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DESCRIPTION
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DESCRIPTION
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Package: SPT
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Type: Package
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Title: SPHERE Psychometrics Tools
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Version: 1.0.0
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Author: Corentin Choisy
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Maintainer: Corentin Choisy <corentin.choisy@univ-nantes.fr>
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Description: Various tools for psychometrics analyses
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License: AGPLv3
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URL: https://git.corentinchoisy.xyz/corentinchoisy/SPT
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Encoding: UTF-8
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LazyData: true
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RoxygenNote: 7.3.2
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Imports:
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vcrpart,
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rjags
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LICENSE
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LICENSE
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GNU AFFERO GENERAL PUBLIC LICENSE
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||||||
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Version 3, 19 November 2007
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Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
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Preamble
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The GNU Affero General Public License is a free, copyleft license for
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|
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|
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|
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|
||||||
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|
||||||
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"Additional permissions" are terms that supplement the terms of this
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When you convey a copy of a covered work, you may at your option
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|
||||||
|
removal in certain cases when you modify the work.) You may place
|
||||||
|
additional permissions on material, added by you to a covered work,
|
||||||
|
for which you have or can give appropriate copyright permission.
|
||||||
|
|
||||||
|
Notwithstanding any other provision of this License, for material you
|
||||||
|
add to a covered work, you may (if authorized by the copyright holders of
|
||||||
|
that material) supplement the terms of this License with terms:
|
||||||
|
|
||||||
|
a) Disclaiming warranty or limiting liability differently from the
|
||||||
|
terms of sections 15 and 16 of this License; or
|
||||||
|
|
||||||
|
b) Requiring preservation of specified reasonable legal notices or
|
||||||
|
author attributions in that material or in the Appropriate Legal
|
||||||
|
Notices displayed by works containing it; or
|
||||||
|
|
||||||
|
c) Prohibiting misrepresentation of the origin of that material, or
|
||||||
|
requiring that modified versions of such material be marked in
|
||||||
|
reasonable ways as different from the original version; or
|
||||||
|
|
||||||
|
d) Limiting the use for publicity purposes of names of licensors or
|
||||||
|
authors of the material; or
|
||||||
|
|
||||||
|
e) Declining to grant rights under trademark law for use of some
|
||||||
|
trade names, trademarks, or service marks; or
|
||||||
|
|
||||||
|
f) Requiring indemnification of licensors and authors of that
|
||||||
|
material by anyone who conveys the material (or modified versions of
|
||||||
|
it) with contractual assumptions of liability to the recipient, for
|
||||||
|
any liability that these contractual assumptions directly impose on
|
||||||
|
those licensors and authors.
|
||||||
|
|
||||||
|
All other non-permissive additional terms are considered "further
|
||||||
|
restrictions" within the meaning of section 10. If the Program as you
|
||||||
|
received it, or any part of it, contains a notice stating that it is
|
||||||
|
governed by this License along with a term that is a further
|
||||||
|
restriction, you may remove that term. If a license document contains
|
||||||
|
a further restriction but permits relicensing or conveying under this
|
||||||
|
License, you may add to a covered work material governed by the terms
|
||||||
|
of that license document, provided that the further restriction does
|
||||||
|
not survive such relicensing or conveying.
|
||||||
|
|
||||||
|
If you add terms to a covered work in accord with this section, you
|
||||||
|
must place, in the relevant source files, a statement of the
|
||||||
|
additional terms that apply to those files, or a notice indicating
|
||||||
|
where to find the applicable terms.
|
||||||
|
|
||||||
|
Additional terms, permissive or non-permissive, may be stated in the
|
||||||
|
form of a separately written license, or stated as exceptions;
|
||||||
|
the above requirements apply either way.
|
||||||
|
|
||||||
|
8. Termination.
|
||||||
|
|
||||||
|
You may not propagate or modify a covered work except as expressly
|
||||||
|
provided under this License. Any attempt otherwise to propagate or
|
||||||
|
modify it is void, and will automatically terminate your rights under
|
||||||
|
this License (including any patent licenses granted under the third
|
||||||
|
paragraph of section 11).
|
||||||
|
|
||||||
|
However, if you cease all violation of this License, then your
|
||||||
|
license from a particular copyright holder is reinstated (a)
|
||||||
|
provisionally, unless and until the copyright holder explicitly and
|
||||||
|
finally terminates your license, and (b) permanently, if the copyright
|
||||||
|
holder fails to notify you of the violation by some reasonable means
|
||||||
|
prior to 60 days after the cessation.
|
||||||
|
|
||||||
|
Moreover, your license from a particular copyright holder is
|
||||||
|
reinstated permanently if the copyright holder notifies you of the
|
||||||
|
violation by some reasonable means, this is the first time you have
|
||||||
|
received notice of violation of this License (for any work) from that
|
||||||
|
copyright holder, and you cure the violation prior to 30 days after
|
||||||
|
your receipt of the notice.
|
||||||
|
|
||||||
|
Termination of your rights under this section does not terminate the
|
||||||
|
licenses of parties who have received copies or rights from you under
|
||||||
|
this License. If your rights have been terminated and not permanently
|
||||||
|
reinstated, you do not qualify to receive new licenses for the same
|
||||||
|
material under section 10.
|
||||||
|
|
||||||
|
9. Acceptance Not Required for Having Copies.
|
||||||
|
|
||||||
|
You are not required to accept this License in order to receive or
|
||||||
|
run a copy of the Program. Ancillary propagation of a covered work
|
||||||
|
occurring solely as a consequence of using peer-to-peer transmission
|
||||||
|
to receive a copy likewise does not require acceptance. However,
|
||||||
|
nothing other than this License grants you permission to propagate or
|
||||||
|
modify any covered work. These actions infringe copyright if you do
|
||||||
|
not accept this License. Therefore, by modifying or propagating a
|
||||||
|
covered work, you indicate your acceptance of this License to do so.
|
||||||
|
|
||||||
|
10. Automatic Licensing of Downstream Recipients.
|
||||||
|
|
||||||
|
Each time you convey a covered work, the recipient automatically
|
||||||
|
receives a license from the original licensors, to run, modify and
|
||||||
|
propagate that work, subject to this License. You are not responsible
|
||||||
|
for enforcing compliance by third parties with this License.
|
||||||
|
|
||||||
|
An "entity transaction" is a transaction transferring control of an
|
||||||
|
organization, or substantially all assets of one, or subdividing an
|
||||||
|
organization, or merging organizations. If propagation of a covered
|
||||||
|
work results from an entity transaction, each party to that
|
||||||
|
transaction who receives a copy of the work also receives whatever
|
||||||
|
licenses to the work the party's predecessor in interest had or could
|
||||||
|
give under the previous paragraph, plus a right to possession of the
|
||||||
|
Corresponding Source of the work from the predecessor in interest, if
|
||||||
|
the predecessor has it or can get it with reasonable efforts.
