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205 lines
11 KiB
Plaintext
205 lines
11 KiB
Plaintext
{smcl}
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{* 14may2012}{...}
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{cmd:help traj}
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{hline}
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{p 4 4 6}{hi: traj}: Fit the traj model{p_end}
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{p 4 4 6}{hi: {help trajplot}}: Plot the traj model results{p_end}
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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:traj} uses a discrete mixture model to model longitudinal data. This model allows for data
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grouping using different parameter values for each group distribution. Groupings may identify
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distinct subpopulations. Alternatively, groupings may represent components approximating an
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unknown (possibly complex) data distribution.{p_end}
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{hline}
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{title:Examples}
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1:{help traj##s_1: Censored Normal Model}
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2.{help traj##s_2: Zero-Inflated Poisson Model}
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3.{help traj##s_3: Logistic Model}
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4.{help traj##s_4: Time-Stable Covariates for Group Membership}
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5.{help traj##s_5: Group Membership Probabilities from a Model with Time Stable Covariates}
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6.{help traj##s_6: Time-Varying Covariates Influencing Trajectory Paths}
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7.{help traj##s_7: Start Values}
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8.{help traj##s_8: Joint Trajectory Model}
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9.{help traj##s_9: Distal Outcome Model}
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10.{help traj##s_10: Wald Tests for Hypotheses Based on the Parameter Estimates}
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11.{help traj##s_11: Exposure Time / Sample Weights}
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12.{help traj##s_12: Dropout Model}
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{hline}
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{marker s_Syntax}
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{title:Syntax}
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{p 6 8 6}
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{cmdab:traj} [{help if:{it:if}}]{cmd:, var(}{help varlist:{it:varlist}}{cmd:) indep(}{help varlist:{it:varlist}}{cmd:) model(}{it:modeltype}{cmd:) order(}{help numlist:{it:numlist}}{cmd:)} [additional options] {p_end}
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{synoptset 20 tabbed}{...}
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{synopthdr}
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{synoptline}
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{syntab:Trajectory Variables}
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{synopt :{opt var(varlist)}}dependent variables, measured at different times or ages{p_end}
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{synopt :{opt indep(varlist)}}independent variables i.e. when the dependant variables were measured{p_end}
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{syntab:Model}
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{synopt :{opt model(modeltype)}}probability distribution for the dependent variables: {opt cnorm}, {opt zip}, or {opt logit} {p_end}
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{synopt :{opt order(numlist)}}polynomial type (0=intercept, 1=linear, 2=quadratic, 3=cubic) for each group trajectory{p_end}
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{synopt :{opt min(#)}}minimum value for the censored normal model (required for cnorm){p_end}
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{synopt :{opt max(#)}}maximum value for the censored normal model (required for cnorm){p_end}
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{synopt :{opt iorder(numlist)}}optional polynomial type (0=intercept, 1=linear, 2=quadratic, 3=cubic) for the zero-inflation of each group{p_end}
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{synopt :{opt exposure(varlist)}}optional exposure variables for the zero-inflated Poisson model{p_end}
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{synopt :{opt weight(varname)}}optional sampling weight variable{p_end}
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{syntab:Time-Stable Covariates for Group Membership}
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{synopt :{opt risk(varlist)}}covariates for the probability of group membership{p_end}
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{synopt :{opt refgroup(#)}}controls the reference group (default = 1) when the risk option is used{p_end}
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{syntab:Time-Varying Covariates for Group Membership}
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{synopt :{opt tcov(varlist)}}time-varying covariates for each group trajectory{p_end}
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{synopt :{opt plottcov(matrix)}}optional values for plotting trajectories with time-varying covariates{p_end}
