|
||||
if graph.cb_ccc_opt {
|
||||
put graph.ed_ccc_opt
|
||||
}
|
||||
if graph.cb_ccc_ref | graph.cb_ccc_opt {
|
||||
put ") "
|
||||
}
|
||||
}
|
||||
if graph.cb_loa {
|
||||
put "loa"
|
||||
if graph.cb_loa_ref | graph.cb_loa_opt | graph.cb_loa_reg {
|
||||
put "("
|
||||
}
|
||||
if graph.cb_loa_ref {
|
||||
put "noref "
|
||||
}
|
||||
if graph.cb_loa_reg {
|
||||
put "regline "
|
||||
}
|
||||
if graph.cb_loa_opt {
|
||||
put graph.ed_loa_opt
|
||||
}
|
||||
if graph.cb_loa_ref | graph.cb_loa_opt | graph.cb_loa_reg {
|
||||
put ") "
|
||||
}
|
||||
}
|
||||
if graph.cb_qnormd {
|
||||
put "qnormd"
|
||||
if graph.cb_qnormd_opt {
|
||||
put "("
|
||||
put graph.ed_qnormd_opt
|
||||
put ") "
|
||||
}
|
||||
}
|
||||
endoptions
|
||||
END
|
||||
@ -1,275 +0,0 @@
|
||||
{smcl}
|
||||
{* 6oct2004/23aug2005/14may2006/9aug2007}
|
||||
{hline}
|
||||
{hi:help concord} {right:(SJ7-3: st0015_4)}
|
||||
{hline}
|
||||
|
||||
{title:Title}
|
||||
|
||||
{p2colset 5 16 18 2}{...}
|
||||
{p2col:{hi:concord} {hline 2}}Concordance correlation coefficient and associated measures, tests, and graphs{p_end}
|
||||
{p2colreset}{...}
|
||||
|
||||
|
||||
{title:Syntax}
|
||||
|
||||
{p 8 17 2}
|
||||
{cmd:concord}
|
||||
{it:vary} {it:varx}
|
||||
{ifin}
|
||||
{weight}
|
||||
[{cmd:,}
|
||||
{cmd:by(}{it:byvar}{cmd:)}
|
||||
{cmdab:s:ummary}
|
||||
{cmdab:le:vel(}{it:#}{cmd:)}
|
||||
{cmd:ccc}[{cmd:(noref} {it:ccc_options}{cmd:)}]
|
||||
{cmd:loa}[{cmd:(noref} {cmdab:reg:line} {it:loa_options}{cmd:)}]
|
||||
{cmd:qnormd}[{cmd:(}{it:qnormd_options}{cmd:)}]]
|
||||
|
||||
|
||||
{title:Description}
|
||||
|
||||
{p 4 4 2}
|
||||
{cmd:concord} computes Lin's (1989, 2000) concordance correlation coefficient
|
||||
for agreement on a continuous measure obtained by two persons or methods. (The
|
||||
measure was introduced earlier by Krippendorff (1970).) The concordance
|
||||
correlation coefficient combines measures of both precision and accuracy to
|
||||
determine how far the observed data deviate from the line of perfect
|
||||
concordance (i.e., the line at 45 degrees on a square scatter plot). Lin's
|
||||
coefficient increases in value as a function of the nearness of the data's
|
||||
reduced major axis to the line of perfect concordance (the accuracy of the
|
||||
data) and of the tightness of the data about its reduced major axis (the
|
||||
precision of the data). The Pearson correlation coefficient, r, the
|
||||
bias-correction factor, C_b, and the equation of the reduced major axis are
|
||||
reported to show these components. Note that the concordance correlation
|
||||
coefficient, rho_c, can be expressed as the product of r, the measure of
|
||||
precision, and C_b, the measure of accuracy.
|
||||
|
||||
{p 4 4 2}
|
||||
{cmd:concord} also provides results for Bland and Altman's
|
||||
limits-of-agreement, "loa", procedure (1986). The loa, a data-scale assessment
|
||||
of the degree of agreement, is a complementary approach to the
|
||||
relationship-scale approach of Lin.
|
||||
|
||||
{p 4 4 2}
|
||||
Finally, two other results are reported:
|
||||
|
||||
{p 8 8 2}
|
||||
1. The correlation between difference and mean. In one interpretation
|
||||
this is a test statistic for a null hypothesis of equal variances given
|
||||
bivariate normality (Pitman 1939; also see Snedecor and Cochran 1989, 192-193).
|
||||
Alternatively, it is an exploratory diagnostic.
|
||||
A value near zero implies concordance.
|
||||
|
||||
{p 8 8 2}
|
||||
2. An F test of equality of means and variances. Note that this too
|
||||
assumes bivariate normality (Bradley and Blackwood 1989).
|
||||
See also Hsu (1940) and Reynolds and Gregoire (1991).
|
||||
Nonsignificance implies concordance.
|
||||
|
||||
{p 4 4 2}
|
||||
The user provides the pairs of measurements for a single property as
|
||||
observations in variables {it:vary} and {it:varx}. Frequency weights may be
|
||||
specified and used. Missing values (if any) are deleted in a casewise manner.
|
||||
|
||||
{p 4 4 2}
|
||||
Various associated graphs may be obtained through options.
|
||||
See below for explanations of the options {cmd:ccc}, {cmd:loa}, and
|
||||
{cmd:qnormd}.
|
||||
|
||||
|
||||
{title:Options}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:by(}{it:byvar}{cmd:)} produces separate results for groups of
|
||||
observations defined by {it:byvar}.
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:summary} requests summary statistics.
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:level(}{it:#}{cmd:)} sets the confidence level % for the CI; default is
|
||||
{cmd:c(level)}.
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:ccc} requests a graphical display of the data and the reduced major axis
|
||||
of the data. The reduced major axis or SD line goes through the intersection of
|
||||
the means and has slope given by the sign of Pearson's r and the ratio of the
|
||||
standard deviations. The SD line serves as a summary of the center of the data.
|
||||
|
||||
{p 8 8 2}
|
||||
{cmd:ccc()} suboption {cmd:noref} suppresses the reference line of
|
||||
perfect concordance, y=x.
|
||||
|
||||
{p 8 8 2}
|
||||
{cmd:ccc()} may also be specified with other options, which should
|
||||
be options of {helpb scatter}. For example, the scheme may be changed by a
|
||||
call such as {cmd:ccc(scheme(lean1)}.
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:loa} requests a graphical display of the loa, the mean difference, and
|
||||
the data presented as paired differences plotted against pair-wise means.
|
||||
|
||||
{p 8 8 2}
|
||||
{cmd:loa()} suboption {cmd:noref} suppresses the reference line of perfect
|
||||
average agreement, y=0.
|
||||
|
||||
{p 8 8 2}
|
||||
{cmd:loa()} suboption {cmd:regline} adds a regression line to the loa plot
|
||||
fitting the paired differences to the pair-wise means.
|
||||
|
||||
{p 8 8 2}
|
||||
{cmd:loa()} may also be specified with other options, which should normally
|
||||
be options of {helpb scatter}. For example,
|
||||
the reference line of perfect average agreement was generated as
|
||||
{cmd:loa(yline(0, lstyle(refline)) yscale(range(0)) ylabel(0, add))}.
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:qnormd} requests a normal plot of differences.
|
||||
|
||||
{p 8 8 2}
|
||||
{cmd:qnormd()} may also be specified with options, which should
|
||||
be options of {helpb scatter}. For example,
|
||||
{cmd:qnormd(title(Normal plot of differences))} adds a title to the graph.
|
||||
|
||||
|
||||
{title:Comments}
|
||||
|
||||
{p 4 4 2}
|
||||
Lin (2000) reported typographical errors in his original 1989 paper
|
||||
that affected calculation of the standard error of rho_c. These corrections
|
||||
were included in {cmd:concord} in version 2.2.7 (January 2002) when the erratum
|
||||
was brought to the attention of the program authors by Dr. Benjamin Littenberg.
|
||||
We thank Dr. Littenberg.
|
||||
|
||||
{p 4 4 2}Kevan Polkinghorne pointed out a problem with {cmd:loa()}.
|
||||
Mark Marshall pointed out a problem with {cmd:by()} under Stata 9.
|
||||
|
||||
{p 4 4 2}
|
||||
Dunn (2004) contains a bibliography on related work.
|
||||
Cox (2004) discusses other graphical approaches to this and related
|
||||
problems. Cox (2006) discusses concordance correlation and
|
||||
other numerical and graphical methods for assessing agreement
|
||||
with various scientific examples.
|
||||
|
||||
|
||||
{title:Saved results}
|
||||
|
||||
{p 4 4 2}
|
||||
The following items are returned in {cmd:r()}, if the {cmd:by()} option was
|
||||
not used:
|
||||
|
||||
{p 8 18}{cmd:r(N)}{space 9}number of observations compared{p_end}
|
||||
{p 8 18}{cmd:r(rho_c)}{space 5}concordance correlation coefficient rho_c{p_end}
|
||||
{p 8 18}{cmd:r(se_rho_c)}{space 2}standard error of rho_c{p_end}
|
||||
{p 8 18}{cmd:r(asym_ll)}{space 3}lower CI limit (asymptotic){p_end}
|
||||
{p 8 18}{cmd:r(asym_ul)}{space 3}upper CI limit (asymptotic){p_end}
|
||||
{p 8 18}{cmd:r(z_tr_ll)}{space 3}lower CI limit (z-transform){p_end}
|
||||
{p 8 18}{cmd:r(z_tr_ul)}{space 3}upper CI limit (z-transform){p_end}
|
||||
{p 8 18}{cmd:r(C_b)}{space 7}bias-correction factor C_b{p_end}
|
||||
{p 8 18}{cmd:r(diff)}{space 6}mean difference{p_end}
|
||||
{p 8 18}{cmd:r(sd_diff)}{space 3}standard deviation of mean difference{p_end}
|
||||
{p 8 18}{cmd:r(LOA_ll)}{space 4}lower loa CI limit{p_end}
|
||||
{p 8 18}{cmd:r(LOA_ul)}{space 4}upper loa CI limit{p_end}
|
||||
{p 8 18}{cmd:r(rdm)}{space 7}correlation between difference and mean{p_end}
|
||||
{p 8 18}{cmd:r(Fdm)}{space 7}F from Bradley-Blackwood test{p_end}
|
||||
|
||||
|
||||
{title:Examples}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:. concord rater1 rater2}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:. concord rater1 rater2 [fw=freq]}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:. concord rater1 rater2, summary ccc}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:. concord rater1 rater2, summary ccc(noref)}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:. concord rater1 rater2, level(90) by(grp)}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:. concord rater1 rater2, loa(regline noref)}
|
||||
|
||||
{p 4 8 2}
|
||||
{cmd:. concord rater1 rater2, qnormd(title(Normal plot))}
|
||||
|
||||
|
||||
{title:Authors}
|
||||
|
||||
{p 4 4 2}
|
||||
Thomas J. Steichen, Winston-Salem, NC, USA, steichen@triad.rr.com
|
||||
|
||||
{p 4 4 2}
|
||||
Nicholas J. Cox, Durham University, UK, n.j.cox@durham.ac.uk
|
||||
|
||||
|
||||
{title:References}
|
||||
|
||||
{p 4 8 2}
|
||||
Bland, J. M., and D. G. Altman. 1986. Statistical methods for assessing
|
||||
agreement between two methods of clinical measurement. {it:Lancet} I:
|
||||
307{c -}310.
|
||||
|
||||
{p 4 8 2}
|
||||
Bradley, E. L., and L. G. Blackwood. 1989. Comparing paired data:
|
||||
a simultaneous test for means and variances. {it:American Statistician}
|
||||
43: 234{c -}235.
|
||||
|
||||
{p 4 8 2}
|
||||
Cox, N. J. 2004. Graphing agreement and disagreement.
|
||||
{it:Stata Journal} 4: 329{c -}349.
|
||||
|
||||
{p 4 8 2}
|
||||
------. 2006.
|
||||
Assessing agreement of measurements and predictions in geomorphology.
|
||||
{it:Geomorphology} 76: 332{c -}346.
|
||||
|
||||
{p 4 8 2}
|
||||
Dunn, G. 2004.
|
||||
{it: Statistical Evaluation of Measurement Errors: Design and Analysis of Reliability Studies.}
|
||||
London: Arnold.
|
||||
|
||||
{p 4 8 2}
|
||||
Hsu, C. T. 1940. On samples from a normal bivariate population.
|
||||
{it:Annals of Mathematical Statistics} 11: 410{c -}426.
|
||||
|
||||
{p 4 8 2}
|
||||
Krippendorff, K. 1970. Bivariate agreement coefficients for reliability of data.
|
||||
In Borgatta, E.F. and G.W. Bohrnstedt (eds)
|
||||
{it:Sociological Methodology}. San Francisco: Jossey-Bass, 139{c -}150.
|
||||
[a.k.a. {it:Sociological Methodology} 2: 139{c -}150]
|
||||
|
||||
{p 4 8 2}
|
||||
Lin, L. I-K. 1989. A concordance correlation coefficient to evaluate
|
||||
reproducibility. {it:Biometrics} 45: 255{c -}268.
|
||||
|
||||
{p 4 8 2}
|
||||
------. 2000. A note on the concordance correlation coefficient.
|
||||
{it:Biometrics} 56: 324{c -}325.
|
||||
|
||||
{p 4 8 2}
|
||||
Pitman, E. J. G. 1939. A note on normal correlation.
|
||||
{it:Biometrika} 31: 9{c -}12.
|
||||
|
||||
{p 4 8 2}
|
||||
Reynolds, M., and T. G. Gregoire. 1991. Comment on Bradley and Blackwood.
|
||||
{it:American Statistician} 45: 163{c -}164.
|
||||
|
||||
{p 4 8 2}
|
||||
Snedecor, G. W., and W. G. Cochran. 1989. {it:Statistical Methods.}
|
||||
Ames, IA: Iowa State University Press.