|
||||||
|
|
||||||
|
You may not impose any further restrictions on the exercise of the
|
||||||
|
rights granted or affirmed under this License. For example, you may
|
||||||
|
not impose a license fee, royalty, or other charge for exercise of
|
||||||
|
rights granted under this License, and you may not initiate litigation
|
||||||
|
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||||
|
any patent claim is infringed by making, using, selling, offering for
|
||||||
|
sale, or importing the Program or any portion of it.
|
||||||
|
|
||||||
|
11. Patents.
|
||||||
|
|
||||||
|
A "contributor" is a copyright holder who authorizes use under this
|
||||||
|
License of the Program or a work on which the Program is based. The
|
||||||
|
work thus licensed is called the contributor's "contributor version".
|
||||||
|
|
||||||
|
A contributor's "essential patent claims" are all patent claims
|
||||||
|
owned or controlled by the contributor, whether already acquired or
|
||||||
|
hereafter acquired, that would be infringed by some manner, permitted
|
||||||
|
by this License, of making, using, or selling its contributor version,
|
||||||
|
but do not include claims that would be infringed only as a
|
||||||
|
consequence of further modification of the contributor version. For
|
||||||
|
purposes of this definition, "control" includes the right to grant
|
||||||
|
patent sublicenses in a manner consistent with the requirements of
|
||||||
|
this License.
|
||||||
|
|
||||||
|
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||||
|
patent license under the contributor's essential patent claims, to
|
||||||
|
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||||
|
propagate the contents of its contributor version.
|
||||||
|
|
||||||
|
In the following three paragraphs, a "patent license" is any express
|
||||||
|
agreement or commitment, however denominated, not to enforce a patent
|
||||||
|
(such as an express permission to practice a patent or covenant not to
|
||||||
|
sue for patent infringement). To "grant" such a patent license to a
|
||||||
|
party means to make such an agreement or commitment not to enforce a
|
||||||
|
patent against the party.
|
||||||
|
|
||||||
|
If you convey a covered work, knowingly relying on a patent license,
|
||||||
|
and the Corresponding Source of the work is not available for anyone
|
||||||
|
to copy, free of charge and under the terms of this License, through a
|
||||||
|
publicly available network server or other readily accessible means,
|
||||||
|
then you must either (1) cause the Corresponding Source to be so
|
||||||
|
available, or (2) arrange to deprive yourself of the benefit of the
|
||||||
|
patent license for this particular work, or (3) arrange, in a manner
|
||||||
|
consistent with the requirements of this License, to extend the patent
|
||||||
|
license to downstream recipients. "Knowingly relying" means you have
|
||||||
|
actual knowledge that, but for the patent license, your conveying the
|
||||||
|
covered work in a country, or your recipient's use of the covered work
|
||||||
|
in a country, would infringe one or more identifiable patents in that
|
||||||
|
country that you have reason to believe are valid.
|
||||||
|
|
||||||
|
If, pursuant to or in connection with a single transaction or
|
||||||
|
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||||
|
covered work, and grant a patent license to some of the parties
|
||||||
|
receiving the covered work authorizing them to use, propagate, modify
|
||||||
|
or convey a specific copy of the covered work, then the patent license
|
||||||
|
you grant is automatically extended to all recipients of the covered
|
||||||
|
work and works based on it.
|
||||||
|
|
||||||
|
A patent license is "discriminatory" if it does not include within
|
||||||
|
the scope of its coverage, prohibits the exercise of, or is
|
||||||
|
conditioned on the non-exercise of one or more of the rights that are
|
||||||
|
specifically granted under this License. You may not convey a covered
|
||||||
|
work if you are a party to an arrangement with a third party that is
|
||||||
|
in the business of distributing software, under which you make payment
|
||||||
|
to the third party based on the extent of your activity of conveying
|
||||||
|
the work, and under which the third party grants, to any of the
|
||||||
|
parties who would receive the covered work from you, a discriminatory
|
||||||
|
patent license (a) in connection with copies of the covered work
|
||||||
|
conveyed by you (or copies made from those copies), or (b) primarily
|
||||||
|
for and in connection with specific products or compilations that
|
||||||
|
contain the covered work, unless you entered into that arrangement,
|
||||||
|
or that patent license was granted, prior to 28 March 2007.
|
||||||
|
|
||||||
|
Nothing in this License shall be construed as excluding or limiting
|
||||||
|
any implied license or other defenses to infringement that may
|
||||||
|
otherwise be available to you under applicable patent law.
|
||||||
|
|
||||||
|
12. No Surrender of Others' Freedom.
|
||||||
|
|
||||||
|
If conditions are imposed on you (whether by court order, agreement or
|
||||||
|
otherwise) that contradict the conditions of this License, they do not
|
||||||
|
excuse you from the conditions of this License. If you cannot convey a
|
||||||
|
covered work so as to satisfy simultaneously your obligations under this
|
||||||
|
License and any other pertinent obligations, then as a consequence you may
|
||||||
|
not convey it at all. For example, if you agree to terms that obligate you
|
||||||
|
to collect a royalty for further conveying from those to whom you convey
|
||||||
|
the Program, the only way you could satisfy both those terms and this
|
||||||
|
License would be to refrain entirely from conveying the Program.
|
||||||
|
|
||||||
|
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||||
|
|
||||||
|
Notwithstanding any other provision of this License, if you modify the
|
||||||
|
Program, your modified version must prominently offer all users
|
||||||
|
interacting with it remotely through a computer network (if your version
|
||||||
|
supports such interaction) an opportunity to receive the Corresponding
|
||||||
|
Source of your version by providing access to the Corresponding Source
|
||||||
|
from a network server at no charge, through some standard or customary
|
||||||
|
means of facilitating copying of software. This Corresponding Source
|
||||||
|
shall include the Corresponding Source for any work covered by version 3
|
||||||
|
of the GNU General Public License that is incorporated pursuant to the
|
||||||
|
following paragraph.
|
||||||
|
|
||||||
|
Notwithstanding any other provision of this License, you have
|
||||||
|
permission to link or combine any covered work with a work licensed
|
||||||
|
under version 3 of the GNU General Public License into a single
|
||||||
|
combined work, and to convey the resulting work. The terms of this
|
||||||
|
License will continue to apply to the part which is the covered work,
|
||||||
|
but the work with which it is combined will remain governed by version
|
||||||
|
3 of the GNU General Public License.
|
||||||
|
|
||||||
|
14. Revised Versions of this License.
|
||||||
|
|
||||||
|
The Free Software Foundation may publish revised and/or new versions of
|
||||||
|
the GNU Affero General Public License from time to time. Such new versions
|
||||||
|
will be similar in spirit to the present version, but may differ in detail to
|
||||||
|
address new problems or concerns.
|
||||||
|
|
||||||
|
Each version is given a distinguishing version number. If the
|
||||||
|
Program specifies that a certain numbered version of the GNU Affero General
|
||||||
|
Public License "or any later version" applies to it, you have the
|
||||||
|
option of following the terms and conditions either of that numbered
|
||||||
|
version or of any later version published by the Free Software
|
||||||
|
Foundation. If the Program does not specify a version number of the
|
||||||
|
GNU Affero General Public License, you may choose any version ever published
|
||||||
|
by the Free Software Foundation.