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{syntab:Dropout Model}
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{synopt :{opt dropout(numlist)}}include logistic model of dropout probability per wave with 0 = constant rate,
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1 = depends on the previous response, 2 = depends on the two previous responses, for each group{p_end}
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{synopt :{opt dcov(varlist)}}optional lagged time-varying covariates for the dropout model{p_end}
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{synopt :{opt obsmar(varname)}}optional binary variable to mark which observations are to be included
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in the dropout model and those to be treated as missing at random. This variable = 1 for observations to
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be treated as data MAR (include completers) and = 0 for observations to be used for the modeled dropout{p_end}
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{syntab:Distal Outcome Model}
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{synopt :{opt outcome(varname)}}a distal variable to be regressed on the probability of group membership{p_end}
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{synopt :{opt omodel(modeltype)}}probability distribution for the outcome variable: {opt normal}, {opt cnorm}, {opt zip}, or {opt logit} {p_end}
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{synopt :{opt ocov(varlist)}}optional covariates for the outcome model{p_end}
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{syntab:Joint Trajectory Model}
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{syntab:The joint trajectory model uses the options shown above with a '2' suffix to specify the second model e.g. {opt model2(modeltype)} etc. See the {help traj##s_8:joint trajectories} example.}
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{syntab:Other}
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{synopt :{opt start(matrix)}}parameter start values to override default start values{p_end}
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{synopt :{opt detail}}show minimization iterations for monitoring model fitting progress{p_end}
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{marker s_1}
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{title:Example 1: Censored Normal Model}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/montreal_sim.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj, model(cnorm) var(qcp*op) indep(age*) order(1 3 2) min(0) max(10)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Age") ytitle("Opposition")"'}{p_end}
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{p 4 10 8}{stata `"list _traj_Group - _traj_ProbG3 if _n < 3, ab(12)"'}{p_end}
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{marker s_2}
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{title:Example 2: Zero-Inflated Poisson Model}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/anag1.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj, model(zip) var(y*) indep(t*) order(2 0 2 2)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Scaled Age") ytitle("Annual Conviction Rate") ci"'}{p_end}
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{marker s_3}
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{title:Example 3: Logistic Model}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/cambrdge.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj, model(logit) var(p1-p23) indep(tt1-tt23) order(3 3 3)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Scaled Age") ytitle("Prevalence")"'}{p_end}
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{marker s_4}
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{title:Example 4: Time-Stable Covariates for Group Membership}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/montreal_sim.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj, model(cnorm) var(qcp*op) indep(age*) order(1 3 2) min(0) max(10) risk(scolmer scolper)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Age") ytitle("Opposition")"'}{p_end}
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{marker s_5}
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{title:Example 5: Group Membership Probabilities from a Model with Time Stable Covariates}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/anag1.dta, clear"}{p_end}
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{p 4 10 8}{stata "matrix strt = (-4.57, -7, 5.96, -1.38, -20.5, 25.4, -8.0, -4.37, 5.55, -1.52, -1.7, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, -2.5, 0, 0, 0, 0)"}{p_end}
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{p 4 10 8}{stata "traj, model(zip) var(y*) indep(t*) order(0 2 2 2) risk(lowiq crimpar daring pbeh) start(strt)"}{p_end}
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{p 4 10 8}{stata "list lowiq - _traj_ProbG4 in 1/6, ab(12)"}{p_end}
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{marker s_6}
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{title:Example 6: Time-Varying Covariates Influencing Trajectory Paths}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/gang_data_sim.dta, clear"}{p_end}
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{p 4 10 8}{stata "matrix tc1 = (0, 0, 0, 0, 0, 0, 0)"}{p_end}
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{p 4 10 8}{stata "matrix tc2 = (0, 0, 0, 1, 1, 1, 1)"}{p_end}
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{p 4 10 8}{stata "traj, model(zip) var(bat*) indep(t*) tcov(gang*) order(2 2 2 2 2) plottcov(tc1)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Scaled Age") ytitle("Rate")"'}{p_end}