|
||||
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{p 4 13 2}
|
||||
STB: STB-43 sg84; STB-45 sg84.1; STB-54 sg84.2; STB-58 sg84.3
|
||||
|
||||
{psee}
|
||||
SJ:{space 3}SJ2-2 st0015; SJ4-4 st0015_1; SJ5-3: st0015_2; SJ6-2: st0015_3
|
||||
{p_end}
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,503 +0,0 @@
|
||||
*! Mata definitions for confa package; 3 March 2009; v.2.0
|
||||
|
||||
set matalnum on
|
||||
|
||||
mata:
|
||||
|
||||
mata set matastrict on
|
||||
mata clear
|
||||
|
||||
//////////////////////////////////////////////
|
||||
// needed by confa.ado
|
||||
|
||||
real CONFA_NF( string input ) {
|
||||
real scalar nopenpar, nclosepar, ncolon;
|
||||
// opening and closing parentheses
|
||||
|
||||
nopenpar = length(tokens(input, ")" ))
|
||||
nclosepar = length(tokens(input, "(" ))
|
||||
// n*par will be 2*nf
|
||||
ncolon = length(tokens(input, ":" ))
|
||||
// ncolon will be 2*nf + 1
|
||||
|
||||
if ( (nopenpar == nclosepar) & (nopenpar == ncolon-1 ) ) {
|
||||
if (mod(nopenpar,2) == 0) {
|
||||
return( nopenpar/2 )
|
||||
}
|
||||
}
|
||||
|
||||
// if everything was OK, should've exited by now
|
||||
// if something's wrong, return zero
|
||||
return(0)
|
||||
}
|
||||
|
||||
matrix CONFA_StrucToSigma(real vector parms) {
|
||||
real scalar CONFA_loglevel, nobsvar, nfactors, eqno;
|
||||
real matrix Lambda, Phi, Theta, Sigma, CONFA_Struc;
|
||||
|
||||
// loglevel
|
||||
CONFA_loglevel = strtoreal( st_global("CONFA_loglevel"))
|
||||
|
||||
CONFA_Struc = st_matrix("CONFA_Struc")
|
||||
if (CONFA_loglevel>4) CONFA_Struc
|
||||
|
||||
if (CONFA_loglevel>4) {
|
||||
printf("{txt}Current parameter values:\n")
|
||||
parms
|
||||
}
|
||||
|
||||
// the length should coinicde with the # pars from CONFA_Struc
|
||||
if ( cols(parms) ~= rows(CONFA_Struc) ) {
|
||||
// something's wrong, let's just drop out with an empty matrix
|
||||
if (CONFA_loglevel>4) {
|
||||
printf("{txt}Expected parameters: {res}%3.0f{txt}; received parameters: {res}%3.0f\n",
|
||||
rows(CONFA_Struc),cols(parms))
|
||||
}
|
||||
return(J(0,0,0))
|
||||
}
|
||||
|
||||
// # observed variables: max entry in the number of means
|
||||
nobsvar = colmax( select(CONFA_Struc[,3], !(CONFA_Struc[,1]-J(rows(CONFA_Struc),1,1)) ) )
|
||||
if (CONFA_loglevel>4) printf("{txt}No. of observed variables: {res}%3.0f\n",nobsvar)
|
||||
|
||||
// # observed factors: max entry in the phi indices
|
||||
nfactors = colmax( select(CONFA_Struc[,3], !(CONFA_Struc[,1]-J(rows(CONFA_Struc),1,3)) ) )
|
||||
if (CONFA_loglevel>4) printf("{txt}No. of latent factors: {res}%3.0f\n",nfactors)
|
||||
|
||||
// set up the matrices
|
||||
Lambda = J(nobsvar,nfactors,0)
|
||||
Phi = J(nfactors,nfactors,0)
|
||||
Theta = J(nobsvar,nobsvar,0)
|
||||
|
||||
// fill the stuff in
|
||||
for(eqno=nobsvar+1;eqno<=rows(CONFA_Struc);eqno++) {
|
||||
if (CONFA_Struc[eqno,1] == 2) {
|
||||
// a lambda-type entry
|
||||
Lambda[ CONFA_Struc[eqno,3], CONFA_Struc[eqno,4] ] = parms[eqno]
|
||||
}
|
||||
if (CONFA_Struc[eqno,1] == 3) {
|
||||
// a phi-type entry
|
||||
Phi[ CONFA_Struc[eqno,3], CONFA_Struc[eqno,4] ] = parms[eqno]
|
||||
Phi[ CONFA_Struc[eqno,4], CONFA_Struc[eqno,3] ] = parms[eqno]
|
||||
}
|
||||
if (CONFA_Struc[eqno,1] == 4) {
|
||||
// a theta-type entry
|
||||
Theta[ CONFA_Struc[eqno,3], CONFA_Struc[eqno,3] ] = parms[eqno]
|
||||
}
|
||||
if (CONFA_Struc[eqno,1] == 5) {
|
||||
// a theta-type correlated errors entry
|
||||
Theta[ CONFA_Struc[eqno,3], CONFA_Struc[eqno, 4] ] = parms[eqno]
|
||||
Theta[ CONFA_Struc[eqno,4], CONFA_Struc[eqno, 3] ] = parms[eqno]
|
||||
}
|
||||
}
|
||||
if (CONFA_loglevel > 4) {
|
||||
printf("{txt}Loadings:\n")
|
||||
Lambda
|
||||
printf("{txt}Factor covariances:\n")
|
||||
Phi
|
||||
printf("{txt}Residual variances:\n")
|
||||
Theta
|
||||
}
|
||||
Sigma = Lambda*Phi*Lambda' + Theta
|
||||
if (CONFA_loglevel > 4) {
|
||||
printf("{txt}Implied moments:\n")
|
||||
Sigma
|
||||
}
|
||||
|
||||
if (CONFA_loglevel == -1) {
|
||||
// post matrices to Stata
|
||||
st_matrix("CONFA_Lambda",Lambda)
|
||||
st_matrix("CONFA_Phi",Phi)
|
||||
st_matrix("CONFA_Theta",Theta)
|
||||
st_matrix("CONFA_Sigma",Sigma)
|
||||
}
|
||||
|
||||
// done with model structure, compute and return implied matrix
|
||||
return( Sigma )
|
||||
}
|
||||
|
||||
// vech covariance matrix, for Satorra-Bentler
|
||||
void SBvechZZtoB(string dlist, string blist) {
|
||||
real matrix data, moments, B;
|
||||
real scalar i;
|
||||
|
||||
// view the deviation variables
|
||||
st_view(data=.,.,tokens(dlist))
|
||||
// view the moment variables
|
||||
// blist=st_local("blist")
|
||||
st_view(moments=.,.,tokens(blist))
|
||||
// vectorize!
|
||||
for(i=1; i<=rows(data); i++) {
|
||||
B = data[i,.]'*data[i,.]
|
||||
moments[i,.] = vech(B)'
|
||||
}
|
||||
}
|
||||
|
||||
// duplication matrix, for Satorra-Bentler
|
||||
void Dupl(scalar p, string Dname) {
|
||||
real scalar pstar, k;
|
||||
real matrix Ipstar, D;
|
||||
|
||||
pstar = p*(p+1)/2
|
||||
Ipstar = I(pstar)
|
||||
D = J(p*p,0,.)
|
||||
for(k=1;k<=pstar;k++) {
|
||||
D = (D, vec(invvech(Ipstar[.,k])))
|
||||
}
|
||||
st_matrix(Dname,D)
|
||||
}
|
||||
|
||||
// Satorra-Bentler Delta matrix
|
||||
// Delta = \frac \partial{\partial \theta} vech \Sigma(\theta)
|
||||
void SBStrucToDelta(string DeltaName) {
|
||||
real scalar CONFA_loglevel, p, t, varno, facno, i, j, k, fac1, fac2, k1, k2;
|
||||
// log level, # obs vars, # parameters, current var, current factor, cycle indices, temp indices
|
||||
real matrix Lambda, Phi, Theta, Sigma, CONFA_Struc, Delta, DeltaRow;
|
||||
// must be self-explanatory
|
||||
real matrix U, E;
|
||||
// identity matrices of the size #factors and #obs vars
|
||||
|
||||
// loglevel
|
||||
|
||||
CONFA_loglevel = strtoreal( st_global("CONFA_loglevel"))
|
||||
|
||||
// need the CONFA matrices
|
||||
CONFA_Struc = st_matrix("CONFA_Struc")
|
||||
Sigma = st_matrix("CONFA_Sigma")
|
||||
Lambda = st_matrix("CONFA_Lambda")
|
||||
Phi = st_matrix("CONFA_Phi")
|
||||
// Theta = st_matrix("CONFA_Theta")
|
||||
|
||||
if (CONFA_loglevel>4) CONFA_Struc
|
||||
|
||||
// # parameters in the model
|
||||
t = rows(CONFA_Struc)
|
||||
|
||||
// cols(Delta) = t = # pars
|
||||
// rows(Delta) = pstar = p*(p+1)/2 = length( vech( Sigma ) )
|
||||
// but that should be accumulated one by one...
|
||||
Delta = J(0,t,.)
|
||||
|
||||
// sources of u and e vectors
|
||||
p = rows( Sigma )
|
||||
U = I( p )
|
||||
E = I( rows(Phi) )
|
||||
|
||||
for(i=1;i<=p;i++) {
|
||||
for(j=i;j<=p;j++) {
|
||||
if (CONFA_loglevel > 4) printf("{txt}Working with pair ({res}%2.0f{txt},{res}%2.0f{txt})\n",i,j)
|
||||
DeltaRow = J(1,t,0)
|
||||
// parse Struc matrix and see how each parameter affects Cov(X_i,X_j)
|
||||
for(k=1;k<=t;k++) {
|
||||
if (CONFA_Struc[k,1] == 1) {
|
||||
// a mean-type entry
|
||||
// for the moment, assume it does not affect anything
|
||||
}
|
||||
if (CONFA_Struc[k,1] == 2) {
|
||||
// a lambda-type entry
|
||||
// CONFA_Struc[k,.] = (2, equation #, variable #, factor #)
|
||||
varno = CONFA_Struc[k,3]
|
||||
facno = CONFA_Struc[k,4]
|
||||
DeltaRow[1,k] = U[i,.] * U[.,varno] * E[facno,.] * Phi * Lambda' * U[.,j] +
|
||||
U[i,.] * Lambda * Phi * E[.,facno] * U[varno,.] * U[.,j]
|
||||
}
|
||||
if (CONFA_Struc[k,1] == 3) {
|
||||
// a phi-type entry
|
||||
// CONFA_Struc[k,.] = (3, equation #, `factor`kk'', `factor`k'')
|
||||
fac1 = CONFA_Struc[k,3]
|
||||
fac2 = CONFA_Struc[k,4]
|
||||
DeltaRow[1,k] = U[i,.] * Lambda * E[.,fac1] * E[fac2,.] * Lambda' * U[.,j]
|
||||
}
|
||||
if (CONFA_Struc[k,1] == 4) {
|
||||
// a theta-type entry
|
||||
// CONFA_Struc[k,.] = (4, equation #, variable #, 0)
|
||||
varno = CONFA_Struc[k,3]
|
||||
DeltaRow[1,k] = (i==j) & (i==varno)
|
||||
}
|
||||
if (CONFA_Struc[k,1] == 5) {
|
||||
// a theta_{jk}-type entry
|
||||
// CONFA_Struc[k,.] = (5, equation #, variable k1, variable k2)
|
||||
k1 = CONFA_Struc[k,3]
|
||||
k2 = CONFA_Struc[k,4]
|
||||
DeltaRow[1,k] = ((i==k1) & (j==k2) ) | ((i==k2) & (j==k1))
|
||||
}
|
||||
}
|
||||
Delta = Delta \ DeltaRow
|
||||
}
|
||||
}
|
||||
|
||||
st_matrix(DeltaName,Delta)
|
||||
}
|
||||
|
||||
|
||||
///////////////////////////////////////////
|
||||
// needed by confa_p.ado
|
||||
|
||||
void CONFA_P_EB(string Fnames, string ObsVarNames, string ToUseName) {
|
||||
real matrix ff, xx;
|
||||
// views
|
||||
real matrix bb, Sigma, Lambda, Theta, Phi;
|
||||
// substantive matrices
|
||||
real scalar p
|
||||
|
||||
// view on the newly generated factors
|
||||
st_view(ff=.,.,tokens(Fnames),ToUseName)
|
||||
|
||||
// view on the observed variables
|
||||
st_view(xx=.,.,tokens(ObsVarNames),ToUseName)
|
||||
|
||||
// get the estimated matrices
|
||||
bb = st_matrix("e(b)")
|
||||
Sigma = st_matrix("e(Sigma)")
|
||||
Theta = st_matrix("e(Theta)")
|
||||
Lambda = st_matrix("e(Lambda)")
|
||||
Phi = st_matrix("e(Phi)")
|
||||
|
||||
// # observed vars
|
||||
p = rows(Sigma)
|
||||
|
||||
// prediction
|
||||
ff[,] = (xx-J(rows(xx),1,1)*bb[1..p]) * invsym(Sigma) * Lambda * Phi
|
||||
}
|
||||
|
||||
void CONFA_P_MLE(string Fnames, string ObsVarNames, string ToUseName) {
|
||||
real matrix ff, xx;
|
||||
// views
|
||||
real matrix bb, Sigma, Lambda, Theta, Phi, ThetaInv;
|
||||
// substantive matrices
|
||||
real scalar p
|
||||
|
||||
// view on the newly generated factors
|
||||
st_view(ff=.,.,tokens(Fnames),ToUseName)
|
||||
|
||||
// view on the observed variables
|
||||
st_view(xx=.,.,tokens(ObsVarNames),ToUseName)
|
||||
|
||||
// get the estimated matrices
|
||||
bb = st_matrix("e(b)")
|
||||
Sigma = st_matrix("e(Sigma)")
|
||||
Theta = st_matrix("e(Theta)")
|
||||
Lambda = st_matrix("e(Lambda)")
|
||||
Phi = st_matrix("e(Phi)")
|
||||
|
||||
// # observed vars
|
||||
p = rows(Sigma)
|
||||
|
||||
// Theta is the vector of diagonal elements,
|
||||
// so the inverse is easy!
|
||||
ThetaInv = diag( 1:/Theta )
|
||||
|
||||
// prediction
|
||||
ff[,] = (xx-J(rows(xx),1,1)*bb[1..p]) * ThetaInv * Lambda * invsym(Lambda' * ThetaInv * Lambda)
|
||||
}
|
||||
|
||||
//////////////////////////////////
|
||||
// needed by confa_lf.ado
|
||||
|
||||
void CONFA_NormalLKHDr( string ParsName, string lnfname) {
|
||||
// ParsName are the parameters
|
||||
// lnfname is the name of the likelihood variable
|
||||
// the observed variables are in $CONFA_obsvar
|
||||
|
||||
real scalar CONFA_loglevel, nobsvar, ldetWS, i;
|
||||
// log level, # obs vars, log determinant, cycle index
|
||||
real matrix Sigma, means, SS, InvWorkSigma;
|
||||
// intermediate computations
|
||||
string scalar obsvar, touse;
|
||||
// list of observed variables
|
||||
real matrix data, lnl, parms;
|
||||
// views
|
||||
|
||||
CONFA_loglevel = strtoreal( st_global("CONFA_loglevel"))
|
||||
|
||||
obsvar = st_global("CONFA_obsvar")
|
||||
nobsvar = length(tokens(obsvar))
|
||||
|
||||
touse = st_global("CONFA_touse")
|
||||
|
||||
st_view(data=., ., tokens(obsvar), touse )
|
||||
st_view(lnl=., ., tokens(lnfname), touse)
|
||||
st_view(parms=., ., tokens(ParsName), touse)
|
||||
|
||||
// using the set up where the means are the first nobsvar entries of the parameter vector,
|
||||
means = parms[1,1..nobsvar]
|
||||
|
||||
Sigma = CONFA_StrucToSigma(parms[1,.])
|
||||
|
||||
if (CONFA_loglevel > 2) {
|
||||
parms[1,.]
|
||||
means
|
||||
Sigma
|
||||
}
|
||||
|
||||
// do some equilibration??
|
||||
|
||||
SS = cholesky(Sigma)
|
||||
InvWorkSigma = solvelower(SS,I(rows(SS)))
|
||||
InvWorkSigma = solveupper(SS',InvWorkSigma)
|
||||
ldetWS = 2*ln(dettriangular(SS))
|
||||
|
||||
for( i=1; i<=rows(data); i++ ) {
|
||||
lnl[i,1] = -.5*(data[i,.]-means)*InvWorkSigma*(data[i,.]-means)' - .5*ldetWS - .5*nobsvar*ln(2*pi())
|
||||
}
|
||||
|
||||
if (CONFA_loglevel>2) {
|
||||
sum(lnl)
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
// normal likelihood with missing data
|
||||
void CONFA_NormalLKHDrMiss( string ParsName, string lnfname) {
|
||||
// ParsName are the parameters
|
||||
// lnfname is the name of the likelihood variable
|
||||
// the observed variables are in $CONFA_obsvar
|
||||
|
||||
|
||||
real scalar CONFA_loglevel, nobsvar, thisldetWS, i, j;
|
||||
// log level, # obs vars, log determinant, cycle index
|
||||
real matrix Sigma, means, thisSigma, thisSS, thisInvSigma, thispattern, parms;
|
||||
// intermediate computations
|
||||
string scalar obsvar, misspat, touse;
|
||||
// list of observed variables; the names of the missing patterns and touse tempvars
|
||||
real matrix data, lnl, parmview, pattern, mdata, mlnl, info;
|
||||
// views
|
||||
|
||||
CONFA_loglevel = strtoreal( st_global("CONFA_loglevel"))
|
||||
|
||||
obsvar = st_global("CONFA_obsvar")
|
||||
nobsvar = length(tokens(obsvar))
|
||||
|
||||
misspat = st_global("CONFA_miss")
|
||||
touse = st_global("CONFA_touse")
|
||||
|
||||
st_view(pattern=., ., misspat, touse )
|
||||
st_view(data=., ., tokens(obsvar), touse )
|
||||
st_view(lnl=., ., lnfname, touse )
|
||||
|
||||
// STILL USING THE FIRST OBSERVATIONS TO GET THE PARAMETERS!!!