|
||||||
|
|
||||||
|
If the Program specifies that a proxy can decide which future
|
||||||
|
versions of the GNU Affero General Public License can be used, that proxy's
|
||||||
|
public statement of acceptance of a version permanently authorizes you
|
||||||
|
to choose that version for the Program.
|
||||||
|
|
||||||
|
Later license versions may give you additional or different
|
||||||
|
permissions. However, no additional obligations are imposed on any
|
||||||
|
author or copyright holder as a result of your choosing to follow a
|
||||||
|
later version.
|
||||||
|
|
||||||
|
15. Disclaimer of Warranty.
|
||||||
|
|
||||||
|
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||||
|
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||||
|
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||||
|
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||||
|
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||||
|
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||||
|
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||||
|
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||||
|
|
||||||
|
16. Limitation of Liability.
|
||||||
|
|
||||||
|
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||||
|
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||||
|
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||||
|
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||||
|
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||||
|
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||||
|
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||||
|
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||||
|
SUCH DAMAGES.
|
||||||
|
|
||||||
|
17. Interpretation of Sections 15 and 16.
|
||||||
|
|
||||||
|
If the disclaimer of warranty and limitation of liability provided
|
||||||
|
above cannot be given local legal effect according to their terms,
|
||||||
|
reviewing courts shall apply local law that most closely approximates
|
||||||
|
an absolute waiver of all civil liability in connection with the
|
||||||
|
Program, unless a warranty or assumption of liability accompanies a
|
||||||
|
copy of the Program in return for a fee.
|
||||||
|
|
||||||
|
END OF TERMS AND CONDITIONS
|
||||||
|
|
||||||
|
How to Apply These Terms to Your New Programs
|
||||||
|
|
||||||
|
If you develop a new program, and you want it to be of the greatest
|
||||||
|
possible use to the public, the best way to achieve this is to make it
|
||||||
|
free software which everyone can redistribute and change under these terms.
|
||||||
|
|
||||||
|
To do so, attach the following notices to the program. It is safest
|
||||||
|
to attach them to the start of each source file to most effectively
|
||||||
|
state the exclusion of warranty; and each file should have at least
|
||||||
|
the "copyright" line and a pointer to where the full notice is found.
|
||||||
|
|
||||||
|
<one line to give the program's name and a brief idea of what it does.>
|
||||||
|
Copyright (C) <year> <name of author>
|
||||||
|
|
||||||
|
This program is free software: you can redistribute it and/or modify
|
||||||
|
it under the terms of the GNU Affero General Public License as published
|
||||||
|
by the Free Software Foundation, either version 3 of the License, or
|
||||||
|
(at your option) any later version.
|
||||||
|
|
||||||
|
This program is distributed in the hope that it will be useful,
|
||||||
|
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
|
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
|
GNU Affero General Public License for more details.
|
||||||
|
|
||||||
|
You should have received a copy of the GNU Affero General Public License
|
||||||
|
along with this program. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
|
Also add information on how to contact you by electronic and paper mail.
|
||||||
|
|
||||||
|
If your software can interact with users remotely through a computer
|
||||||
|
network, you should also make sure that it provides a way for users to
|
||||||
|
get its source. For example, if your program is a web application, its
|
||||||
|
interface could display a "Source" link that leads users to an archive
|
||||||
|
of the code. There are many ways you could offer source, and different
|
||||||
|
solutions will be better for different programs; see section 13 for the
|
||||||
|
specific requirements.
|
||||||
|
|
||||||
|
You should also get your employer (if you work as a programmer) or school,
|
||||||
|
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||||
|
For more information on this, and how to apply and follow the GNU AGPL, see
|
||||||
|
<http://www.gnu.org/licenses/>.
|
||||||
8
NAMESPACE
Normal file
8
NAMESPACE
Normal file
@ -0,0 +1,8 @@
|
|||||||
|
# Generated by roxygen2: do not edit by hand
|
||||||
|
|
||||||
|
export(bpcm)
|
||||||
|
export(pcm)
|
||||||
|
export(res_ij)
|
||||||
|
export(residif)
|
||||||
|
import(rjags)
|
||||||
|
import(vcrpart)
|
||||||
416
R/bpcm.R
Normal file
416
R/bpcm.R
Normal file
@ -0,0 +1,416 @@
|
|||||||
|
## File Name: bpcm.R
|
||||||
|
## File version: 1.0
|
||||||
|
|
||||||
|
|
||||||
|
#' Compute Bayesian Partial Credit Model (BPCM) for polytomous and dichotomous items
|
||||||
|
#'
|
||||||
|
#' This function computes a bayesian PCM, potentially accounting for DIF on specified items
|
||||||
|
#'
|
||||||
|
#' @param df data.frame containing the response data
|
||||||
|
#' @param grp vector containing the column where an optional group membership variable is stored in df
|
||||||
|
#' @param is.dif indicator vector containing 1 at the inded of each DIF item in df and 0 otherwise
|
||||||
|
#' @param is.unif indicator vector containing 1 at the inded of each uniform DIF item in df and 0 otherwise