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{p 4 10 8}{stata "traj, model(zip) var(bat*) indep(t*) tcov(gang*) order(2 2 2 2 2) plottcov(tc2)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Scaled Age") ytitle("Rate")"'}{p_end}
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{marker s_7}
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{title:Example 7: Start Values}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/cambrdge.dta, clear"}{p_end}
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{p 4 10 8}{stata "matrix strt = ( -4.8, -15.5, 16.2, -4.5, -1.1, -4.5, 5.1, -1.3, 0, -.2, 66, 20, 7, 7 )"}{p_end}
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{p 4 10 8}{stata "traj , model(zip) var(x01-x23) indep(tt1-tt23) order(0 2 0 2) iorder(1) start(strt)"}{p_end}
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{p 4 10 8}{stata `"trajplot, ytitle("Offense Counts") xtitle("Scaled Age")"'}{p_end}
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{marker s_8}
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{title:Example 8: Joint Trajectory Model}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/montreal_sim.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj , model(cnorm) var(qcp84op qcp88op qcp89op qcp90op qcp91op) indep(age1-age5) order(1 2 2) max(10) var2(qas91det qas92det qas93det qas94det qas95det) indep2(age3-age7) model2(zip) order2(2 2 2 2)"}{p_end}
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{p 4 10 8}{stata `"trajplot, ytitle("Opposition") xtitle("Age")"'}{p_end}
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{p 4 10 8}{stata `"trajplot, model(2) ytitle("Rate") xtitle("Age")"'}{p_end}
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{marker s_9}
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{title:Example 9: Distal Outcome Model}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/montreal_sim.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj, model(cnorm) max(10) var(qcp84op qcp88op qcp89op qcp90op qcp91op) indep(age1-age5) order(0 2 2) outcome(nbp14) omodel(poisson)"}{p_end}
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{p 4 10 8}{stata `"trajplot, ytitle("Opposition") xtitle("Age")"'}{p_end}
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{marker s_10}
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{title:Example 10: Wald Tests for Hypotheses Based on the Parameter Estimates}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/anag1.dta, clear"}{p_end}
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{p 4 10 8}{stata "matrix strt = (-4.57, -7, 5.96, -1.38, -20.5, 25.4, -8.0, -4.37, 5.55, -1.52, -1.7, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, -2.5, 0, 0, 0, 0)"}{p_end}
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{p 4 10 8}{stata "traj, model(zip) var(y*) indep(t*) order(0 2 2 2) risk(lowiq crimpar daring pbeh) start(strt)"}{p_end}
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{p 4 10 8}{stata "trajplot, ci"}{p_end}
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{p 4 10 8}{stata "testnl _b[lowiq2] = _b[lowiq3] = _b[lowiq4]"}{p_end}
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{marker s_11}
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{title:Example 11: Exposure Time / Sample Weights}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/weight_expos_sim.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj, model(zip) var(g2 - g13) indep(t*) order(2 2) iorder(0 2) expos(e*) weight(wt50)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Age") ytitle("Annual Arrest Rate")"'}{p_end}
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{marker s_12}
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{title:Example 12: Dropout Model}
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{p 4 10 8}{stata "use http://www.andrew.cmu.edu/user/bjones/traj/data/panss.dta, clear"}{p_end}
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{p 4 10 8}{stata "traj, var(p1-p6) indep(t1-t6) model(cnorm) min(-999) max(999) order(3 3 0) risk(risper) dropout(2 2 2) dcov(risper risper risper risper risper risper)"}{p_end}
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{p 4 10 8}{stata `"trajplot, xtitle("Time (weeks)") ytitle("PANSS") ci"'}{p_end}
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{p 4 10 8}{stata `"trajplot, dropout xtitle("Time (weeks)") ytitle("Dropout probability")"'}{p_end}
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{title:Author}
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{p 4 4 6}Bobby L. Jones{p_end}
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{p 4 4 6}bjones@andrew.cmu.edu{p_end}
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{p 4 4 6}based on work with Daniel S. Nagin.{p_end}
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{title:Web-page}
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{p 4 4 6}http://www.andrew.cmu.edu/user/bjones/{p_end}
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{title:References}
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{p 4 4 6}Jones BL, Nagin DS, Roeder K. 2001. A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research 29:374-393{p_end}
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{p 4 4 6}Jones BL, Nagin DS. 2007. Advances in group-based trajectory modeling and an SAS procedure for estimating them. Sociological Methods & Research 35:542-571{p_end}
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{p 4 4 6}Nagin D. 2005. Group-Based Modeling of Development. Cambridge, MA: Harvard Univ. Press{p_end}
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{smcl}
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