|
||||
st_view(parmview=., ., tokens(ParsName), touse )
|
||||
parms = parmview[1,1..cols(parmview)]
|
||||
|
||||
if (CONFA_loglevel>2) {
|
||||
obsvar
|
||||
parms
|
||||
}
|
||||
|
||||
// using the set up where the means are the first nobsvar entries of the parameter vector,
|
||||
means = parms[1..nobsvar]
|
||||
|
||||
Sigma = CONFA_StrucToSigma(parms)
|
||||
|
||||
// utilize an existing set up of the missing data patterns
|
||||
// data assumed to be sorted by the patterns of missing data
|
||||
|
||||
info = panelsetup( pattern, 1 )
|
||||
|
||||
for (i=1; i<=rows(info); i++) {
|
||||
panelsubview(mdata=., data, i, info)
|
||||
panelsubview(mlnl=., lnl, i, info)
|
||||
// mdata should contain the portion of the data with the same missing data pattern
|
||||
// mlnl will be conforming to mdata
|
||||
|
||||
// OK, now need to figure out that pattern
|
||||
thispattern = J(1, cols(data), 1) - colmissing( mdata[1,] )
|
||||
if (CONFA_loglevel > 2) {
|
||||
printf("{txt}Pattern #{res}%5.0f{txt} :", i)
|
||||
thispattern
|
||||
};
|
||||
|
||||
// modify the matrices
|
||||
|
||||
thisSigma = select( select( Sigma, thispattern), thispattern' )
|
||||
thisSS = cholesky(thisSigma)
|
||||
thisInvSigma = solvelower(thisSS,I(rows(thisSS)))
|
||||
thisInvSigma = solveupper(thisSS',thisInvSigma)
|
||||
thisldetWS = 2*ln(dettriangular(thisSS))
|
||||
|
||||
if (CONFA_loglevel > 3) {
|
||||
thisSigma
|
||||
thisInvSigma
|
||||
};
|
||||
|
||||
for( j=1; j<=rows(mdata); j++ ) {
|
||||
// this is actually a single line broken by arithmetic operator signs
|
||||
// that's bad style but it works
|
||||
mlnl[j,1] = -.5*(select(data[j,.],thispattern)-select(means,thispattern)) *
|
||||
thisInvSigma *
|
||||
(select(data[j,.],thispattern)-select(means,thispattern))' -
|
||||
.5*thisldetWS - .5*sum(thispattern)*ln(2*pi())
|
||||
}
|
||||
|
||||
if (CONFA_loglevel>3) {
|
||||
mlnl
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
// Bollen-Stine bootstrap rotation
|
||||
void CONFA_BSrotate(
|
||||
string SigmaName, // the parameter matrix name
|
||||
string varnames // the variable names
|
||||
) {
|
||||
|
||||
// declarations
|
||||
real matrix data // views of the data
|
||||
real matrix Sigma, SS, S2, SS2 // the covariance matrices and temp matrices
|
||||
real matrix means // the means -- need modifications for weighted data!!!
|
||||
real scalar n // dimension, no. obs
|
||||
|
||||
// get the data in
|
||||
st_view(data=., ., tokens(varnames) )
|
||||
n=rows(data)
|
||||
|
||||
Sigma = st_matrix(SigmaName)
|
||||
|
||||
// probability weights!!!
|
||||
means = colsum(data)/n
|
||||
SS = (cross(data,data)-n*means'*means)/(n-1)
|
||||
|
||||
S2 = cholesky(Sigma)
|
||||
SS2 = cholesky(SS)
|
||||
SS2 = solveupper(SS2',I(rows(SS)))
|
||||
|
||||
data[,] = data*SS2*S2'
|
||||
|
||||
}
|
||||
|
||||
|
||||
// build a library
|
||||
mata mlib create lconfa, replace
|
||||
mata mlib add lconfa *()
|
||||
mata mlib index
|
||||
|
||||
end
|
||||
// of mata
|
||||
|
||||
exit
|
||||
|
||||
// don't need this:
|
||||
|
||||
string scalar CONFA_UL( string input ) {
|
||||
|
||||
string rowvector s;
|
||||
real scalar i,j,n;
|
||||
|
||||
// tokenize input into a string vector
|
||||
s = tokens( input )
|
||||
n = cols( s )
|
||||
for(i=1;i<=n;i++) {
|
||||
// as I go over the elements, compare to the previous ones
|
||||
for(j=1;j<i;j++) {
|
||||
if ( s[i] == s[j] ) {
|
||||
s[i] = ""
|
||||
continue
|
||||
}
|
||||
}
|
||||
}
|
||||
// assemble back into a string scalar
|
||||
return( stritrim(invtokens( s ) ) )
|
||||
}
|
||||
@ -1,299 +0,0 @@
|
||||
{smcl}
|
||||
{* *! version 1.2.10 15may2007}{...}
|
||||
{cmd:help confa} {right: ({browse "http://www.stata-journal.com/article.html?article=st0169":SJ9-3: st0169})}
|
||||
{hline}
|
||||
|
||||
{title:Title}
|
||||
|
||||
{p2colset 5 14 16 2}{...}
|
||||
{p2col :{hi:confa} {hline 2}}Confirmatory factor analysis{p_end}
|
||||
{p2colreset}{...}
|
||||
|
||||
|
||||
{title:Syntax}
|
||||
|
||||
{p 8 11 2}
|
||||
{cmd:confa}
|
||||
{it:factorspec} [{it:factorspec ...}]
|
||||
{ifin}
|
||||
{weight}
|
||||
[{cmd:,} {it:options}]
|
||||
|
||||
{pstd}
|
||||
{it:factorspec} is{p_end}
|
||||
|
||||
{p 8 27}
|
||||
{cmd:(}{it:factorname}{cmd::} {it:varlist}{cmd:)}{p_end}
|
||||
|
||||
{synoptset 28 tabbed}{...}
|
||||
{synopthdr}
|
||||
{synoptline}
|
||||
{syntab:Model}
|
||||
{synopt :{cmdab:corr:elated(}{it:{help confa##corr:corrspec}} [...]{cmd:)}}correlated measurement errors{p_end}
|
||||
{synopt :{cmd:unitvar(}{it:factorlist}|{cmd:_all}{cmd:)}}set variance of the factor(s) to 1{p_end}
|
||||
{synopt :{opt free}}do not impose any constraints by default; seldom used{p_end}
|
||||
{synopt :{opt constr:aint(numlist)}}user-supplied constraints;
|
||||
must be used with {cmd:free}{p_end}
|
||||
{synopt: {cmdab:miss:ing}}full-information maximum-likelihood
|
||||
estimation with missing data{p_end}
|
||||
{synopt: {cmdab:usen:ames}}alternative coefficient labeling{p_end}
|
||||
|
||||
{syntab:Variance estimation}
|
||||
{synopt :{opth vce(vcetype)}}{it:vcetype} may be
|
||||
{opt r:obust}, {opt cl:uster} {it:clustvar}, {cmd:oim}, {cmd:opg}, or
|
||||
{opt sb:entler}{p_end}
|
||||
|
||||
{syntab:Reporting}
|
||||
{synopt :{opt l:evel(#)}}set confidence level; default is {cmd:level(95)}{p_end}
|
||||
|
||||
{syntab:Other}
|
||||
{synopt :{opt svy}}respect survey settings{p_end}
|
||||
{synopt :{opth "from(confa##init_specs:init_specs)"}}control the starting values{p_end}
|
||||
{synopt :{opt loglevel(#)}}specify the details of output; programmers only{p_end}
|
||||
{synopt :{it:{help confa##maximize:ml_options}}}maximization options{p_end}
|
||||
{synoptline}
|
||||
{p2colreset}{...}
|
||||
|
||||
|
||||
{title:Description}
|
||||
|
||||
{pstd}{cmd:confa} fits single-level confirmatory factor analysis (CFA) models.
|
||||
In a CFA model, each of the variables is assumed to
|
||||
be an indicator of underlying unobserved factor(s) with a linear dependence
|
||||
between the factors and observed variables:
|
||||
|
||||
{center:{it:y_i} = {it:m_i} + {it:l_i1 f_1} + ... + {it:l_iK f_K} + {it:e_i}}
|
||||
|
||||
{pstd}where {it:y_i} is the {it:i}th variable
|
||||
in the {it:varlist}, {it:m_i} is its mean,
|
||||
{it:l_ik} are the latent variable loading(s),
|
||||
{it:f_k} are the {it:k}th latent factor(s)
|
||||
({it:k} = 1,...,{it:K}),
|
||||
and {it:e_i} is the measurement error.
|
||||
Thus the specification
|
||||
{cmd:(}{it:factorname}{cmd::} {it:varlist}{cmd:)}
|
||||
is interpreted as follows: the latent factor
|
||||
{it:f_k} is given {it:factorname}
|
||||
(for display purposes only);
|
||||
the variables specified in
|
||||
the {it:varlist} have their loadings, {it:l_ik}, estimated;
|
||||
and all other observed variables in the model
|
||||
have fixed loadings, {it:l_ik} = 0.
|
||||
|
||||
{pstd}The model is fitted by the maximum likelihood
|
||||
procedure; see {helpb ml}.
|
||||
|
||||
{pstd}As with all latent variable models, a number
|
||||
of identifying assumptions need to be made about
|
||||
the latent variables {it:f_k}. They are assumed
|
||||
to have mean zero, and their scales are determined
|
||||
by the first variable in the {it:varlist}
|
||||
(i.e., {it:l_1k} is set to equal 1 for all {it:k}).
|
||||
Alternatively, identification can be achieved by setting the
|
||||
variance of the latent variable to 1 (with option
|
||||
{cmd:unitvar()}). More sophisticated identification
|
||||
conditions can be achieved by specifying the
|
||||
{cmd:free} option and then providing the necessary
|
||||
constraints in the {cmd:constraint()} option.
|
||||
|
||||
|
||||
{title:Options}
|
||||
|
||||
{dlgtab:Model}
|
||||
|
||||
{marker corr}{...}
|
||||
{phang}{cmd:correlated(}{it:corrspec} [{it:corrspec} ...]{cmd:)}
|
||||
specifies the correlated measurement errors {it:e_i} and {it:e_j}.
|
||||
Here {it:corrspec} is of the form{p_end}
|
||||
{pmore} [{cmd:(}]{it:varname_k}{cmd::}{it:varname_j}[{cmd:)}]{p_end}
|
||||
{pmore}where {it:varname_k} and {it:varname_j} are some of
|
||||
the observed variables in the model; that is, they must appear in at
|
||||
least one
|
||||
{it:factorspec} statement. If there
|
||||
is only one correlation specified, the optional parentheses
|
||||
shown above may be omitted. There should be no space between the colon and
|
||||
{it:varname_j}.
|
||||
|
||||
{phang}{cmd:unitvar(}{it:factorlist}|{cmd:_all)} specifies the factors
|
||||
(from those named in {it:factorspec}) that will be identified by setting
|
||||
their variances to 1. The keyword {cmd:_all} can be used to specify that all
|
||||
the factors have their variances set to 1 (and hence the matrix Phi can be
|
||||
interpreted as a correlation matrix).
|
||||
|
||||
{phang}{cmd:free} frees up all the parameters in the model (making it
|
||||
underidentified). It is then the user's responsibility to provide
|
||||
identification constraints and adjust the degrees of freedom of the tests.
|
||||
This option is seldom used.
|
||||
|
||||
{phang}{cmd:constraint(}{it:numlist}{cmd:)} can be used to supply additional
|
||||
constraints. There are no checks implemented for
|
||||
redundant or conflicting constraints, so in some rare cases, the degrees of
|
||||
freedom may be incorrect. It might be wise to run the model with the
|
||||
{cmd:free} and {cmd:iterate(0)} options and then look at the names in the output of
|
||||
{cmd:matrix list e(b)} to find out the specific names of the parameters.
|
||||
|
||||
{phang}{cmd:missing} requests full-information maximum-likelihood estimation
|
||||
with missing data. By default, estimation proceeds by listwise deletion.
|
||||
|
||||
{phang}{cmd:usenames} requests that the parameters be labeled with the names
|
||||
of the variables and factors rather than with numeric values (indices of the
|
||||
corresponding matrices). It is a technical detail that does not affect the
|
||||
estimation procedure in any way, but it is helpful when working with several
|
||||
models simultaneously, tabulating the estimation results, and transferring the
|
||||
starting values between models.
|
||||
|
||||
{dlgtab:Variance estimation}
|
||||
|
||||
{phang}{cmd:vce(}{it:vcetype}{cmd:)} specifies different estimators of the
|
||||
variance-covariance matrix. Common estimators ({cmd:vce(oim)},
|
||||
observed information matrix, the default; {cmd:vce(robust)}, sandwich
|
||||
information matrix; {cmd:vce(cluster }{it:clustvar}{cmd:)}, clustered
|
||||
sandwich estimator with clustering on {it:clustvar}) are supported, along with
|
||||
their aliases (the {cmd:robust} and {cmd:cluster(}{it:clustvar}{cmd:)}
|
||||
options). See {manhelpi vce_option R}.
|
||||
|
||||
{pmore}An additional estimator specific to structural equation modeling is the Satorra-Bentler
|
||||
estimator (Satorra and Bentler 1994). It is requested by
|
||||
{cmd:vce(}{cmdab:sben:tler}{cmd:)} or {cmd:vce(}{cmdab:sat:orrabentler}{cmd:)}. When
|
||||
this option is specified, additional Satorra-Bentler scaled and adjusted
|
||||
goodness-of-fit statistics are computed and presented in the output.
|
||||
|
||||
{dlgtab:Reporting}
|
||||
|
||||
{phang}{cmd:level(}{it:#}{cmd:)} changes the confidence level for
|
||||
confidence-interval reporting. See
|
||||
{helpb estimation_options##level():[R] estimation options}.
|
||||
|
||||
{dlgtab:Other}
|
||||
|
||||
{phang}{cmd:svy} instructs {cmd:confa} to respect the complex
|
||||
survey design, if one is specified. See {manhelp svyset SVY}.
|
||||
|
||||
{marker init_specs}{...}
|
||||
{phang}{cmd:from(}{cmd:ones}|{cmd:2sls}|{cmd:ivreg}|{cmd:smart}|{it:ml_init_args}{cmd:)} provides the choice of starting values for the maximization
|
||||
procedure. The {cmd:ml} command's internal default is to set all parameters to zero,
|
||||
which leads to a noninvertible matrix, Sigma, and {cmd:ml} has to make many changes to those initial values to find anything feasible. Moreover, this
|
||||
initial search procedure sometimes leads to a domain where the likelihood is
|
||||
nonconcave, and optimization might fail there.
|
||||
|
||||
{phang2}{cmd:ones} sets all the parameters to values of one except
|
||||
for covariance parameters (off-diagonal values of the Phi and Theta matrices),
|
||||
which are set to 0.5. This might be a reasonable choice for data with
|
||||
variances of observed variables close to 1 and positive covariances (no
|
||||
inverted scales).
|
||||
|
||||
{phang2} {cmd:2sls} or {cmd:ivreg} requests that the initial parameters for the
|
||||
freely estimated loadings be set to the two-stage least-squares
|
||||
instrumental-variable estimates of Bollen (1996). This requires the model to
|
||||
be identified by scaling indicators (i.e., setting one of the loadings to 1)
|
||||
and to have at least three indicators for each latent variable. The instruments
|
||||
used are all other indicators of the same factor. No checks for their validity
|
||||
or search for other instruments is performed.
|
||||
|
||||
{phang2} {cmd:smart} provides an alternative set of starting values that
|
||||
is often reasonable (e.g., assuming that the reliability of observed
|
||||
variables is 0.5).
|
||||
|
||||
{pmore}Other specification of starting values, {it:ml_init_args}, should follow the format of
|
||||
{cmd:ml init}. Those typically include the list of starting values of the form
|
||||
{cmd:from(}{it:# #} ... {it:#}{cmd:, copy)} or a matrix of starting values
|
||||
{cmd:from(}{it:matname}{cmd:,} [{cmd:copy}|{cmd:skip}]{cmd:)}. See {manhelp ml R}.
|
||||
|
||||
{phang}{cmd:loglevel(}{it:#}{cmd:)} specifies the details of output about
|
||||
different stages of model setup and estimation,
|
||||
and is likely of interest only to programmers. Higher numbers
|
||||
imply more output.
|
||||
|
||||
{marker maximize}{...}
|
||||
{phang}For other options, see {helpb maximize}.
|
||||
|
||||
|
||||
{title:Remarks}
|
||||
|
||||
{pstd}{cmd:confa} relies on {cmd:listutil} for some parsing tasks. If
|
||||
{cmd:listutil} is not installed in your Stata, {cmd:confa} will try to install
|
||||
it from the SSC ({cmd:ssc install listutil}). If the installation is
|
||||
unsuccessful, {cmd:confa} will issue an error message and stop.