|
||||||
|
#' @param prior prior function to be used
|
||||||
|
#' @param diag.plots boolean indicating whether the JAGS diagnosis plots should be displayed
|
||||||
|
#' @return A data.frame containing various model outputs
|
||||||
|
#' @import rjags
|
||||||
|
#' @export
|
||||||
|
|
||||||
|
bpcm <- function(df=NULL,grp=NULL,is.dif=NULL,is.unif=NULL,priors=NULL,param=list(),verbose=T,diag.plots=F) {
|
||||||
|
|
||||||
|
##### Detecting errors
|
||||||
|
if ( (sum(is.dif)!=0 | sum(is.unif)!=0) & any(is.null(grp))) {
|
||||||
|
stop('ERROR: no group variable provided, but is.dif or is.unif are not NULL')
|
||||||
|
}
|
||||||
|
if ( sum(is.dif)==0 & sum(is.unif)!=0) {
|
||||||
|
warning('WARNING: no DIF item specified, but is.unif is not NULL. Ignoring is.unif')
|
||||||
|
}
|
||||||
|
if ( sum(is.dif)!=0 ) {
|
||||||
|
if (length(is.dif)!=ncol(df)) {
|
||||||
|
stop('ERROR: is.dif must have one element per item in df')
|
||||||
|
}
|
||||||
|
if (length(is.unif)!=ncol(df)) {
|
||||||
|
stop('ERROR: is.unif must have one element per item in df')
|
||||||
|
}
|
||||||
|
if ( !("m.gamma"%in%names(priors)) ) {
|
||||||
|
stop('ERROR: DIF requested. Please provide mean of prior gamma distribution as priors$m.gamma')
|
||||||
|
}
|
||||||
|
if ( !("s.gamma"%in%names(priors)) ) {
|
||||||
|
stop('ERROR: DIF requested. Please provide SD of prior gamma distribution as priors$s.gamma')
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if ( !any(is.null(grp)) ) {
|
||||||
|
if ( !("m.beta"%in%names(priors)) ) {
|
||||||
|
stop('ERROR: Group effect requested. Please provide mean of prior beta distribution as priors$m.beta')
|
||||||
|
}
|
||||||
|
if ( !("s.gamma"%in%names(priors)) ) {
|
||||||
|
stop('ERROR: Group effect requested. Please provide SD of prior beta distribution as priors$s.beta')
|
||||||
|
}
|
||||||
|
if (nrow(df)!=length(grp)) {
|
||||||
|
stop('ERROR: grp must be of length nrow(df)')
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if ( !("m.delta"%in%names(priors)) ) {
|
||||||
|
stop('ERROR: Please provide mean of prior delta distribution as priors$m.delta')
|
||||||
|
}
|
||||||
|
if ( !("s.delta"%in%names(priors)) ) {
|
||||||
|
stop('ERROR: Please provide SD of prior delta distribution as priors$s.delta')
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
if (verbose) {
|
||||||
|
cat('\n')
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
cat("######################################### FITTING MODEL #########################################\n")
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
}
|
||||||
|
|
||||||
|
###############################################
|
||||||
|
############### NO GROUP EFFECT ###############
|
||||||
|
###############################################
|
||||||
|
if (is.null(grp)) {
|
||||||
|
nam <- colnames(df)
|
||||||
|
nam_o <- nam
|
||||||
|
Y <- matrix(unlist(df),nrow=nrow(df))+1
|
||||||
|
n <- nrow(Y)
|
||||||
|
p <- ncol(Y)
|
||||||
|
K <- apply(Y,2,max,na.rm=TRUE)
|
||||||
|
m.delta <- priors$m.delta
|
||||||
|
s.delta <- priors$s.delta
|
||||||
|
|
||||||
|
namlist <- c(sapply(1:p, function(x) paste0("delta[",x,',',paste0(1:K[p],"]"))))
|
||||||
|
if (sum(is.dif)!=0) {
|
||||||
|
namlist <- c(namlist,sapply(1:p, function(x) paste0("gamma[",x,',',paste0(1:K[p],"]"))),"beta")
|
||||||
|
} else {
|
||||||
|
if (!is.null(grp)) {
|
||||||
|
namlist <- c(namlist,"beta")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
data <- list(Y=Y,n=n,p=p,K=K,m.delta=m.delta,s.delta=s.delta)
|
||||||
|
params <- c("delta")
|
||||||
|
|
||||||
|
|
||||||
|
if ("n.burn"%in%names(param)) {
|
||||||
|
n.burn <- param$n.burn
|
||||||
|
} else {
|
||||||
|
n.burn <- 4000
|
||||||
|
}
|
||||||
|
if ("n.thin"%in%names(param)) {
|
||||||
|
n.thin <- param$n.thin
|
||||||
|
} else {
|
||||||
|
n.thin <- 20
|
||||||
|
}
|
||||||
|
if ("n.chains"%in%names(param)) {
|
||||||
|
n.chains <- param$n.chains
|
||||||
|
} else {
|
||||||
|
n.chains <- 2
|
||||||
|
}
|
||||||
|
if ("n.sim"%in%names(param)) {
|
||||||
|
n.sim <- param$n.sim
|
||||||
|
} else {
|
||||||
|
n.sim <- 20000
|
||||||
|
}
|
||||||
|
if ("nb.cores"%in%names(param)) {
|
||||||
|
nb.cores <- param$nb.cores
|
||||||
|
} else {
|
||||||
|
nb.cores <- 6
|
||||||
|
}
|
||||||
|
cl <- parallel::makeForkCluster(nb.cores)
|
||||||
|
|
||||||
|
if (verbose) {
|
||||||
|
cat('Initializing Chains...')
|
||||||
|
}
|
||||||
|
dclone::parJagsModel(cl,name="mod.jags",data = data,inits = NULL,file = "/home/corentin/Documents/These/Recherche/SPT/src/bpcm",n.chains=n.chains)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
cat('Applying burn-in iterations...')
|
||||||
|
}
|
||||||
|
dclone::parUpdate(cl,object="mod.jags",n.burn=n.burn)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
cat('Running Markov Chains...')
|
||||||
|
}
|
||||||
|
mod.samples <- dclone::parCodaSamples(cl,model="mod.jags",variable.names=params,n.iter = n.sim,thin = n.thin)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
}
|
||||||
|
parallel::stopCluster(cl)
|
||||||
|
if (diag.plots) {
|
||||||
|
cat("Displaying traceplots...")
|
||||||
|
traceplot(mod.samples)
|
||||||
|
readline(prompt="Use the arrows to navigate between traceplots. Press [enter] to continue")
|
||||||
|
cat("DONE\n")
|
||||||
|
cat("Displaying autocorrelation plots...")
|
||||||
|
autocorr.plot(mod.samples,ask=F)
|
||||||
|
readline(prompt="Use the arrows to navigate between autocorr plots. Press [enter] to continue")
|
||||||
|
cat("DONE\n ")
|
||||||
|
}
|
||||||
|
res <- mod.samples[[1]]
|
||||||
|
res <- as.data.frame(res)[,namlist]
|
||||||
|
namlist2 <- unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1))))
|
||||||
|
if (sum(is.dif)!=0) {
|
||||||
|
namlist2 <- c(namlist2,unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1,":grp")))),"beta")
|
||||||
|
} else {
|
||||||
|
if (!is.null(grp)) {
|
||||||
|
namlist2 <- c(namlist2,"beta")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
colnames(res) <- namlist2
|
||||||
|
res <- res[,namlist2]
|
||||||
|
res <- res[,apply(res,2,function(x) all(x==0))==0]
|
||||||
|
xsi <- apply(res,2,function(x) c(mean(x),sd(x),quantile(x,0.05),quantile(x,0.95)) )
|
||||||
|
rownames(xsi) <- c("post.mean","post.sd","post.90.cred.low","post.90.cred.high")
|
||||||
|
xsi <- round(t(xsi),4)
|
||||||
|
if ("beta" %in% rownames(xsi)) {
|
||||||
|
beta <- xsi[rownames(xsi)=="beta",]
|
||||||
|
xsi <- xsi[rownames(xsi)!="beta",]
|
||||||
|
}
|
||||||
|
out <- list(mcmc.res=res,
|
||||||
|
dif.items=nam_o[which(is.dif==1)],
|
||||||
|
beta=beta,
|
||||||
|
thresholds=xsi)
|
||||||
|
|
||||||
|
|
||||||
|
#####################################################
|
||||||
|
############### GROUP EFFECT / NO DIF ###############
|
||||||
|
#####################################################
|
||||||
|
} else if (is.null(is.dif) | sum(is.dif)==0) {
|
||||||
|
nam <- colnames(df)
|
||||||
|
nam_o <- nam
|
||||||
|
Y <- matrix(unlist(df),nrow=nrow(df))+1
|
||||||
|
Z <- matrix(unlist(grp),nrow=length(grp))
|
||||||
|
n <- nrow(Y)
|
||||||
|
p <- ncol(Y)
|
||||||
|
K <- apply(Y,2,max,na.rm=TRUE)
|
||||||
|
m.delta <- priors$m.delta
|
||||||
|
s.delta <- priors$s.delta
|
||||||
|
m.beta <- priors$m.beta
|
||||||
|
s.beta <- priors$s.beta
|
||||||
|
|
||||||
|
namlist <- c(sapply(1:p, function(x) paste0("delta[",x,',',paste0(1:K[p],"]"))))
|
||||||
|
if (sum(is.dif)!=0) {
|
||||||
|
namlist <- c(namlist,sapply(1:p, function(x) paste0("gamma[",x,',',paste0(1:K[p],"]"))),"beta")
|
||||||
|
} else {
|
||||||
|
if (!is.null(grp)) {
|
||||||
|
namlist <- c(namlist,"beta")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
data <- list(Y=Y,Z=Z,n=n,p=p,K=K,m.beta=m.beta,s.beta=s.beta,m.delta=m.delta,s.delta=s.delta)
|
||||||
|
params <- c("delta","beta")
|
||||||
|
|
||||||
|
|
||||||
|
if ("n.burn"%in%names(param)) {
|
||||||
|
n.burn <- param$n.burn
|
||||||
|
} else {
|
||||||
|
n.burn <- 4000
|
||||||
|
}
|
||||||
|
if ("n.thin"%in%names(param)) {
|
||||||
|
n.thin <- param$n.thin
|
||||||
|
} else {
|
||||||
|
n.thin <- 20
|
||||||
|
}
|
||||||
|
if ("n.chains"%in%names(param)) {
|
||||||
|
n.chains <- param$n.chains
|
||||||
|
} else {
|
||||||
|
n.chains <- 2
|
||||||
|
}
|
||||||
|
if ("n.sim"%in%names(param)) {
|
||||||
|
n.sim <- param$n.sim
|
||||||
|
} else {
|
||||||
|
n.sim <- 20000
|
||||||
|
}
|
||||||
|
if ("nb.cores"%in%names(param)) {
|
||||||
|
nb.cores <- param$nb.cores
|
||||||
|
} else {
|
||||||
|
nb.cores <- 6
|
||||||
|
}
|
||||||
|
cl <- parallel::makeForkCluster(nb.cores)
|
||||||
|
|
||||||
|
if (verbose) {
|
||||||
|
cat('Initializing Chains...')
|
||||||
|
}
|
||||||
|
dclone::parJagsModel(cl,name="mod.jags",data = data,inits = NULL,file = "/home/corentin/Documents/These/Recherche/SPT/src/bpcm_beta",n.chains=n.chains)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
cat('Applying burn-in iterations...')
|
||||||
|
}
|
||||||
|
dclone::parUpdate(cl,object="mod.jags",n.burn=n.burn)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
cat('Running Markov Chains...')
|
||||||
|
}
|
||||||
|
mod.samples <- dclone::parCodaSamples(cl,model="mod.jags",variable.names=params,n.iter = n.sim,thin = n.thin)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
}
|
||||||
|
parallel::stopCluster(cl)
|
||||||
|
if (diag.plots) {
|
||||||
|
cat("Displaying traceplots...")
|
||||||
|
traceplot(mod.samples)
|
||||||
|
readline(prompt="Use the arrows to navigate between traceplots. Press [enter] to continue")
|
||||||
|
cat("DONE\n")
|
||||||
|
cat("Displaying autocorrelation plots...")
|
||||||
|
autocorr.plot(mod.samples,ask=F)
|
||||||
|
readline(prompt="Use the arrows to navigate between autocorr plots. Press [enter] to continue")
|
||||||
|
cat("DONE\n ")
|
||||||
|
}
|
||||||
|
res <- mod.samples[[1]]
|
||||||
|
res <- as.data.frame(res)[,namlist]
|
||||||
|
namlist2 <- unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1))))
|
||||||
|
if (sum(is.dif)!=0) {
|
||||||
|
namlist2 <- c(namlist2,unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1,":grp")))),"beta")
|
||||||
|
} else {
|
||||||
|
if (!is.null(grp)) {
|
||||||
|
namlist2 <- c(namlist2,"beta")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
colnames(res) <- namlist2
|
||||||
|
res <- res[,namlist2]
|
||||||
|
res <- res[,apply(res,2,function(x) all(x==0))==0]
|
||||||
|
xsi <- apply(res,2,function(x) c(mean(x),sd(x),quantile(x,0.05),quantile(x,0.95)) )
|
||||||
|
rownames(xsi) <- c("post.mean","post.sd","post.90.cred.low","post.90.cred.high")
|
||||||
|
xsi <- round(t(xsi),4)
|
||||||
|
if ("beta" %in% rownames(xsi)) {
|
||||||
|
beta <- xsi[rownames(xsi)=="beta",]
|
||||||
|
xsi <- xsi[rownames(xsi)!="beta",]
|
||||||
|
}
|
||||||
|
out <- list(mcmc.res=res,
|
||||||
|
dif.items=nam_o[which(is.dif==1)],
|
||||||
|
beta=beta,
|
||||||
|
thresholds=xsi)
|
||||||
|
|
||||||
|
#####################################################
|
||||||
|
############### GROUP EFFECT / NO DIF ###############
|
||||||
|
#####################################################
|
||||||
|
} else {
|
||||||
|
|
||||||
|
|
||||||
|
nam <- colnames(df)
|
||||||
|
nam_o <- nam
|
||||||
|
Y <- matrix(unlist(df),nrow=nrow(df))+1
|
||||||
|
Z <- matrix(unlist(grp),nrow=length(grp))
|
||||||
|
n <- nrow(Y)
|
||||||
|
p <- ncol(Y)
|
||||||
|
|
||||||
|
pnodif <- p-sum(is.dif)
|
||||||
|
pnodif1 <- p-sum(is.dif)+1
|
||||||
|
pdif <- sum(is.dif)
|
||||||
|
pnounif <- pnodif+pdif-sum(is.unif)
|
||||||
|
pnounif1 <- pnodif+pdif-sum(is.unif)+1