|
||||
|
||||
{pstd}In large models, {cmd:confa} may be restricted by Stata's
|
||||
{help limits:limit} of 244 characters in the string expression. You might
|
||||
want to {helpb rename} your variables and give them shorter names.
|
||||
|
||||
|
||||
{title:Examples}
|
||||
|
||||
{pstd}Holzinger-Swineford data{p_end}
|
||||
{phang2}{cmd:. use hs-cfa}
|
||||
|
||||
{pstd}Basic model with different starting values{p_end}
|
||||
{phang2}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(ones)}{p_end}
|
||||
{phang2}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(iv)}{p_end}
|
||||
{phang2}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(smart)}
|
||||
|
||||
{pstd}Robust and Satorra-Bentler standard errors{p_end}
|
||||
{phang2}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(iv) vce(sbentler)}{p_end}
|
||||
{phang2}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(iv) robust}
|
||||
|
||||
{pstd}Correlated measurement errors{p_end}
|
||||
{phang2}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(iv) corr( x7:x8 )}
|
||||
|
||||
{pstd}An alternative identification{p_end}
|
||||
{phang2}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(ones) unitvar(_all) corr(x7:x8)}
|
||||
|
||||
{pstd}Missing data{p_end}
|
||||
{phang2}{cmd:. forvalues k=1/9 {c -(}}{p_end}
|
||||
{phang2}{cmd:. gen y`k' = cond( uniform()<0.0`k', ., x`k')}{p_end}
|
||||
{phang2}{cmd:. {c )-}}{p_end}
|
||||
{phang2}{cmd:. confa (vis: y1 y2 y3) (text: y4 y5 y6) (math: y7 y8 y9), from(iv)}{p_end}
|
||||
{phang2}{cmd:. confa (vis: y1 y2 y3) (text: y4 y5 y6) (math: y7 y8 y9), from(iv) missing difficult}{p_end}
|
||||
|
||||
|
||||
{title:Saved results}
|
||||
|
||||
{pstd}Aside from the standard {help estcom:estimation results}, {cmd:confa}
|
||||
also performs the overall goodness-of-fit test with results
|
||||
saved in {cmd:e(lr_u)}, {cmd:e(df_u)}, and {cmd:e(p_u)}
|
||||
for the test statistic, its goodness of fit, and the resulting
|
||||
p-value. A test versus the model with the independent data
|
||||
is provided with the {helpb ereturn} results with the {cmd:indep}
|
||||
suffix. Here, under the null hypothesis,
|
||||
the covariance matrix is assumed diagonal.
|
||||
|
||||
{pstd}When {cmd:sbentler} is specified, Satorra-Bentler standard errors are
|
||||
computed and posted as {cmd:e(V)}, with intermediate matrices saved in
|
||||
{cmd:e(SBU)}, {cmd:e(SBV)}, {cmd:e(SBGamma)}, and {cmd:e(SBDelta)}. Also,
|
||||
several corrected overall fit test statistics is reported and saved: T scaled
|
||||
({cmd:ereturn} results with the {cmd:Tsc} suffix) and T adjusted ({cmd:ereturn}
|
||||
results with the {cmd:Tadj} suffix). Scalars {cmd:e(SBc)} and {cmd:e(SBd)} are
|
||||
the scaling constants, with the latter also being the approximate degrees of
|
||||
freedom of the chi-squared test from Satorra and Bentler (1994), and T double
|
||||
bar from Yuan and Bentler (1997) (with the {cmd:T2} suffix).
|
||||
|
||||
|
||||
{title:References}
|
||||
|
||||
{phang}Bollen, K. A. 1996. An alternative two stage least squares (2SLS)
|
||||
estimator for latent variable equations. {it:Psychometrika} 61: 109-121.
|
||||
|
||||
{phang}Satorra, A., and P. M. Bentler. 1994. Corrections to test statistics and standard errors in covariance structure analysis. In {it:Latent Variables Analysis}, ed. A. von Eye and C. C. Clogg, 399-419. Thousand Oaks, CA: Sage.
|
||||
|
||||
{phang} Yuan, K.-H., and P. M. Bentler. 1997. Mean and covariance structure analysis: Theoretical and practical improvements.
|
||||
{it:Journal of the American Statistical Association} 92: 767-774.
|
||||
|
||||
|
||||
{title:Author}
|
||||
|
||||
{pstd}Stanislav Kolenikov{p_end}
|
||||
{pstd}Department of Statistics{p_end}
|
||||
{pstd}University of Missouri{p_end}
|
||||
{pstd}Columbia, MO{p_end}
|
||||
{pstd}kolenikovs@missouri.edu{p_end}
|
||||
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{psee}
|
||||
Article: {it:Stata Journal}, volume 9, number 3: {browse "http://www.stata-journal.com/article.html?article=st0169":st0169}
|
||||
|
||||
{psee}Online: {helpb factor}, {helpb bollenstine}, {helpb confa_estat:confa postestimation} (if installed)
|
||||
{p_end}
|
||||
@ -1,222 +0,0 @@
|
||||
*! version 2.0.2 08 Sep 2009; part of confa suite
|
||||
program confa_estat, rclass
|
||||
version 10
|
||||
|
||||
if "`e(cmd)'" != "confa" {
|
||||
error 301
|
||||
}
|
||||
|
||||
gettoken subcmd rest : 0, parse(" ,")
|
||||
local lsubcmd= length("`subcmd'")
|
||||
|
||||
if `"`subcmd'"' == substr("fitindices", 1, max(3, `lsubcmd')) {
|
||||
FitIndices `rest'
|
||||
}
|
||||
else if `"`subcmd'"' == substr("correlate", 1, max(4, `lsubcmd')) {
|
||||
Correlate `rest'
|
||||
}
|
||||
else if `"`subcmd'"' == substr("aic",1, max(3, `lsubcmd')) {
|
||||
FitIndices , aic
|
||||
}
|
||||
else if `"`subcmd'"' == substr("bic",1, max(3, `lsubcmd')) {
|
||||
FitIndices , bic
|
||||
}
|
||||
else if `"`subcmd'"' == substr("ic",1,max(2, `lsubcmd')) {
|
||||
FitIndices, aic bic
|
||||
}
|
||||
else if `"`subcmd'"' == substr("summarize", 1, max(2, `lsubcmd')) {
|
||||
Summarize `rest'
|
||||
}
|
||||
else if `"`subcmd'"' == "vce" {
|
||||
vce `rest'
|
||||
}
|
||||
else {
|
||||
di as err "`subcmd' not allowed"
|
||||
exit 198
|
||||
}
|
||||
|
||||
return add
|
||||
|
||||
end
|
||||
|
||||
program Summarize
|
||||
syntax , [noHEAder noWEIghts]
|
||||
if "`e(wexp)'" ~= "" & "`weights'"~="noweights" local wgt [iw `e(wexp)']
|
||||
sum `e(observed)' `wgt' if e(sample)
|
||||
end
|
||||
|
||||
program Correlate, rclass
|
||||
* correlation matrix of estimated factors
|
||||
|
||||
syntax, [level(passthru) bound]
|
||||
|
||||
di _n "{txt}Correlation equivalents of covariances"
|
||||
|
||||
if "`bound'" != "" local bound ci(atanh)
|
||||
|
||||
local q = rowsof( e(Phi) )
|
||||
if `q'>1 {
|
||||
* display the factor correlations
|
||||
|
||||
di as text "{hline 13}{c TT}{hline 64}"
|
||||
if "`e(vcetype)'" ~= "" {
|
||||
di as text " {c |} {center 15:`e(vcetype)'}"
|
||||
}
|
||||
di as text " {c |} Coef. Std. Err. z P>|z| [$S_level% Conf. Interval]"
|
||||
di as text "{hline 13}{c +}{hline 64}"
|
||||
|
||||
* parse the factor names
|
||||
local fnames : rownames e(Phi)
|
||||
* parse the unitvar list to be used in -inlist-
|
||||
local unitvarlist = `"""' + subinstr("`e(unitvar)'"," ",`"",""',.) + `"""'
|
||||
|
||||
_diparm __lab__ , label("Factors") eqlabel
|
||||
|
||||
forvalues i=1/`q' {
|
||||
local i1 = `i'+1
|
||||
forvalues j=`i1'/`q' {
|
||||
if inlist("`: word `i' of `fnames''", `unitvarlist') & inlist("`: word `j' of `fnames''", `unitvarlist') {
|
||||
* both factor variances are constrained at 1, display as is
|
||||
_diparm phi_`i'_`j' , prob label("`: word `i' of `fnames''-`: word `j' of `fnames''") `level' `bound'
|
||||
}
|
||||
else if inlist("`: word `i' of `fnames''", `unitvarlist') & !inlist("`: word `j' of `fnames''", `unitvarlist') {
|
||||
* `i' is restricted unit variance, `j' is not
|
||||
_diparm phi_`i'_`j' phi_`j'_`j', ///
|
||||
function( @1/sqrt(@2) ) d( 1/sqrt(@2) -0.5*@1/sqrt(@2*@2*@2) ) ///
|
||||
prob label("`: word `i' of `fnames''-`: word `j' of `fnames''") `level' `bound'
|
||||
}
|
||||
else if !inlist("`: word `i' of `fnames''", `unitvarlist') & inlist("`: word `j' of `fnames''", `unitvarlist') {
|
||||
* `j' is restricted unit variance, `i' is not
|
||||
_diparm phi_`i'_`j' phi_`i'_`i', ///
|
||||
function( @1/sqrt(@2) ) d( 1/sqrt(@2) -0.5*@1/sqrt(@2*@2*@2) ) ///
|
||||
prob label("`: word `i' of `fnames''-`: word `j' of `fnames''") `level' `bound'
|
||||
}
|
||||
else {
|
||||
* display correlation transform
|
||||
_diparm phi_`i'_`j' phi_`i'_`i' phi_`j'_`j', ///
|
||||
function( @1/sqrt(@2*@3) ) d( 1/sqrt(@2*@3) -0.5*@1/sqrt(@2*@2*@2*@3) -0.5*@1/sqrt(@2*@3*@3*@3) ) ///
|
||||
prob label("`: word `i' of `fnames''-`: word `j' of `fnames''") `level' `bound'
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
if "`e(correlated)'" ~= "" {
|
||||
|
||||
if `q' < 2 {
|
||||
* need to display the header
|
||||
|
||||
di as text "{hline 13}{c TT}{hline 64}"
|
||||
if "`e(vcetype)'" ~= "" {
|
||||
di as text " {c |} {center 15:`e(vcetype)'}"
|
||||
}
|
||||
di as text " {c |} Coef. Std. Err. z P>|z| [$S_level% Conf. Interval]"
|
||||
di as text "{hline 13}{c +}{hline 64}"
|
||||
}
|
||||
|
||||
* print out correlated measurement errors
|
||||
_diparm __lab__ , label("Errors") eqlabel
|
||||
local correlated `e(correlated)'
|
||||
local obsvar `e(observed)'
|
||||
while "`correlated'" != "" {
|
||||
gettoken corrpair correlated : correlated , match(m)
|
||||
gettoken corr1 corrpair : corrpair, parse(":")
|
||||
unab corr1 : `corr1'
|
||||
gettoken sc corr2 : corrpair, parse(":")
|
||||
unab corr2 : `corr2'
|
||||
|
||||
poslist `obsvar' \ `corr1', global(CFA_temp)
|
||||
local k1 = $CFA_temp
|
||||
poslist `obsvar' \ `corr2', global(CFA_temp)
|
||||
local k2 = $CFA_temp
|
||||
|
||||
_diparm theta_`k1'_`k2' theta_`k1' theta_`k2', ///
|
||||
function( @1/sqrt(@2*@3) ) d( 1/sqrt(@2*@3) -0.5*@1/sqrt(@2*@2*@2*@3) -0.5*@1/sqrt(@2*@3*@3*@3) ) ///
|
||||
prob label("`corr1'-`corr2'") `level' `bound'
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
else if `q'<2 {
|
||||
di as text _n "Nothing to display" _n
|
||||
}
|
||||
|
||||
di as text "{hline 13}{c BT}{hline 64}"
|
||||
|
||||
global CFA_temp
|
||||
|
||||
end
|
||||
|
||||
program FitIndices, rclass
|
||||
|
||||
syntax , [all tli rmsea rmsr aic bic]
|
||||
|
||||
di _n "{txt} Fit indices" _n
|
||||
return add
|
||||
|
||||
* all, by default
|
||||
if "`*'" == "" local all 1
|
||||
|
||||
* the fundamentals
|
||||
local p = `: word count `e(obsvar)''
|
||||
|
||||
if "`rmsea'`all'"~="" {
|
||||
return scalar RMSEA = sqrt( max( (e(lr_u)-e(df_u))/(e(N)-1), 0 )/e(df_u) )
|
||||
tempname ll lu
|
||||
scalar `ll' = cond(chi2(e(df_u),e(lr_u))>0.95,npnchi2(e(df_u),e(lr_u),0.95),0)
|
||||
scalar `lu' = cond(chi2(e(df_u),e(lr_u))>0.05,npnchi2(e(df_u),e(lr_u),0.05),0)
|
||||
return scalar RMSEA05 = sqrt( `ll'/( (e(N)-1)*e(df_u) ) )
|
||||
return scalar RMSEA95 = sqrt( `lu'/( (e(N)-1)*e(df_u) ) )
|
||||
di "{txt}RMSEA {col 8}= {res}" %6.4f return(RMSEA) _c
|
||||
di "{txt}, 90% CI{col 8}= ({res}" %6.4f return(RMSEA05) "{txt}, {res}" %6.4f return(RMSEA95) "{txt})"
|
||||
}
|
||||
|
||||
if "`rmsr'`all'"~="" {
|
||||
cap mat li e(S)
|
||||
if _rc {
|
||||
* no matrix posted
|
||||
return scalar RMSR = .
|
||||
}
|
||||
else {
|
||||
tempname res
|
||||
mata : st_numscalar("`res'",norm(vech(st_matrix("e(Sigma)") - st_matrix("e(S)"))) )
|
||||
return scalar RMSR = `res'/sqrt(e(pstar) )
|
||||
}
|
||||
di "{txt}RMSR {col 8}= {res}" %6.4f return(RMSR)
|
||||
}
|
||||
|
||||
if "`tli'`all'"~="" {
|
||||
return scalar TLI = (e(lr_indep)/e(df_indep)-e(lr_u)/e(df_u))/(e(lr_indep)/e(df_indep)-1)
|
||||
di "{txt}TLI {col 8}= {res}" %6.4f return(TLI)
|
||||
}
|
||||
|
||||
if "`cfi'`all'"~="" {
|
||||
return scalar CFI = 1 - max( e(lr_u)-e(df_u),0 )/max( e(lr_u)-e(df_u), e(lr_indep)-e(df_indep),0 )
|
||||
di "{txt}CFI {col 8}= {res}" %6.4f return(CFI)
|
||||
}
|
||||
|
||||
if "`aic'`all'"~="" {
|
||||
if "`e(wexp)'" == "" & "`e(vcetype)'"~="Robust" return scalar AIC = -2*e(ll) + 2*e(df_m)
|
||||
else return scalar AIC = .
|
||||
di "{txt}AIC {col 8}= {res}" %8.3f return(AIC)
|
||||
}
|
||||
|
||||
if "`bic'`all'"~="" {
|
||||
if "`e(wexp)'" == "" & "`e(vcetype)'"~="Robust" return scalar BIC = -2*e(ll) + e(df_m)*ln( e(N) )
|
||||
else return scalar BIC = .