|
||||||
|
|
||||||
|
K <- apply(Y,2,max,na.rm=TRUE)
|
||||||
|
m.delta <- priors$m.delta
|
||||||
|
s.delta <- priors$s.delta
|
||||||
|
m.beta <- priors$m.beta
|
||||||
|
s.beta <- priors$s.beta
|
||||||
|
|
||||||
|
m.gamma <- priors$m.gamma
|
||||||
|
s.gamma <- priors$s.gamma
|
||||||
|
|
||||||
|
namlist <- c(sapply(1:p, function(x) paste0("delta[",x,',',paste0(1:K[p],"]"))))
|
||||||
|
if (sum(is.dif)!=0) {
|
||||||
|
namlist <- c(namlist,sapply(1:p, function(x) paste0("gamma[",x,',',paste0(1:K[p],"]"))),"beta")
|
||||||
|
} else {
|
||||||
|
if (!is.null(grp)) {
|
||||||
|
namlist <- c(namlist,"beta")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
Y <- Y[,c(which(is.dif+is.unif==0),which(is.dif+is.unif==1),which(is.dif+is.unif==2))]
|
||||||
|
nam <- nam[c(which(is.dif+is.unif==0),which(is.dif+is.unif==1),which(is.dif+is.unif==2))]
|
||||||
|
|
||||||
|
data <- list(Y=Y,Z=Z,n=n,p=p,pnounif=pnounif,pnounif1=pnounif1,pdif=pdif,pnodif1=pnodif1,pnodif=pnodif,K=K,m.beta=m.beta,s.beta=s.beta,m.gamma=m.gamma,s.gamma=s.gamma,m.delta=m.delta,s.delta=s.delta,difff=as.factor(is.dif),unif=as.factor(is.unif))
|
||||||
|
params <- c("delta","gamma","beta")
|
||||||
|
|
||||||
|
|
||||||
|
if ("n.burn"%in%names(param)) {
|
||||||
|
n.burn <- param$n.burn
|
||||||
|
} else {
|
||||||
|
n.burn <- 4000
|
||||||
|
}
|
||||||
|
if ("n.thin"%in%names(param)) {
|
||||||
|
n.thin <- param$n.thin
|
||||||
|
} else {
|
||||||
|
n.thin <- 20
|
||||||
|
}
|
||||||
|
if ("n.chains"%in%names(param)) {
|
||||||
|
n.chains <- param$n.chains
|
||||||
|
} else {
|
||||||
|
n.chains <- 2
|
||||||
|
}
|
||||||
|
if ("n.sim"%in%names(param)) {
|
||||||
|
n.sim <- param$n.sim
|
||||||
|
} else {
|
||||||
|
n.sim <- 20000
|
||||||
|
}
|
||||||
|
if ("nb.cores"%in%names(param)) {
|
||||||
|
nb.cores <- param$nb.cores
|
||||||
|
} else {
|
||||||
|
nb.cores <- 6
|
||||||
|
}
|
||||||
|
cl <- parallel::makeForkCluster(nb.cores)
|
||||||
|
|
||||||
|
|
||||||
|
if (verbose) {
|
||||||
|
cat('Initializing Chains...')
|
||||||
|
}
|
||||||
|
dclone::parJagsModel(cl,name="mod.jags",data = data,inits = NULL,file = "/home/corentin/Documents/These/Recherche/SPT/src/bpcm_dif",n.chains=n.chains)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
cat('Applying burn-in iterations...')
|
||||||
|
}
|
||||||
|
dclone::parUpdate(cl,object="mod.jags",n.burn=n.burn)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
cat('Running Markov Chains...')
|
||||||
|
}
|
||||||
|
mod.samples <- dclone::parCodaSamples(cl,model="mod.jags",variable.names=params,n.iter = n.sim,thin = n.thin)
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE \n')
|
||||||
|
}
|
||||||
|
parallel::stopCluster(cl)
|
||||||
|
if (diag.plots) {
|
||||||
|
cat("Displaying traceplots...")
|
||||||
|
traceplot(mod.samples)
|
||||||
|
readline(prompt="Use the arrows to navigate between traceplots. Press [enter] to continue")
|
||||||
|
cat("DONE\n")
|
||||||
|
cat("Displaying autocorrelation plots...")
|
||||||
|
autocorr.plot(mod.samples,ask=F)
|
||||||
|
readline(prompt="Use the arrows to navigate between autocorr plots. Press [enter] to continue")
|
||||||
|
cat("DONE\n ")
|
||||||
|
}
|
||||||
|
res <- mod.samples[[1]]
|
||||||
|
res <- as.data.frame(res)[,namlist]
|
||||||
|
namlist2 <- unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1))))
|
||||||
|
if (sum(is.dif)!=0) {
|
||||||
|
namlist2 <- c(namlist2,unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1,":grp")))),"beta")
|
||||||
|
} else {
|
||||||
|
if (!is.null(grp)) {
|
||||||
|
namlist2 <- c(namlist2,"beta")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
colnames(res) <- namlist2
|
||||||
|
res <- res[,namlist2]
|
||||||
|
res <- res[,apply(res,2,function(x) all(x==0))==0]
|
||||||
|
xsi <- apply(res,2,function(x) c(mean(x),sd(x),quantile(x,0.05),quantile(x,0.95)) )
|
||||||
|
rownames(xsi) <- c("post.mean","post.sd","post.90.cred.low","post.90.cred.high")
|
||||||
|
xsi <- round(t(xsi),4)
|
||||||
|
if ("beta" %in% rownames(xsi)) {
|
||||||
|
beta <- xsi[rownames(xsi)=="beta",]
|
||||||
|
if (is.null(grp)) {
|
||||||
|
beta <- NA
|
||||||
|
}
|
||||||
|
xsi <- xsi[rownames(xsi)!="beta",]
|
||||||
|
}
|
||||||
|
out <- list(mcmc.res=res,
|
||||||
|
dif.items=nam_o[which(is.dif==1)],
|
||||||
|
beta=beta,
|
||||||
|
thresholds=xsi)
|
||||||
|
}
|
||||||
|
if (is.null(dim(out$beta))) {
|
||||||
|
out$beta <- NA
|
||||||
|
}
|
||||||
|
return(out)
|
||||||
|
}
|
||||||
236
R/pcm.R
Normal file
236
R/pcm.R
Normal file
@ -0,0 +1,236 @@
|
|||||||
|
## File Name: pcm.R
|
||||||
|
## File version: 1.0
|
||||||
|
|
||||||
|
#' Compute Partial Credit Model (PCM) for polytomous and dichotomous items
|
||||||
|
#'
|
||||||
|
#' This function computes a frequentist PCM, potentially accounting for DIF on specified items
|
||||||
|
#'
|
||||||
|
#' @param df data.frame containing the data
|
||||||
|
#' @param items vector containing the names of columns where item responses are stored in df
|
||||||
|
#' @param grp string containing the name of the column where an optional group membership variable is stored in df
|
||||||
|
#' @param dif.items vector containing the list of indexes in "items" corresponding to dif items
|
||||||
|
#' @param type.dif vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF
|
||||||
|
#' @return A data.frame containing various model outputs
|
||||||
|
#' @import vcrpart
|
||||||
|
#' @export
|
||||||
|
|
||||||
|
pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,verbose=T,fit="ucminf") {
|
||||||
|
##### Detecting errors
|
||||||
|
|
||||||
|
if (any(!(items %in% colnames(df)))) {
|
||||||
|
stop("ERROR: provided item name does not exist in df")
|
||||||
|
}
|
||||||
|
if (any(!(grp %in% colnames(df)))) {
|
||||||
|