|
||||
di "{txt}BIC {col 8}= {res}" %8.3f return(BIC)
|
||||
}
|
||||
|
||||
end
|
||||
|
||||
exit
|
||||
|
||||
Version history:
|
||||
1.0.0 9 Jan 2008 -- Correlate
|
||||
FitIndices
|
||||
1.0.1 12 Sep 2008 -- AIC, BIC
|
||||
1.0.2 Oct 2008 -- bound for Correlate CI
|
||||
correlated measurement errors
|
||||
CI for RMSEA
|
||||
@ -1,205 +0,0 @@
|
||||
{smcl}
|
||||
{* *! version 1.1.13 04jun2007}{...}
|
||||
{cmd:help confa postestimation}{right: ({browse "http://www.stata-journal.com/article.html?article=st0169":SJ9-3: st0169})}
|
||||
{hline}
|
||||
|
||||
{title:Title}
|
||||
|
||||
{p2colset 5 29 31 2}{...}
|
||||
{p2col :{hi:confa postestimation} {hline 2}}Postestimation tools for confa{p_end}
|
||||
{p2colreset}{...}
|
||||
|
||||
|
||||
{title:Description}
|
||||
|
||||
{pstd}The following commands are available after {helpb confa}:{p_end}
|
||||
|
||||
{synoptset 17}{...}
|
||||
{p2coldent :command}description{p_end}
|
||||
{synoptline}
|
||||
{synopt :{helpb confa_estat##fit:estat fitindices}}fit indices{p_end}
|
||||
{synopt :{helpb confa_estat##ic:estat aic}}AIC{p_end}
|
||||
{synopt :{helpb confa_estat##ic:estat bic}}BIC{p_end}
|
||||
{synopt :{helpb confa_estat##corr:estat correlate}}correlations of factors and measurement errors{p_end}
|
||||
{synopt :{helpb confa_estat##predict:predict}}factor scores{p_end}
|
||||
{synopt :{helpb bollenstine}}Bollen-Stine bootstrap{p_end}
|
||||
{synoptline}
|
||||
{p2colreset}{...}
|
||||
|
||||
|
||||
{marker fit}{...}
|
||||
{title:The estat fitindices command}
|
||||
|
||||
{title:Syntax}
|
||||
|
||||
{p 8 15 2}
|
||||
{cmd:estat} {cmdab:fit:indices}
|
||||
[{cmd:,} {it:options}]
|
||||
|
||||
{p2colset 9 27 29 2}{...}
|
||||
{p2col:{it:options}}fit index{p_end}
|
||||
{p2line}
|
||||
{p2col :{opt aic}}Akaike information criterion{p_end}
|
||||
{p2col :{opt bic}}Schwarz Bayesian information criterion{p_end}
|
||||
{p2col :{opt cfi}}comparative fit index{p_end}
|
||||
{p2col :{opt rmsea}}root mean squared error of approximation{p_end}
|
||||
{p2col :{opt rmsr}}root mean squared residual{p_end}
|
||||
{p2col :{opt tli}}Tucker-Lewis index{p_end}
|
||||
{p2col :{opt _all}}all the above indices, the default{p_end}
|
||||
{p2line}
|
||||
{p2colreset}{...}
|
||||
|
||||
{title:Description}
|
||||
|
||||
{pmore}{opt estat }{cmd:fitindices} computes, prints, and saves into
|
||||
{cmd:r()} results several traditional fit indices.
|
||||
|
||||
{title:Options}
|
||||
|
||||
{phang2}
|
||||
{opt aic} requests the Akaike information criterion (AIC).
|
||||
|
||||
{phang2}
|
||||
{opt bic} requests the Schwarz Bayesian information criterion (BIC).
|
||||
|
||||
{phang2}
|
||||
{opt cfi} requests the CFI (Bentler 1990b).
|
||||
|
||||
{phang2}
|
||||
{opt rmsea} requests the RMSEA (Browne and Cudeck 1993).
|
||||
|
||||
{phang2}
|
||||
{opt rmsr} requests the RMSR.
|
||||
|
||||
{phang2}
|
||||
{opt tli} requests the TLI (Tucker and Lewis 1973).
|
||||
|
||||
{phang2}
|
||||
{opt _all} requests all the above indices. This is the default
|
||||
behavior if no option is specified.
|
||||
|
||||
|
||||
{marker ic}{...}
|
||||
{title:The estat aic and estat bic commands}
|
||||
|
||||
{title:Syntax}
|
||||
|
||||
{p 8 15 2}
|
||||
{cmd:estat} {cmd:aic}
|
||||
|
||||
{p 8 15 2}
|
||||
{cmd:estat} {cmd:aic}
|
||||
|
||||
{title:Description}
|
||||
|
||||
{pmore}{cmd:estat aic} and {cmd:estat bic} compute the Akaike and Schwarz
|
||||
Bayesian information criteria, respectively.
|
||||
|
||||
|
||||
{title:The estat correlate command}
|
||||
|
||||
{title:Syntax}
|
||||
|
||||
{p 8 15 2}
|
||||
{cmd:estat} {cmdab:corr:elate}
|
||||
[{cmd:,}
|
||||
{opt level(#)}
|
||||
{opt bound}]
|
||||
|
||||
{title:Description}
|
||||
|
||||
{marker corr}{...}
|
||||
{pmore}{opt estat} {cmd:correlate} transforms the covariance parameters into
|
||||
correlations for factor covariances and measurement-error covariances. The
|
||||
delta method standard errors are given; for correlations close to plus or
|
||||
minus 1, the confidence intervals may extend beyond the range of admissible
|
||||
values.{p_end}
|
||||
|
||||
{title:Options}
|
||||
|
||||
{phang2}{opt level(#)} changes the confidence level for confidence-interval
|
||||
reporting.{p_end}
|
||||
|
||||
{phang2}{cmd:bound} provides an alternative confidence interval based on
|
||||
Fisher's z transform of the correlation coefficient. It guarantees
|
||||
that the end points of the interval are in the (-1,1) range, provided the
|
||||
estimate itself is in this range.
|
||||
|
||||
|
||||
{marker predict}{...}
|
||||
{title:The predict command}
|
||||
|
||||
{title:Syntax}
|
||||
|
||||
{p 8 19 2}
|
||||
{cmd:predict} {dtype} {it:{help newvarlist}} {ifin} [{cmd:,} {it:scoring_method}]
|
||||
|
||||
{p2colset 9 27 29 2}{...}
|
||||
{p2col:{it:scoring_method}}factor scoring method{p_end}
|
||||
{p2line}
|
||||
{p2col:{cmdab:reg:ression}}regression, or empirical Bayes, score{p_end}
|
||||
{p2col:{cmdab:emp:iricalbayes}}alias for {cmd:regression}{p_end}
|
||||
{p2col:{cmdab:eb:ayes}}alias for {cmd:regression}{p_end}
|
||||
{p2col:{opt mle}}MLE, or Bartlett score{p_end}
|
||||
{p2col:{cmdab:bart:lett}}alias for {cmd:mle}{p_end}
|
||||
{p2line}
|
||||
{p2colreset}{...}
|
||||
|
||||
{title:Description}
|
||||
|
||||
{pmore} {cmd:predict} can be used to create factor scores following {cmd:confa}.
|
||||
The number of variables in {it:newvarlist} must be the same as the number of
|
||||
factors in the model specification; all factors are predicted at once by the
|
||||
relevant matrix formula.
|
||||
|
||||
{title:Options}
|
||||
|
||||
{phang2}
|
||||
{opt regression}, {opt empiricalbayes}, or {opt ebayes}
|
||||
requests regression, or empirical Bayes, factor scoring procedure.
|
||||
|
||||
{phang2}
|
||||
{opt mle} or {opt bartlett} requests Bartlett scoring procedure.
|
||||
|
||||
|
||||
{title:Example}
|
||||
|
||||
{phang}{cmd:. use hs-cfa}{p_end}
|
||||
{phang}{cmd:. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(iv) corr(x7:x8)}{p_end}
|
||||
{phang}{cmd:. estat fit}{p_end}
|
||||
{phang}{cmd:. estat corr}{p_end}
|
||||
{phang}{cmd:. estat corr, bound}{p_end}
|
||||
{phang}{cmd:. predict fa1-fa3, reg}{p_end}
|
||||
{phang}{cmd:. predict fb1-fb3, bart}{p_end}
|
||||
|
||||
|
||||
{title:References}
|
||||
|
||||
{phang}
|
||||
Bentler, P. M. 1990. Comparative fit indexes in structural models.
|
||||
{it:Psychological Bulletin} 107: 238-246.
|
||||
|
||||
{phang}
|
||||
Browne, M. W., and R. Cudeck. 1993. Alternative ways of assessing model fit.
|
||||
In {it:Testing Structural Equation Models}, ed. K. A. Bollen and J. S. Long,
|
||||
136-162. Newbury Park, CA: Sage.
|
||||
|
||||
{phang}
|
||||
Tucker, L. R., and C. Lewis. 1973. A reliability coefficient for maximum likelihood factor analysis. {it:Psychometrika} 38: 1-10.
|
||||
|
||||
|
||||
{title:Author}
|
||||
|
||||
{pstd}Stanislav Kolenikov{p_end}
|
||||
{pstd}Department of Statistics{p_end}
|
||||
{pstd}University of Missouri{p_end}
|
||||
{pstd}Columbia, MO{p_end}
|
||||
{pstd}kolenikovs@missouri.edu{p_end}
|
||||
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{psee}
|
||||
Article: {it:Stata Journal}, volume 9, number 3: {browse "http://www.stata-journal.com/article.html?article=st0169":st0169}
|
||||
|
||||
{psee}Online: {helpb confa}, {helpb bollenstine}{p_end}
|
||||
@ -1,20 +0,0 @@
|
||||
*! Log likelihood for confa: linear form; part of confa suite; 16 Oct 2008
|
||||
program define confa_lf
|
||||
|
||||
args lnf $CONFA_args
|
||||
* $CONFA_args contains the names of the equations, but
|
||||
* we need the variable names
|
||||
gettoken lnf allthenames : 0
|
||||
|
||||
tempvar lnl
|
||||
qui g double `lnl' = .
|
||||
|
||||
mata: CONFA_NormalLKHDr( "`allthenames'", "`lnl'")
|
||||
|
||||
qui replace `lnf' = `lnl'
|
||||
|
||||
if $CONFA_loglevel > 3 li `lnl'
|
||||
|
||||
end
|
||||
|
||||
exit
|
||||
@ -1,20 +0,0 @@
|
||||
*! Log likelihood for confa: linear form; part of confa suite; 23 Apr 2009
|
||||
program define confa_lfm
|
||||
|
||||
args lnf $CONFA_args
|
||||
* $CONFA_args contains the names of the equations, but
|
||||
* we need the variable names
|
||||
gettoken lnf allthenames : 0
|
||||
|
||||
tempvar lnl
|
||||
qui g double `lnl' = .
|
||||
|
||||
mata: CONFA_NormalLKHDrMiss( "`allthenames'", "`lnl'")
|
||||
|
||||
qui replace `lnf' = `lnl'
|
||||
|
||||
if $CONFA_loglevel > 3 li `lnl'
|
||||
|
||||
end
|
||||
|
||||
exit
|
||||
@ -1,89 +0,0 @@
|
||||
*! v.1.0 -- prediction commands for confa suite; 16 Oct 2008
|
||||
program define confa_p
|
||||
version 10
|
||||
|
||||
* set trace on
|
||||
|
||||
* di as inp "`0'"
|
||||
|
||||
syntax anything [if] [in], [EBayes EMPiricalbayes REGression MLE BARTlett SCores]
|
||||
|
||||
* fork to equation scores or factor scores
|
||||
|
||||
if "`scores'"!="" EqScores `0'
|
||||
else if "`ebayes'`empiricalbayes'`regression'`mle'`bartlett'" != "" FScores `0'
|
||||
else {
|
||||
di as err "cannot figure those options out"
|
||||
exit 198
|
||||
}
|
||||
|
||||
end
|
||||
|
||||
program define EqScores
|
||||
|
||||
* implicitly used by _robust and svy
|
||||
* typical request: predict stub*, scores
|
||||
* stub* is not parsed well by newvarlist, have to use anything
|
||||
syntax anything [if] [in], scores
|
||||
marksample touse, novarlist
|
||||
ml score `anything' if `touse'
|
||||
end
|
||||
|
||||
program define FScores
|
||||
|
||||
* user requested factor predictions
|
||||
|
||||
syntax newvarlist [if] [in], [EBayes EMPiricalbayes REGression MLE BARTlett]
|
||||
|
||||
marksample touse, novarlist
|
||||
|
||||
if "`ebayes'`empiricalbayes'`regression'`mle'`bartlett'" == "" | ///
|
||||
( ("`ebayes'`empiricalbayes'`regression'"~="" ) & ("`mle'`bartlett'"~="" ) ) {
|
||||
|
||||
di as err "One and only one factor scoring option must be specified"
|
||||
exit 198
|
||||
}
|
||||
else {
|
||||
|
||||
local nfactors = rowsof( e(Phi) )
|
||||
|
||||
if "`: word count `varlist''" ~= "`nfactors'" {
|
||||
di as err "Must specify as many new variables as there were factors in confa model"
|
||||
exit 198
|
||||
}
|
||||
|
||||
* generate new variables
|
||||
forvalues k=1/`nfactors' {
|
||||
tempname f`k'
|
||||
qui gen double `f`k'' = .
|
||||
local flist `flist' `f`k''
|
||||
}
|
||||
|
||||
if "`ebayes'`empiricalbayes'`regression`" ~= "" {
|
||||
* Empirical Bayes:
|
||||
mata : CONFA_P_EB("`flist'", "`e(observed)'", "`touse'")
|
||||
}
|
||||
|
||||
if "`mle'`bartlett'" ~= "" {
|
||||
* MLE/Bartlett scoring:
|
||||
mata : CONFA_P_MLE("`flist'", "`e(observed)'", "`touse'")
|
||||
}
|
||||
|
||||
nobreak {
|
||||
forvalues k=1/`nfactors' {
|
||||
local type : word `k' of `typlist'
|
||||
local name : word `k' of `varlist'
|
||||
qui gen `type' `name' = `f`k'' if `touse'
|
||||
label var `name' `"`e(factor`k')', `ebayes'`empiricalbayes'`regression'`mle'`bartlett' method"'
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
end
|
||||
|
||||
exit
|
||||
|
||||
History:
|
||||
v.1.0 -- June 12, 2008: Empirical Bayes and MLE scoring
|
||||
@ -1,18 +0,0 @@
|
||||
*! comfirmdir Version 1.1 dan.blanchette@duke.edu 22Jan2009
|
||||
*! Center of Entrepreneurship and Innovation Duke University's Fuqua School of Business
|
||||
* confirmdir Version 1.1 dan_blanchette@unc.edu 17Jan2008
|
||||
* research computing, unc-ch
|
||||
* - made it handle long directory names
|
||||
** confirmdir Version 1.0 dan_blanchette@unc.edu 05Oct2003
|
||||
** the carolina population center, unc-ch
|
||||
|
||||
program define confirmdir, rclass
|
||||
version 8
|
||||
|
||||
local cwd `"`c(pwd)'"'
|
||||
quietly capture cd `"`1'"'
|
||||
local confirmdir=_rc
|
||||
quietly cd `"`cwd'"'
|
||||
return local confirmdir `"`confirmdir'"'
|
||||
|
||||
end
|
||||
@ -1,54 +0,0 @@
|
||||
{smcl}
|
||||
{* 17Jan2008}{...}
|
||||
{* 28Oct2004}{...}
|
||||
{* 19Nov2003}{...}
|
||||
{hline}
|
||||
help for {hi:confirmdir} {right:manual: {hi:[R] none}}
|
||||
{right:dialog: {hi: none} }
|
||||
{hline}
|
||||
|
||||
|
||||
{title:Confirms if directory exists}
|
||||
|
||||
{p 8 17 2}
|
||||
{cmd:confirmdir} {it:full direcotory name}
|
||||
{p_end}
|
||||
|
||||
|
||||
{title:Description}
|
||||
|
||||
{p 4 4 2}{cmd:confirmdir} is designed for programmers who want to know if a directory exists.
|
||||
This is just like {cmd:confirm} command when used to confirm a file. If Stata allowed their
|
||||
{cmd:confirm} command to also have the "dir" option, this program would not have been
|
||||
written. Stata's {cmd:confirm file} will confirm a directory in UNIX/Linux but not in Windows.
|
||||
{cmd:confirmdir} works in all operating systems.{p_end}
|
||||
|
||||
{title:Examples}
|
||||
|
||||
{p 4 8 2}{cmd:. confirmdir "c:\My Favorite Directory\Where I Keep Stuff\"}{p_end}
|
||||
|
||||
{p 4 8 2}{cmd:. confirmdir /projects/ethiopia/survey2002/data/}{p_end}
|
||||
|
||||
|
||||
{title:Saved Results}
|
||||
|
||||
{p 4 8 2}The {cmd:confirmdir} command saves in {cmd:r()}:{p_end}
|
||||
|
||||
{synoptset 20 tabbed}{...}
|
||||
{p2col 5 20 24 2: Macros}{p_end}
|
||||
{synopt:{cmd:r(confirmdir)}}return code returned by the {help cd:cd} command.{p_end}
|
||||
|
||||
|
||||
{title:Author}
|
||||
|
||||
{p 4 4 2}
|
||||
Dan Blanchette {break}
|
||||
Center of Entrepreneurship and Innovation {break}
|
||||
Duke University's Fuqua School of Business {break}
|
||||
Dan.Blanchette@Duke.edu{p_end}
|
||||
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{p 4 13 2}On-line: {help confirm:confirm} {help cd:cd}, {help tmpdir:tmpdir} (if installed){p_end}
|
||||
|
||||
@ -1,62 +0,0 @@
|
||||
program def convlist, rclass
|
||||
*! NJC 1.0.0 9 April 2001
|
||||
version 6.0
|
||||
gettoken lists 0 : 0, parse(",")
|
||||
if "`lists'" == "" | "`lists'" == "," {
|
||||
di in r "no lists specified"
|
||||
exit 198
|
||||
}
|
||||
|
||||
tokenize "`lists'", parse("\")
|
||||
if "`4'" != "" | "`2'" != "\" {
|
||||
di in r "incorrect syntax"
|
||||
exit 198
|
||||
}
|
||||
numlist "`1'"
|
||||
local list1 `r(numlist)'
|
||||
local n1 : word count `list1'
|
||||
numlist "`3'"
|
||||
local list2 `r(numlist)'
|
||||
local n2 : word count `list2'
|
||||
|
||||
syntax [ , Global(str) Noisily ]
|
||||
|
||||
if length("`global'") > 8 {
|
||||
di in r "global name must be <=8 characters"
|
||||
exit 198
|
||||
}
|
||||
|
||||
local n3 = `n1' + `n2' - 1
|
||||
local k = 1
|
||||
while `k' <= `n3' {
|
||||
tempname c`k'
|
||||
scalar `c`k'' = 0
|
||||
local k = `k' + 1
|
||||
}
|
||||
|
||||
tokenize `list1'
|
||||
|
||||
local i = 1
|
||||
while `i' <= `n1' {
|
||||
local j = 1
|
||||
while `j' <= `n2' {
|
||||
local b`j' : word `j' of `list2'
|
||||
local k = `i' + `j' - 1
|
||||
scalar `c`k'' = `c`k'' + (``i'' * `b`j'')
|
||||
local j = `j' + 1
|
||||
}
|
||||
local i = `i' + 1
|
||||
}
|
||||
|
||||
local k = 1
|
||||
while `k' <= `n3' {
|
||||
local this = `c`k''
|
||||
local newlist "`newlist'`this' "
|
||||
local k = `k' + 1
|
||||
}
|
||||
|
||||
if "`noisily'" != "" { di "`newlist'" }
|
||||
if "`global'" != "" { global `global' "`newlist'" }
|
||||
return local list `newlist'
|
||||
end
|
||||
|
||||
@ -1,2 +0,0 @@
|
||||
.h listutil
|
||||
|
||||
@ -1,877 +0,0 @@
|
||||
*! version 0.8.1 - 2009-07-31
|
||||
* work around S11 bug in estimates table
|
||||
|
||||
* To do:
|
||||
* 1) add weights
|
||||
* 2) use returns rather than scalars
|
||||
|
||||
// compare count models
|
||||
|
||||
capture program drop countfit
|
||||
program define countfit, rclass
|
||||
version 9.0
|
||||
|
||||
* 2009-07-31
|
||||
local vwidth = 32 // causes error in Stata 11
|
||||
local vwidth = 30
|
||||
|
||||
syntax varlist [if] [in] ///
|
||||
, [Generate(string) replace ///
|
||||
INFlate(string) /// inflation variables
|
||||
MAXcount(integer 9) ///
|
||||
NOGraph /// suppress graph of differences in predicted probabilities
|
||||
NODifferences /// suppress table of differences in predicted probabilities
|
||||
NOEstimates /// suppress table of estimated parameters
|
||||
NOFit /// suppress table of fit statistics
|
||||
Prm Nbreg ZIP ZINb ///
|
||||
nodash ///
|
||||
NOConstant ///
|
||||
NOPRTable ///
|
||||
note(string) /// note for graph
|
||||
GRAPHexport(string) /// options passed to graph export
|
||||
NOIsily ///
|
||||
]
|
||||
|
||||
// trap weights
|
||||
|
||||
if "`e(wtype)'"!="" {
|
||||
di in r "-countfix- does not work with weights."
|
||||
exit
|
||||
}
|
||||
|
||||
// define variable lists
|
||||
|
||||
* inflate is same as rhs if not specified
|
||||
if "`inflate'"=="" {
|
||||
** better way to do this?
|
||||
local nvars : word count `varlist'
|
||||
foreach i of numlist 2(1)`nvars' {
|
||||
local var : word `i' of `varlist'
|
||||
local inflate "`inflate' `var'"
|
||||
}
|
||||
}
|
||||
|
||||
// set up printing
|
||||
|
||||
local f83 "%8.3f"
|
||||
local f93 "%9.3f"
|
||||
local f93l "%-9.3f"
|
||||
local f103 "%10.3f"
|
||||
local dash ""
|
||||
if "`nodash'"=="" {
|
||||
local dash ///
|
||||
"--------------------------------------------------------------------------"
|
||||
}
|
||||
|
||||
// construct information on models being assessed
|
||||
|
||||
* defalult name is CF for variables created
|
||||
if "`generate'"=="" local set "CF"
|
||||
local set "`generate'" // label for sets of models
|
||||
local modellist "" // list of types of models estimated
|
||||
local mdlnmlist "" // like modellist but begin with generate prefix _
|
||||
local pltsym "" // symbols used in the plot
|
||||
local plty "" // y variables to plot
|
||||
local pltx "" // x variables to plot
|
||||
local mdlnum = 1 // number associated with each model
|
||||
|
||||
* set up information for all models specified
|
||||
if "`prm'"=="prm" {
|
||||
local mdlnmlist "`mdlnmlist' `set'PRM"
|
||||
local modellist "PRM "
|
||||
local pltx "`set'PRMval"
|
||||
local plty "`set'PRMdif "
|
||||
local pltsym "Th "
|
||||
local pltleg1 "`set'PRM"
|
||||
local mdlnum = `mdlnum' + 1
|
||||
}
|
||||
if "`nbreg'"=="nbreg" {
|
||||
local mdlnmlist "`mdlnmlist' `set'NBRM"
|
||||
local modellist "`modellist'NBRM "
|
||||
local pltx "`set'NBRMval"
|
||||
local plty "`plty' `set'NBRMdif "
|
||||
local pltsym "`pltsym' Sh "
|
||||
local pltleg`mdlnum' "`set'NBRM"
|
||||
local mdlnum = `mdlnum' + 1
|
||||
}
|
||||
if "`zip'"=="zip" {
|
||||
local mdlnmlist "`mdlnmlist' `set'ZIP"
|
||||
local modellist "`modellist'ZIP "
|
||||
local pltx "`set'ZIPval"
|
||||
local plty "`plty' `set'ZIPdif "
|
||||
local pltsym "`pltsym' T "
|
||||
local pltleg`mdlnum' "`set'ZIP"
|
||||
local mdlnum = `mdlnum' + 1
|
||||
}
|
||||
if "`zinb'"=="zinb" {
|
||||
local mdlnmlist "`mdlnmlist' `set'ZINB"
|
||||
local modellist "`modellist'ZINB "
|
||||
local pltx "`set'ZINBval"
|
||||
local plty "`plty' `set'ZINBdif "
|
||||
local pltsym "`pltsym' S "
|
||||
local pltleg`mdlnum' "`set'ZINB"
|
||||
local mdlnum = `mdlnum' + 1
|
||||
}
|
||||
* if none, then all
|
||||
if "`prm'"=="" & "`nbreg'"=="" & "`zip'"=="" & "`zinb'"=="" {
|
||||
local pltx "`set'PRMval"
|
||||
local plty "`set'PRMdif `set'NBRMdif `set'ZIPdif `set'ZINBdif"
|
||||
local pltsym "Th Sh T S"
|
||||
local pltleg1 "`set'PRM"
|
||||
local pltleg2 "`set'NBRM"
|
||||
local pltleg3 "`set'ZIP"
|
||||
local pltleg4 "`set'ZINB"
|
||||
local mdlnmlist "`set'PRM `set'NBRM `set'ZIP `set'ZINB"
|
||||
local modellist "PRM NBRM ZIP ZINB"
|
||||
local prm "prm"
|
||||
local nbreg "nbreg"
|
||||
local zip "zip"
|
||||
local zinb "zinb"
|
||||
* drop? local alphaopt "drop(lnalpha:_cons)"
|
||||
}
|
||||
|
||||
// estimate models
|
||||
|
||||
* 2009-07-31
|
||||
_count_estimate `varlist' `if' `in' [`fweight' `pweight' `iweight'], ///
|
||||
max(`maxcount') gen(`generate') `replace' inflate(`inflate') ///
|
||||
`prm' `nbreg' `zip' `zinb' `noconstant' `noisily'
|
||||
|
||||
// table of estimates
|
||||
|
||||
|
||||
if "`noestimates'"=="" {
|
||||
estimates table `mdlnmlist', eform ///
|
||||
b(%9.3f) t(%7.2f) label varwidth(`vwidth') ///
|
||||
stats(alpha N ll bic aic)
|
||||
}
|
||||
|
||||
// summary table of mean observed - predicted probabilities
|
||||
|
||||
local c1 = 10
|
||||
local c2 = 25
|
||||
local c3 = 31
|
||||
local c4 = 47
|
||||
|
||||
if "`nodifferences'"=="" {
|
||||
|
||||
di in g "Comparison of Mean Observed and Predicted Count"
|
||||
di
|
||||
di in g " " _col(`c1') " Maximum" ///
|
||||
_col(`c2') " At" ///
|
||||
_col(`c3') " Mean"
|
||||
di in g "Model" _col(`c1') " Difference" ///
|
||||
_col(`c2') "Value" ///
|
||||
_col(`c3') " |Diff|"
|
||||
di in g "---------------------------------------------"
|
||||
}
|
||||
|
||||
* loop through models and compute differences
|
||||
foreach m in `modellist' {
|
||||
|
||||
local modelnm "`set'`m'"
|
||||
|
||||
* stats on difference
|
||||
qui sum `modelnm'dif
|
||||
local toplot "`toplot' `modelnm'dif"
|
||||
local `m'difsd = r(sd) // sd of obs-pred
|
||||
local `m'difmin = r(min) // min
|
||||
local `m'difmax = r(max) // max
|
||||
|
||||
* stats on mean absolute difference
|
||||
capture drop `modelnm'difabs
|
||||
qui gen `modelnm'absdif = abs(`modelnm'dif)
|
||||
qui sum `modelnm'absdif
|
||||
local `m'absdifmax = r(max) // max
|
||||
scalar absdifsd`m' = r(sd) // sd
|
||||
scalar absdifmn`m' = r(mean) // mean
|
||||
* find values for largest difference
|
||||
tempname difval
|
||||
qui gen `difval' = (`modelnm'absdif>``m'absdifmax'-.00001)*_n ///
|
||||
if `modelnm'absdif!=.
|
||||
qui sum `difval'
|
||||
local `m'absdifmaxval = r(max) - 1
|
||||
|
||||
* sign of deviation
|
||||
tempname maxdif // 0.8.0
|
||||
if ``m'absdifmax' == abs(``m'difmin') {
|
||||
*local maxdif = ``m'difmin' // 0.8.0
|
||||
scalar `maxdif' = ``m'difmin'
|
||||
}
|
||||
if ``m'absdifmax' == abs(``m'difmax') {
|
||||
*local maxdif = ``m'difmax' // 0.8.0
|
||||
scalar `maxdif' = ``m'difmax'
|
||||
}
|
||||
|
||||
if "`nodifferences'"=="" {
|
||||
* print summary of differences from predictions
|
||||
di in y "`modelnm'" _col(`c1') in y `f83' `maxdif' ///
|
||||
_col(`c2') `f83' " ``m'absdifmaxval'" ///
|
||||
_col(`c3') `f83' absdifmn`m'
|
||||
}
|
||||
|
||||
} // loop through models
|
||||
|
||||
// TABLES OF OBSERVED AND PREDICTED COUNTS
|
||||
|
||||
if "`noprtable'" == "" {
|
||||
|
||||
foreach t in `mdlnmlist' {
|
||||
|
||||
qui {
|
||||
sum `t'absdif
|
||||
local max = r(N) - 1 // largest count
|
||||
tempname sumde sumpe sumob sumpr // 0.8.0
|
||||
scalar `sumde' = r(sum) // sum of abs dif
|
||||
sum `t'pearson
|
||||
scalar `sumpe' = r(sum) // sum of pearson dif
|
||||
sum `t'obeq
|
||||
scalar `sumob' = r(sum) // sum of pr observed
|
||||
sum `t'preq
|
||||
scalar `sumpr' = r(sum) // sum of pr predicted
|
||||
} // qui
|
||||
local c1 = 7
|
||||
local c2 = 19
|
||||
local c3 = 30
|
||||
local c4 = 40
|
||||
local c5 = 50
|
||||
di
|
||||
di in y "`t'" in g ": Predicted and actual probabilities"
|
||||
di
|
||||
di in g "Count" _col(`c1') " Actual" ///
|
||||
_col(`c2') "Predicted" ///
|
||||
_col(`c3') " |Diff|" ///
|
||||
_col(`c4') " Pearson"
|
||||
|
||||
local dash ""
|
||||
if "`nodash'"=="" {
|
||||
local dash ///
|
||||
"------------------------------------------------"
|
||||
}
|
||||
|
||||
di in g "`dash'"
|
||||
|
||||
foreach c of numlist 0(1)`max' {
|
||||
local i = `c' + 1
|
||||
tempname ob pr de pe // 0.8.0
|
||||
scalar `ob' = `t'obeq[`i']
|
||||
scalar `pr' = `t'preq[`i']