stop("ERROR: provided group variable name does not exist in df")
|
||||||
|
}
|
||||||
|
if (any(!is.null(grp))) {
|
||||||
|
if (any(!(grp%in%colnames(df)))) {
|
||||||
|
stop("ERROR: group name does not exist in df")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (!is.null(dif.items) & length(dif.items)!=length(type.dif)) {
|
||||||
|
stop('ERROR: type.dif is not the same length as dif.items')
|
||||||
|
}
|
||||||
|
if (!is.null(dif.items) & is.null(type.dif)) {
|
||||||
|
warning("WARNING: no type.dif provided, assuming non-homogeneous DIF on all items")
|
||||||
|
}
|
||||||
|
if (!("id"%in%colnames(df))) {
|
||||||
|
stop('ERROR: no column named id provided')
|
||||||
|
}
|
||||||
|
if ( any(apply(df[df[,grp]==0,items],2,max)<max(df[,items])) | any(apply(df[df[,grp]==1,items],2,max)<max(df[,items])) ) {
|
||||||
|
if (fit=="ucminf") {
|
||||||
|
fit <- "optim"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
##### Analysis
|
||||||
|
restab.diftype <- NULL
|
||||||
|
se.beta <- NULL
|
||||||
|
beta.ci <- NULL
|
||||||
|
beta.p <- NULL
|
||||||
|
nbitems <- length(items)
|
||||||
|
items_o <- items
|
||||||
|
colnames(df)[which(colnames(df)%in%items_o)] <- paste0("item",1:nbitems)
|
||||||
|
items <- paste0("item",1:nbitems)
|
||||||
|
# If no group
|
||||||
|
if (is.null(grp)) {
|
||||||
|
if (verbose) {
|
||||||
|
cat('\n')
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
cat("######################################### FITTING MODEL #########################################\n")
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
}
|
||||||
|
grp <- NULL
|
||||||
|
# prepare data
|
||||||
|
df <- df[,c('id',items)]
|
||||||
|
print(df)
|
||||||
|
colnames(df)[2:(length(colnames(df)))] <- paste0("item",seq(1,length(colnames(df))-1))
|
||||||
|
df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
|
||||||
|
colnames(df.long) <- c("id","item","resp")
|
||||||
|
nbitems <- length(2:(length(colnames(df))))
|
||||||
|
maxmod <- max(df[,2:(length(colnames(df)))])
|
||||||
|
df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-1),ordered = F)
|
||||||
|
df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
|
||||||
|
df.long$id <- factor(df.long$id)
|
||||||
|
# fit pcm
|
||||||
|
mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"))
|
||||||
|
comod <- coef(mod)
|
||||||
|
# output results
|
||||||
|
restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-1,nbitems)]))
|
||||||
|
rownames(restab) <- paste0("item",1:nbitems)
|
||||||
|
colnames(restab) <- paste0("delta_",1:maxmod)
|
||||||
|
restab.dif <- NULL
|
||||||
|
beta <- NULL
|
||||||
|
}
|
||||||
|
# If group
|
||||||
|
else {
|
||||||
|
grp <- df[,grp]
|
||||||
|
df$grp <- grp
|
||||||
|
|
||||||
|
# If group and DIF
|
||||||
|
if (!is.null(dif.items)) {
|
||||||
|
if (verbose) {
|
||||||
|
cat('\n')
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
cat("######################################### FITTING MODEL #########################################\n")
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
}
|
||||||
|
# prepare data
|
||||||
|
df <- df[,c('id',items,"grp")]
|
||||||
|
colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2))
|
||||||
|
df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
|
||||||
|
colnames(df.long) <- c("id","grp","item","resp")
|
||||||
|
nbitems <- length(2:(length(colnames(df))-1))
|
||||||
|
maxmod <- max(df[,2:(length(colnames(df))-1)])
|
||||||
|
df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
|
||||||
|
df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
|
||||||
|
df.long$id <- factor(df.long$id)
|
||||||
|
|
||||||
|
# Create 1 dif column per dif item
|
||||||
|
for (i in 1:length(dif.items)) {
|
||||||
|
df.long[,paste0("dif",i)] <- ifelse(df.long$item==dif.items[i],1,0)
|
||||||
|
}
|
||||||
|
difvar <- sapply(1:length(dif.items),function(x) paste0("dif",x))
|
||||||
|
difvar.unif <- difvar[type.dif==1]
|
||||||
|
difvar.nonunif <- difvar[type.dif==0]
|
||||||
|
# fit pcm
|
||||||
|
formudif <- paste0("resp ~ 0 + ge(grp",ifelse(length(difvar.unif>0),"+",""),ifelse(length(difvar.unif>0),paste0(difvar.unif,":grp",collapse="+"),""),")+ce(item",ifelse(length(difvar.nonunif>0),"+",""),ifelse(length(difvar.nonunif)>0,paste0(difvar.nonunif,":grp",collapse="+"),""),")+re(0|id)")
|
||||||
|
formudif <- as.formula(formudif)
|
||||||
|
mod <- olmm(formudif,data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
|
||||||
|
comod <- coef(mod)
|
||||||
|
# output results
|
||||||
|
nbcoef <- nbitems+length(difvar.nonunif)
|
||||||
|
restab <- t(sapply(1:nbcoef,function(x) comod[seq(x,length(comod)-2-length(difvar.unif),nbitems+length(difvar.nonunif))]))
|
||||||
|
difcoef.unif <- NULL
|
||||||
|
if (length(difvar.unif)>0) {
|
||||||
|
difcoef.unif <- comod[(length(comod)-length(difvar.unif)):(length(comod)-1)]
|
||||||
|
if (length(difvar.unif)!=1) {
|
||||||
|
difcoef.unif <- as.matrix(difcoef.unif)
|
||||||
|
} else {
|
||||||
|
difcoef.unif <- t(as.matrix(difcoef.unif))
|
||||||
|
}
|
||||||
|
rname <- paste0("item",dif.items[type.dif==1])
|
||||||
|
rownames(difcoef.unif) <- paste0("dif.",items_o[which(items%in%rname)])
|
||||||
|
colnames(difcoef.unif) <- "gamma"
|
||||||
|
difcoef.unif <- as.data.frame(difcoef.unif)
|
||||||
|
for (k in 1:maxmod) {
|
||||||
|
difcoef.unif[,paste0("gamma_",k)] <- difcoef.unif[,"gamma"]
|
||||||
|
}
|
||||||
|
difcoef.unif <- as.matrix(difcoef.unif[,2:ncol(difcoef.unif)])
|
||||||
|
}
|
||||||
|
difcoef.nonunif <- NULL
|
||||||
|
if (length(difvar.nonunif)>0) {
|
||||||
|
difcoef.nonunif <- restab[nbitems+c(1:length(difvar.nonunif)),]
|
||||||
|
if (length(difvar.nonunif)==1) {
|
||||||
|
difcoef.nonunif <- t(as.matrix(difcoef.nonunif))
|
||||||
|
} else {
|
||||||
|