|
||||
scalar `de' = abs(`t'dif[`i'])
|
||||
scalar `pe' = `t'pearson[`i']
|
||||
|
||||
di in y "`c'" ///
|
||||
_col(`c1') `f83' `ob' ///
|
||||
_col(`c2') `f83' `pr' ///
|
||||
_col(`c3') `f83' `de' ///
|
||||
_col(`c4') `f83' `pe'
|
||||
|
||||
} // loop through counts
|
||||
|
||||
di in g "`dash'"
|
||||
di in y "Sum" ///
|
||||
_col(`c1') `f83' `sumob' ///
|
||||
_col(`c2') `f83' `sumpr' ///
|
||||
_col(`c3') `f83' `sumde' ///
|
||||
_col(`c4') `f83' `sumpe'
|
||||
|
||||
} // loop through modellist
|
||||
|
||||
} // print table of predictions
|
||||
|
||||
// PLOT DIFFERENCES
|
||||
|
||||
if "`nograph'"=="" {
|
||||
|
||||
twoway (connected `plty' `pltx', ///
|
||||
msymbol(`pltsym') ///
|
||||
clpat(tight_dot tight_dot tight_dot tight_dot ) ///
|
||||
ytitle("Observed-Predicted") ///
|
||||
subtitle("Note: positive deviations show underpredictions.", ///
|
||||
pos(11) size(small)) ///
|
||||
ylabel(-.10(.05).10, grid gmax gmin) ///
|
||||
xlabel(0(1)`maxcount') ///
|
||||
note(`note') ///
|
||||
ysize(3.5) xsize(4.5) ///
|
||||
legend(order(1 "`pltleg1'" 2 "`pltleg2'" ///
|
||||
3 "`pltleg3'" 4 "`pltleg4'")) ///
|
||||
)
|
||||
|
||||
* export graph
|
||||
if "`graphexport'" != "" {
|
||||
qui graph export `graphexport'
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
// COMPARE FIT STATISTICS
|
||||
|
||||
if "`nofit'"=="" {
|
||||
|
||||
local dash ""
|
||||
if "`nodash'"=="" {
|
||||
local dash ///
|
||||
"-------------------------------------------------------------------------"
|
||||
}
|
||||
local c1 = 16
|
||||
local c2 = 32
|
||||
local c3 = 48
|
||||
local c4 = 56
|
||||
local c5 = 62
|
||||
|
||||
di _n in g "Tests and Fit Statistics"
|
||||
di
|
||||
tempname aicd // 0.8.0
|
||||
|
||||
* base PRM
|
||||
if "`prm'"!="" {
|
||||
local mdl1 "`set'PRM"
|
||||
local mdl1type "PRM"
|
||||
di in y "`mdl1'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl1' ///
|
||||
_col(`c2') in g "AIC=" in y `f103' aic`mdl1' ///
|
||||
_col(`c3') in g "Prefer" ///
|
||||
_col(`c4') in g "Over" ///
|
||||
_col(`c5') in g "Evidence"
|
||||
|
||||
* prm vs nbreg
|
||||
if "`nbreg'"!="" {
|
||||
local mdl2 "`set'NBRM"
|
||||
local mdl2type "NBRM"
|
||||
global bic1 = bic`mdl1'
|
||||
global bic2 = bic`mdl2'
|
||||
_count_bic_dif
|
||||
local pref = $bicpref
|
||||
local nopref = $bicnopref
|
||||
di "`dash'"
|
||||
* bic
|
||||
di in y " " in g " vs " in y "`mdl2'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' $bicdif ///
|
||||
_col(`c3') in y "`mdl`pref'type'" ///
|
||||
_col(`c4') in y "`mdl`nopref'type'" ///
|
||||
_col(`c5') in y "$bicsup"
|
||||
* aic
|
||||
scalar `aicd' = aic`mdl1' - aic`mdl2'
|
||||
local aicfav "`mdl2type'"
|
||||
local aicnofav "`mdl1type'"
|
||||
if aic`mdl1' < aic`mdl2' {
|
||||
local aicfav "`mdl1type'"
|
||||
local aicnofav "`mdl2type'"
|
||||
}
|
||||
di _col(`c1') in g "AIC=" in y `f103' aic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' `aicd' ///
|
||||
_col(`c3') in y "`aicfav'" ///
|
||||
_col(`c4') in y "`aicnofav'"
|
||||
* lr test
|
||||
local lrfav "`mdl1type'"
|
||||
local lrnofav "`mdl2type'"
|
||||
if lrnb_prmp < .05 {
|
||||
local lrfav "`mdl2type'"
|
||||
local lrnofav "`mdl1type'"
|
||||
}
|
||||
di _col(`c1') in g "LRX2=" in y `f93' lrnb_prm ///
|
||||
_col(`c2') in g "prob=" in y `f93' lrnb_prmp in g ///
|
||||
_col(`c3') in y "`lrfav'" ///
|
||||
_col(`c4') in y "`lrnofav'" ///
|
||||
_col(`c5') in y "p=" `f93l' lrnb_prmp
|
||||
} // no nbreg vs prm
|
||||
|
||||
* prm vs zip
|
||||
if "`zip'"!="" {
|
||||
local mdl2 "`set'ZIP"
|
||||
local mdl2type "ZIP"
|
||||
* bic
|
||||
global bic1 = bic`mdl1'
|
||||
global bic2 = bic`mdl2'
|
||||
_count_bic_dif
|
||||
local pref = $bicpref
|
||||
local nopref = $bicnopref
|
||||
di in g "`dash'"
|
||||
di in y " " in g " vs " in y "`mdl2'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' $bicdif ///
|
||||
_col(`c3') in y "`mdl`pref'type'" ///
|
||||
_col(`c4') in y "`mdl`nopref'type'" ///
|
||||
_col(`c5') in y "$bicsup"
|
||||
* aic
|
||||
tempname aicd // 0.8.0
|
||||
scalar `aicd' = aic`mdl1' - aic`mdl2'
|
||||
local aicfav "`mdl2type'"
|
||||
local aicnofav "`mdl1type'"
|
||||
if aic`mdl1' < aic`mdl2' {
|
||||
local aicfav "`mdl1type'"
|
||||
local aicnofav "`mdl2type'"
|
||||
}
|
||||
di _col(`c1') in g "AIC=" in y `f103' aic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' `aicd' ///
|
||||
_col(`c3') in y "`aicfav'" ///
|
||||
_col(`c4') in y "`aicnofav'"
|
||||
* vuong test
|
||||
local vufav "`mdl1type'"
|
||||
local vunofav "`mdl2type'"
|
||||
if vu`mdl2'>0 {
|
||||
local vufav "`mdl2type'"
|
||||
local vunofav "`mdl1type'"
|
||||
}
|
||||
di _col(`c1') in g "Vuong=" in y `f83' vu`mdl2' ///
|
||||
_col(`c2') in g "prob=" in y `f93' vu`mdl2'p in g ///
|
||||
_col(`c3') in y "`vufav'" ///
|
||||
_col(`c4') in y "`vunofav'" ///
|
||||
_col(`c5') in y "p=" `f93l' vu`mdl2'p
|
||||
} // no zip vs prm
|
||||
|
||||
* prm vs zinb
|
||||
if "`zinb'"!="" {
|
||||
local mdl2 "`set'ZINB"
|
||||
local mdl2type "ZINB"
|
||||
* bic
|
||||
global bic1 = bic`mdl1'
|
||||
global bic2 = bic`mdl2'
|
||||
_count_bic_dif
|
||||
local pref = $bicpref
|
||||
local nopref = $bicnopref
|
||||
di in g "`dash'"
|
||||
di in y " " in g " vs " in y "`mdl2'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' $bicdif ///
|
||||
_col(`c3') in y "`mdl`pref'type'" ///
|
||||
_col(`c4') in y "`mdl`nopref'type'" ///
|
||||
_col(`c5') in y "$bicsup"
|
||||
* aic
|
||||
scalar `aicd' = aic`mdl1' - aic`mdl2'
|
||||
local aicfav "`mdl2type'"
|
||||
local aicnofav "`mdl1type'"
|
||||
|
||||
if aic`mdl1' < aic`mdl2' {
|
||||
local aicfav "`mdl1type'"
|
||||
local aicnofav "`mdl2type'"
|
||||
}
|
||||
di _col(`c1') in g "AIC=" in y `f103' aic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' `aicd' ///
|
||||
_col(`c3') in y "`aicfav'" ///
|
||||
_col(`c4') in y "`aicnofav'"
|
||||
} // no zinb vs prm
|
||||
|
||||
} // if no prm
|
||||
|
||||
* base nbreg
|
||||
if "`nbreg'"!="" {
|
||||
local mdl1 "`set'NBRM"
|
||||
local mdl1type "NBRM"
|
||||
di in g "`dash'"
|
||||
di in y "`mdl1'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl1' ///
|
||||
_col(`c2') in g "AIC=" in y `f103' aic`mdl1' ///
|
||||
_col(`c3') in g "Prefer" ///
|
||||
_col(`c4') in g "Over" ///
|
||||
_col(`c5') in g "Evidence"
|
||||
|
||||
* nbreg vs zip
|
||||
if "`zip'"!="" {
|
||||
local mdl2 "`set'ZIP"
|
||||
local mdl2type "ZIP"
|
||||
* bic
|
||||
global bic1 = bic`mdl1'
|
||||
global bic2 = bic`mdl2'
|
||||
_count_bic_dif
|
||||
local pref = $bicpref
|
||||
local nopref = $bicnopref
|
||||
di in g "`dash'"
|
||||
di in y " " in g " vs " in y "`mdl2'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' $bicdif ///
|
||||
_col(`c3') in y "`mdl`pref'type'" ///
|
||||
_col(`c4') in y "`mdl`nopref'type'" ///
|
||||
_col(`c5') in y "$bicsup"
|
||||
* aic
|
||||
scalar `aicd' = aic`mdl1' - aic`mdl2'
|
||||
local aicfav "`mdl2type'"
|
||||
local aicnofav "`mdl1type'"
|
||||
|
||||
if aic`mdl1' < aic`mdl2' {
|
||||
local aicfav "`mdl1type'"
|
||||
local aicnofav "`mdl2type'"
|
||||
}
|
||||
di _col(`c1') in g "AIC=" in y `f103' aic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' `aicd' ///
|
||||
_col(`c3') in y "`aicfav'" ///
|
||||
_col(`c4') in y "`aicnofav'"
|
||||
} // no zip vs nbreg
|
||||
|
||||
* nbreg vs zinb
|
||||
if "`zinb'"!="" {
|
||||
local mdl2 "`set'ZINB"
|
||||
local mdl2type "ZINB"
|
||||
* bic
|
||||
global bic1 = bic`mdl1'
|
||||
global bic2 = bic`mdl2'
|
||||
_count_bic_dif
|
||||
local pref = $bicpref
|
||||
local nopref = $bicnopref
|
||||
di in g "`dash'"
|
||||
di in y " " in g " vs " in y "`mdl2'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' $bicdif ///
|
||||
_col(`c3') in y "`mdl`pref'type'" ///
|
||||
_col(`c4') in y "`mdl`nopref'type'" ///
|
||||
_col(`c5') in y "$bicsup"
|
||||
* aic
|
||||
scalar `aicd' = aic`mdl1' - aic`mdl2'
|
||||
local aicfav "`mdl2type'"
|
||||
local aicnofav "`mdl1type'"
|
||||
if aic`mdl1' < aic`mdl2' {
|
||||
local aicfav "`mdl1type'"
|
||||
local aicnofav "`mdl2type'"
|
||||
}
|
||||
di _col(`c1') in g "AIC=" in y `f103' aic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' `aicd' ///
|
||||
_col(`c3') in y "`aicfav'" ///
|
||||
_col(`c4') in y "`aicnofav'"
|
||||
* vuong test
|
||||
local vufav "`mdl1type'"
|
||||
local vunofav "`mdl2type'"
|
||||
if vu`mdl2'>0 {
|
||||
local vufav "`mdl2type'"
|
||||
local vunofav "`mdl1type'"
|
||||
}
|
||||
di _col(`c1') in g "Vuong=" in y `f83' vu`mdl2' ///
|
||||
_col(`c2') in g "prob=" in y `f93' vu`mdl2'p in g ///
|
||||
_col(`c3') in y "`vufav'" ///
|
||||
_col(`c4') in y "`vunofav'" ///
|
||||
_col(`c5') in y "p=" `f93l' vu`mdl2'p
|
||||
|
||||
} // no zinb vs nbreg
|
||||
|
||||
} // if no nbreg
|
||||
|
||||
if "`zip'"!="" {
|
||||
|
||||
local mdl1 "`set'ZIP"
|
||||
local mdl1type "ZIP"
|
||||
di in g "`dash'"
|
||||
di in y "`mdl1'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl1' ///
|
||||
_col(`c2') in g "AIC=" in y `f103' aic`mdl1' ///
|
||||
_col(`c3') in g "Prefer" ///
|
||||
_col(`c4') in g "Over" ///
|
||||
_col(`c5') in g "Evidence"
|
||||
|
||||
* zip vs zinb
|
||||
if "`zinb'"!="" {
|
||||
local mdl2 "`set'ZINB"
|
||||
local mdl2type "ZINB"
|
||||
* bic
|
||||
global bic1 = bic`mdl1'
|
||||
global bic2 = bic`mdl2'
|
||||
_count_bic_dif
|
||||
local pref = $bicpref
|
||||
local nopref = $bicnopref
|
||||
di in g "`dash'"
|
||||
di in y " " in g " vs " in y "`mdl2'" ///
|
||||
_col(`c1') in g "BIC=" in y `f103' bic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' $bicdif ///
|
||||
_col(`c3') in y "`mdl`pref'type'" ///
|
||||
_col(`c4') in y "`mdl`nopref'type'" ///
|
||||
_col(`c5') in y "$bicsup"
|
||||
* aic
|
||||
scalar `aicd' = aic`mdl1' - aic`mdl2'
|
||||
local aicfav "`mdl2type'"
|
||||
local aicnofav "`mdl1type'"
|
||||
if aic`mdl1' < aic`mdl2' {
|
||||
local aicfav "`mdl1type'"
|
||||
local aicnofav "`mdl2type'"
|
||||
}
|
||||
di _col(`c1') in g "AIC=" in y `f103' aic`mdl2' ///
|
||||
_col(`c2') in g "dif=" in y `f103' `aicd' ///
|
||||
_col(`c3') in y "`aicfav'" ///
|
||||
_col(`c4') in y "`aicnofav'"
|
||||
* lr test
|
||||
local lrfav "`mdl1type'"
|
||||
local lrnofav "`mdl2type'"
|
||||
if lrnb_prmp < .05 {
|
||||
local lrfav "`mdl2type'"
|
||||
local lrnofav "`mdl1type'"
|
||||
}
|
||||
di _col(`c1') in g "LRX2=" in y `f93' lrzip_zinb ///
|
||||
_col(`c2') in g "prob=" in y `f93' lrzip_zinbp in g ///
|
||||
_col(`c3') in y "`lrfav'" ///
|
||||
_col(`c4') in y "`lrnofav'" ///
|
||||
_col(`c5') in y "p=" `f93l' lrnb_prmp
|
||||
di in g "`dash'"
|
||||
|
||||
} // no zinb vs zip
|
||||
|
||||
} // no zip
|
||||
|
||||
} // no fit
|
||||
|
||||
end
|
||||
|
||||
// compute strength of bic difference?
|
||||
|
||||
capture program drop _count_bic_dif
|
||||
program define _count_bic_dif
|
||||
|
||||
* compute bin diff from model 1 and model 2
|
||||
global bicdif = $bic1 - $bic2
|
||||
tempname bicdabs // 0.8.0
|
||||
scalar `bicdabs' = abs($bicdif)
|
||||
|
||||
* evaluate support based on bic difference
|
||||
if `bicdabs'~=. {
|
||||
global bicsup "Very strong"
|
||||
if `bicdabs'<= .0000000001 {
|
||||
global bicsup "no"
|
||||
}
|
||||
else if `bicdabs' <=2 {
|
||||
global bicsup "Weak"
|
||||
}
|
||||
else if `bicdabs' <=6 {
|
||||
global bicsup "Positive"
|
||||
}
|
||||
else if `bicdabs' <=10 {
|
||||
global bicsup "Strong"
|
||||
}
|
||||
global bicpref = 2
|
||||
global bicnopref = 1
|
||||
if $bicdif < 0 {
|
||||
global bicpref = 1
|
||||
global bicnopref = 2
|
||||
}
|
||||
if `bicdabs'< .0000000001 & `bicdabs'>-.0000000001 {
|
||||
global bicpref = 0
|
||||
}
|
||||
|
||||
}
|
||||
end
|
||||
|
||||
// ESTIMATE MODELS AND STORE RESULTS
|
||||
|
||||
capture program drop _count_estimate
|
||||
program define _count_estimate, rclass
|
||||
|
||||
syntax varlist [if] [in] [, ///
|
||||
inflate(varlist) ///
|
||||
MAXcount(integer 9) Generate(string) replace ///
|
||||
Prm Nbreg ZIP ZINb NOConstant ///
|
||||
NOIsily ]
|
||||
|
||||
// estimate models & create globals with stats
|
||||
|
||||
local set "`generate'"
|
||||
tempname n
|
||||
|
||||
local noise ""
|
||||
if "`noisily'"=="noisily" {
|
||||
local noise "noisily"
|
||||
}
|
||||
|
||||
// prm
|
||||
|
||||
if "`prm'"!="" {
|
||||
qui {
|
||||
local modelnm "PRM"
|
||||
local fullnm "`set'`modelnm'"
|
||||
`noise' poisson `varlist' `if' `in', `noconstant'
|
||||
estimates store `fullnm'
|
||||
scalar ll`fullnm' = e(ll) // log lik
|
||||
fitstat, bic
|
||||
scalar bicp`fullnm' = r(bic_p) // bic'
|
||||
return scalar bicp`fullnm' = r(bic_p) // 0.8.0
|
||||
scalar aic`fullnm' = r(aic) // aic
|
||||
scalar x2`fullnm' = r(lrx2) // lrx2 all b=0
|
||||
scalar x2p`fullnm' = r(lrx2_p)
|
||||
scalar bic`fullnm' = r(bic) // bic
|
||||
if "`replace'"=="replace" {
|
||||
capture drop `fullnm'rate
|
||||
capture drop `fullnm'prgt
|
||||
capture drop `fullnm'val
|
||||
capture drop `fullnm'obeq
|
||||
capture drop `fullnm'preq
|
||||
capture drop `fullnm'prle
|
||||
capture drop `fullnm'oble
|
||||
capture drop `fullnm'absdif
|
||||
capture drop `fullnm'dif
|
||||
capture drop `fullnm'pearson
|
||||
local i = 0
|
||||
while `i'<=`maxcount' {
|
||||
capture drop `fullnm'pr`i'
|
||||
capture drop `fullnm'cu`i'
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
prcounts `fullnm', plot max(`maxcount') // predicted counts
|
||||
label var `fullnm'preq "`modelnm' predicted" // predicted Pr(y)
|
||||
gen `fullnm'dif = `fullnm'obeq - `fullnm'preq // obs - predicted
|
||||
label var `fullnm'dif "`modelnm' obs - pred"
|
||||
* 2004-10-29 add CT 5.34
|
||||
scalar `n' = e(N) // sample size
|
||||
gen `fullnm'pearson = ///
|
||||
(`n' * `fullnm'dif * `fullnm'dif) / `fullnm'preq
|
||||
label var `fullnm'pearson "`modelnm' contribution to Pearson X2"
|
||||
sum `fullnm'pearson
|
||||
} // qui
|
||||
}
|
||||
|
||||
// nbreg
|
||||
|
||||
if "`nbreg'"!="" {
|
||||
qui {
|
||||
local modelnm "NBRM"
|
||||
local fullnm "`set'`modelnm'"
|
||||
`noise' nbreg `varlist' `if' `in', `noconstant'
|
||||
estimates store `fullnm'
|
||||
scalar ll`fullnm' = e(ll) // log lik
|
||||
|
||||
scalar lrnb_prm = e(chi2_c) // lrx2 of nb vs prm
|
||||
scalar lrnb_prmp = chiprob(1, e(chi2_c))*0.5
|
||||
|
||||
fitstat, bic
|
||||
scalar bicp`fullnm' = r(bic_p) // bic'
|
||||
scalar aic`fullnm' = r(aic) // aic
|
||||
scalar x2`fullnm' = r(lrx2) // lrx2 all b=0
|
||||
scalar x2p`fullnm' = r(lrx2_p)
|
||||
scalar bic`fullnm' = r(bic) // bic
|
||||
|
||||
if "`replace'"=="replace" {
|
||||
capture drop `fullnm'rate
|
||||
capture drop `fullnm'prgt
|
||||
capture drop `fullnm'val
|
||||
capture drop `fullnm'obeq
|
||||
capture drop `fullnm'preq
|
||||
capture drop `fullnm'prle
|
||||
capture drop `fullnm'oble
|
||||
capture drop `fullnm'dif
|
||||
capture drop `fullnm'absdif
|
||||
capture drop `fullnm'pearson
|
||||
local i = 0
|
||||
while `i'<=`maxcount' {
|
||||
capture drop `fullnm'pr`i'
|
||||
capture drop `fullnm'cu`i'
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
|
||||
prcounts `fullnm', plot max(`maxcount') // predicted counts
|
||||
label var `fullnm'preq "`modelnm' predicted"
|
||||
gen `fullnm'dif = `fullnm'obeq - `fullnm'preq
|
||||
label var `fullnm'dif "`modelnm' obs - pred"
|
||||
* 2004-10-29 add CT 5.34
|
||||
scalar `n' = e(N) // sample size
|
||||
gen `fullnm'pearson = ///
|
||||
(`n' * `fullnm'dif * `fullnm'dif) / `fullnm'preq
|
||||
label var `fullnm'pearson "`modelnm' contribution to Pearson X2"
|
||||
sum `fullnm'pearson
|
||||
} // qui
|
||||
}
|
||||
|
||||
// zip
|
||||
|
||||
if "`zip'"!="" {
|
||||
qui {
|
||||
local modelnm "ZIP"
|
||||
local fullnm "`set'`modelnm'"
|
||||
`noise' zip `varlist' `if' `in', ///
|
||||
inf(`inflate') vuong `noconstant'
|
||||
estimates store `fullnm'
|
||||
scalar vu`fullnm' = e(vuong) // vuong vs prm
|
||||
scalar vu`fullnm'p = 1-norm(abs(e(vuong)))
|
||||
scalar ll`fullnm' = e(ll) // log lik
|
||||
fitstat, bic
|
||||
scalar bicp`fullnm' = r(bic_p) // bic'
|
||||
scalar aic`fullnm' = r(aic) // aic
|
||||
scalar x2`fullnm' = r(lrx2) // lrx2 all b=0
|
||||
scalar x2p`fullnm' = r(lrx2_p)
|
||||
scalar bic`fullnm' = r(bic) // bic
|
||||
if "`replace'"=="replace" {
|
||||
capture drop `fullnm'rate
|
||||
capture drop `fullnm'prgt
|
||||
capture drop `fullnm'val
|
||||
capture drop `fullnm'obeq
|
||||
capture drop `fullnm'preq
|
||||
capture drop `fullnm'prle
|
||||
capture drop `fullnm'oble
|
||||
capture drop `fullnm'all0
|
||||
capture drop `fullnm'dif
|
||||
capture drop `fullnm'absdif
|
||||
capture drop `fullnm'pearson
|
||||
local i = 0
|
||||
while `i'<=`maxcount' {
|
||||
capture drop `fullnm'pr`i'
|
||||
capture drop `fullnm'cu`i'
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
|
||||
prcounts `fullnm', plot max(`maxcount') // predicted counts
|
||||
label var `fullnm'preq "`modelnm' predicted"
|
||||
gen `fullnm'dif = `fullnm'obeq - `fullnm'preq
|
||||
label var `fullnm'dif "`modelnm' obs - pred"
|
||||
* 2004-10-29 add CT 5.34
|
||||
scalar `n' = e(N) // sample size
|
||||
gen `fullnm'pearson = ///
|
||||
(`n' * `fullnm'dif * `fullnm'dif) / `fullnm'preq
|
||||
label var `fullnm'pearson "`modelnm' contribution to Pearson X2"
|
||||
qui sum `fullnm'pearson
|
||||
} // qui
|
||||
}
|
||||
|
||||
// zinb
|
||||
|
||||
if "`zinb'"!="" {
|
||||
qui {
|
||||
local modelnm "ZINB"
|
||||
local fullnm "`set'`modelnm'"
|
||||
`noise' zinb `varlist' `if' `in', ///
|
||||
inf(`inflate') vuong zip `noconstant'
|
||||
estimates store `fullnm'
|
||||
scalar vu`fullnm' = e(vuong) // vuong vs nbreg
|
||||
scalar vu`fullnm'p = 1-norm(abs(e(vuong)))
|
||||
scalar ll`fullnm' = e(ll) // loglik
|
||||
scalar lrzip_zinb = e(chi2_cp) // lrx2 zinb vs zip
|
||||
scalar lrzip_zinbp = chiprob(1, e(chi2_cp))*0.5
|
||||
fitstat, bic
|
||||
scalar bicp`fullnm' = r(bic_p) // bic'
|
||||
scalar aic`fullnm' = r(aic) // aic
|
||||
scalar x2`fullnm' = r(lrx2) // lrx2 all b=0
|
||||
scalar x2p`fullnm' = r(lrx2_p)
|
||||
scalar bic`fullnm' = r(bic) // bic
|
||||
if "`replace'"=="replace" {
|
||||
capture drop `fullnm'rate
|
||||
capture drop `fullnm'prgt
|
||||
capture drop `fullnm'val
|
||||
capture drop `fullnm'obeq
|
||||
capture drop `fullnm'preq
|
||||
capture drop `fullnm'prle
|
||||
capture drop `fullnm'oble
|
||||
capture drop `fullnm'absdif
|
||||
capture drop `fullnm'dif
|
||||
capture drop `fullnm'all0
|
||||
capture drop `fullnm'pearson
|
||||
local i = 0
|
||||
while `i'<=`maxcount' {
|
||||
capture drop `fullnm'pr`i'
|
||||
capture drop `fullnm'cu`i'
|
||||
local i = `i' + 1
|
||||
}
|
||||
}
|
||||
|
||||
prcounts `fullnm', plot max(`maxcount') // predicted counts
|
||||
label var `fullnm'preq "`modelnm' predicted"
|
||||
gen `fullnm'dif = `fullnm'obeq - `fullnm'preq
|
||||
label var `fullnm'dif "`modelnm' obs - pred"
|
||||
* 2004-10-29 add CT 5.34
|
||||
scalar `n' = e(N) // sample size
|
||||
gen `fullnm'pearson = ///
|
||||
(`n' * `fullnm'dif * `fullnm'dif) / `fullnm'preq
|
||||
label var `fullnm'pearson "`modelnm' contribution to Pearson X2"
|
||||
qui sum `fullnm'pearson
|
||||
} // qui
|
||||
}
|
||||
|
||||
end // _count_estimate
|
||||
|
||||
exit
|
||||
* version 0.8.0 fix pr bug
|
||||
* version 0.2.1 13Apr2005 add replace; trap weights
|
||||
* version 0.2.0 27Feb2005 first documented version
|
||||
@ -1,86 +0,0 @@
|
||||
.-
|
||||
help for ^countfit^ - 03Nov2005
|
||||
.-
|
||||
|
||||
Compare fit of alternive count models
|
||||
-------------------------------------
|
||||
|
||||
^countfit^ varlist [if] [in]^,^ [^inf^late^(^varlist2^)^ ^noc^onstant ^p^rm ^n^breg
|
||||
^zip^ ^zin^b ^g^enerate^(^prefix^)^ ^replace^ ^note(^string^)^
|
||||
^graph^export^(^filename[^, replace^]^)^ ^nog^raph ^nod^ifferences ^noprt^able
|
||||
^noe^stimates ^nof^it ^nodash^ ^max^count(#) ^noi^sily]
|
||||
|
||||
Description
|
||||
-----------
|
||||
|
||||
^countfit^ compares the fit of the Poissin, negative binomial, zero-inflated
|
||||
Poisson and zero-inflated negative binomial models, generateing a table of
|
||||
estimates, a table of differences between observed and average estimated
|
||||
probabilities for each count, a graph of these differences, and various tests
|
||||
and measures used to compare the fit of count models.
|
||||
|
||||
Specifying the model
|
||||
--------------------
|
||||
Immediately afer the command name ^countfit^ you specify the dependent and
|
||||
independent variables as you would with ^poisson^ or the other models. For
|
||||
zero-inflated models, the ^inflate^ option is used in the same was as in the
|
||||
^zip^ and ^zinb^ commands. ^noconstant^ can be used to exclude the constant
|
||||
term.
|
||||
|
||||
Options to select the models to fit
|
||||
-----------------------------------
|
||||
By default, ^poisson^, ^nbreg^, ^zip^ and ^zinb^ are estimated. If you
|
||||
only want some of these models, specify the modesl you want with:
|
||||
|
||||
^prm^ - include ^poisson^
|
||||
^nbreg^ - include ^nbreg^
|
||||
^zip^ - include ^poisson^
|
||||
^zinb^ - include ^poisson^
|
||||
|
||||
Options to label results
|
||||
------------------------
|
||||
^generate()^ is up to five letters to name the variables that are created
|
||||
and to label the models in the output. This name is placed in front
|
||||
of the type of model (e.g., namePRM). This is optional but will help
|
||||
keep track of results from multiple specifications of models.
|
||||
|
||||
^replace^ will replace variables created with the ^generate^ option if they
|
||||
already exist.
|
||||
|
||||
^note()^ is a label added to the graph that is saved.
|
||||
|
||||
^graphexport()^ contains options to be sent to the ^graph export^ command to
|
||||
export the graph that is create. For example, ^graph(mdl.emf, replace)^
|
||||
would save the file mdl.emf and replace it if it exists. If this option
|
||||
is not included, the graph is not saved.
|
||||
|
||||
Options controlling what is printed
|
||||
-----------------------------------
|
||||
^noisily^ shows the output from the estimation commands called by ^countfit^.
|
||||
^nograph^ suppress graph of differences from observed counts.
|
||||
^nodifferences^ suppress table of differences from observed counts.
|
||||
^noprtable^ suppresses table of predictions for each model.
|
||||
^noestimates^ suppress table of estimated coefficients.
|
||||
^nofit^ suppress table of fit statistics and test of fit
|
||||
^nodash^ suppress dashed lines between measures of fit
|
||||
^maxcount()^ number of counts to evaluate.
|
||||
^noisily^ includes output from Stata estimation commands; without this option
|
||||
the results are only shown in the ^estimates table^ output.
|
||||
|
||||
Notes
|
||||
-----
|
||||
|
||||
^countfit^ is based on the results from the Stata models described above,
|
||||
the predictions computed by ^prcounts^, and the fit measures computed by
|
||||
^fitstat^.
|
||||
|
||||
Examples
|
||||
--------
|
||||
|
||||
^. use couart2^
|
||||
^. countfit art fem mar kid5 phd ment, inf(ment fem) nbreg zinb nograph^
|
||||
|
||||
.-
|
||||
Author: J. Scott Long and Jeremy Freese
|
||||
www.indiana.edu/~jslsoc/spost.htm
|
||||
spostsup@@indiana.edu
|
||||
@ -1,29 +0,0 @@
|
||||
program def cseplist, rclass
|
||||
*! NJC 1.0.0 31 August 2000
|
||||
version 6.0
|
||||
gettoken list 0 : 0, parse(",")
|
||||
if "`list'" == "" | "`list'" == "," {
|
||||
di in r "nothing in list"
|
||||
exit 198
|
||||
}
|
||||
syntax , [ Global(str) Noisily ]
|
||||
|
||||
if length("`global'") > 8 {
|
||||
di in r "global name must be <=8 characters"
|
||||
exit 198
|
||||
}
|
||||
|
||||
tokenize `list'
|
||||
local n : word count `list'
|
||||
local i = 1
|
||||
while `i' < `n' {
|
||||
local newlist "`newlist'``i'',"
|
||||
local i = `i' + 1
|
||||
}
|
||||
local newlist "`newlist'``n''"
|
||||
|
||||
if "`noisily'" != "" { di "`newlist'" }
|
||||
if "`global'" != "" { global `global' "`newlist'" }
|
||||
return local list `newlist'
|
||||
end
|
||||
|
||||
@ -1,2 +0,0 @@
|
||||
.h listutil
|
||||
|
||||
@ -1,54 +0,0 @@
|
||||
program def cvarlist, rclass
|
||||
*! NJC 1.0.0 18 August 2000
|
||||
version 6.0
|
||||
gettoken list 0 : 0, parse(",")
|
||||
if "`list'" == "" | "`list'" == "," {
|
||||
di in r "nothing in list"
|
||||
exit 198
|
||||
}
|
||||
syntax , [ new NUmeric String Noisily Global(str)]
|
||||
|
||||
local nopts : word count `new' `numeric' `string'
|
||||
if `nopts' > 1 {
|
||||
di in r "use just one of options new, numeric, string"
|
||||
exit 198
|
||||
}
|
||||
|
||||
if "`global'" != "" {
|
||||
tokenize `global'
|
||||
args global1 global2 global3
|
||||
if "`global3'" != "" {
|
||||
di in r "global( ) must contain at most 2 names"
|
||||
exit 198
|
||||
}
|
||||
if (length("`global1'") > 8) | (length("`global2'") > 8) {
|
||||
di in r "global name must be <=8 characters"
|
||||
exit 198
|
||||
}
|
||||
}
|
||||
|
||||
tokenize `list'
|
||||
local nwords : word count `list'
|
||||
local i = 1
|
||||
while `i' <= `nwords' {
|
||||
capture confirm `new' `numeric' `string' variable ``i''
|
||||
if _rc == 0 { local newlist "`newlist'``i'' " }
|
||||
else local badlist "`badlist'``i'' "
|
||||
local i = `i' + 1
|
||||
}
|
||||
|
||||
if "`noisily'" != "" {
|
||||
if "`newlist'" != "" {
|
||||
di in g "list 1: " in y "`newlist'"
|
||||
}
|
||||
if "`badlist'" != "" {
|
||||
di in g "list 2: " in y "`badlist'"
|
||||
}
|
||||
}
|
||||
|
||||
if "`global1'" != "" { global `global1' "`newlist'" }
|
||||
if "`global2'" != "" { global `global2' "`badlist'" }
|
||||
return local list1 `newlist'
|
||||
return local list2 `badlist'
|
||||
end
|
||||
|
||||
@ -1,2 +0,0 @@
|
||||
.h listutil
|
||||
|
||||
Reference in New Issue
Block a user