difcoef.nonunif <- as.matrix(difcoef.nonunif)
|
||||||
|
}
|
||||||
|
rname <- paste0("item",dif.items[type.dif==0])
|
||||||
|
rownames(difcoef.nonunif) <- paste0("dif.",items_o[which(items%in%rname)])
|
||||||
|
colnames(difcoef.nonunif) <- paste0("gamma_",1:maxmod)
|
||||||
|
}
|
||||||
|
restab <- restab[1:nbitems,]
|
||||||
|
rownames(restab) <- items_o
|
||||||
|
colnames(restab) <- paste0("delta_",1:maxmod)
|
||||||
|
restab.dif <- rbind(difcoef.nonunif,difcoef.unif)
|
||||||
|
restab.diftype <- matrix(ifelse(type.dif==1,"HOMOGENEOUS","NON-HOMOGENEOUS"))
|
||||||
|
restab.diftype <- noquote(restab.diftype)
|
||||||
|
rownames(restab.diftype) <- rownames(restab.dif)
|
||||||
|
colnames(restab.diftype) <- "dif.type"
|
||||||
|
beta <- comod["grp"]
|
||||||
|
se.beta <- (confint(mod)["grp",2]-beta)/1.96
|
||||||
|
beta.ci <- confint(mod)["grp",]
|
||||||
|
beta.p <- 2*pnorm(-abs(beta/se.beta))
|
||||||
|
beta <- as.numeric(beta)
|
||||||
|
se.beta <- as.numeric(se.beta)
|
||||||
|
beta.p <- as.numeric(beta.p)
|
||||||
|
beta <- -1*beta
|
||||||
|
beta.ci <- -1*c(beta.ci[2],beta.ci[1])
|
||||||
|
|
||||||
|
} else {
|
||||||
|
# If group no DIF
|
||||||
|
if (verbose) {
|
||||||
|
cat('\n')
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
cat("######################################### FITTING MODEL #########################################\n")
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
}
|
||||||
|
# prepare data
|
||||||
|
df <- df[,c('id',items,"grp")]
|
||||||
|
colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2))
|
||||||
|
df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
|
||||||
|
colnames(df.long) <- c("id","grp","item","resp")
|
||||||
|
nbitems <- length(2:(length(colnames(df))-1))
|
||||||
|
maxmod <- max(df[,2:(length(colnames(df))-1)])
|
||||||
|
df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
|
||||||
|
df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
|
||||||
|
df.long$id <- factor(df.long$id)
|
||||||
|
# fit pcm
|
||||||
|
mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
|
||||||
|
comod <- coef(mod)
|
||||||
|
# output results
|
||||||
|
restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-2,nbitems)]))
|
||||||
|
rownames(restab) <- items_o
|
||||||
|
colnames(restab) <- paste0("delta_",1:maxmod)
|
||||||
|
restab.dif <- NULL
|
||||||
|
beta <- comod[length(comod)-1]
|
||||||
|
se.beta <- (confint(mod)["grp",2]-beta)/1.96
|
||||||
|
beta.ci <- confint(mod)["grp",]
|
||||||
|
beta.p <- 2*pnorm(-abs(beta/se.beta))
|
||||||
|
beta <- as.numeric(beta)
|
||||||
|
se.beta <- as.numeric(se.beta)
|
||||||
|
beta.p <- as.numeric(beta.p)
|
||||||
|
beta <- -1*beta
|
||||||
|
beta.ci <- -1*c(beta.ci[2],beta.ci[1])
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
theta <- -1*ranef(mod,norm=F)+ifelse(grp==1,beta,0)
|
||||||
|
resid <- apply(matrix(1:nbitems,ncol=length(nbitems)),1, function(k) sapply(1:nrow(df), function(j) res_ij(theta[j],restab[k,],df[j,items[k]],beta=0)))
|
||||||
|
colnames(resid) <- items_o
|
||||||
|
|
||||||
|
##### Output
|
||||||
|
if (verbose) {
|
||||||
|
cat(paste0('Number of individuals: ',nrow(df),"\n"))
|
||||||
|
cat(paste0('Number of items: ',length(items),"\n"))
|
||||||
|
cat(paste0('Item Thresholds and DIF parameters: ',"\n"))
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
out <- list(
|
||||||
|
beta=beta,
|
||||||
|
beta.se=se.beta,
|
||||||
|
beta.ci=beta.ci,
|
||||||
|
beta.p=beta.p,
|
||||||
|
dif.items=dif.items,
|
||||||
|
dif.type=restab.diftype,
|
||||||
|
thresholds=restab,
|
||||||
|
dif.param=restab.dif,
|
||||||
|
theta=theta,
|
||||||
|
residuals=resid
|
||||||
|
)
|
||||||
|
return(out)
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
37
R/res_ij.R
Normal file
37
R/res_ij.R
Normal file
@ -0,0 +1,37 @@
|
|||||||
|
## File Name: res_ij.R
|
||||||
|
## File version: 1.0
|
||||||
|
|
||||||
|
#' Compute rasch person-item residuals
|
||||||
|
#'
|
||||||
|
#' This function computes person-item residuals for rasch family adjacent-categories models
|
||||||
|
#' like the Rasch model or the PCM
|
||||||
|
#'
|
||||||
|
#' @param thetahat_i Person parameter estimate for person i
|
||||||
|
#' @param delta_hat_j Item threshold parameter estimates for item j
|
||||||
|
#' @param res Response of person i to item j
|
||||||
|
#' @param beta Estimated latent mean difference between groups, can be ignored
|
||||||
|
#' @return A single scalar equal to the residual for person i on item j
|
||||||
|
#' @import vcrpart
|
||||||
|
#' @export
|
||||||
|
|
||||||
|
res_ij <- function(thetahat_i,delta_hat_j,res,beta=0){
|
||||||
|
var=0
|
||||||
|
denomin=1
|
||||||
|
for (i in 1:length(delta_hat_j)) {
|
||||||
|
|
||||||
|
denomin=denomin+exp(i*(thetahat_i+beta)-sum(delta_hat_j[1:i]))
|
||||||
|
}
|
||||||
|
posterior=c(1)
|
||||||
|
for (i in 1:length(delta_hat_j)) {
|
||||||
|
posterior=c(posterior,exp(i*(thetahat_i+beta)-sum(delta_hat_j[1:i])))
|
||||||
|
}
|
||||||
|
|
||||||
|
posterior=posterior/denomin
|
||||||
|
|
||||||
|
for(i in 0:length(delta_hat_j)){
|
||||||
|
|
||||||
|
var=var+((i-sum(posterior*0:length(delta_hat_j)))**2)*posterior[i+1]
|
||||||
|
}
|
||||||
|
return((res-sum(posterior*0:length(delta_hat_j)))/sqrt(var))
|
||||||
|
|
||||||
|
}
|
||||||
117
R/residif.R
Normal file
117
R/residif.R
Normal file
@ -0,0 +1,117 @@
|
|||||||
|
## File Name: residif.R
|
||||||
|
## File version: 1.0
|
||||||
|
|
||||||
|
#' RESIDIF procedure for DIF detection as per Andrich and Hagquist (2015)
|
||||||
|
#'
|
||||||
|
#' This function detects DIF on PCM items using ANOVA of person-item residuals
|
||||||
|
#'
|
||||||
|
#' @param df data.frame containing the data
|
||||||
|
#' @param items vector containing the names of columns where item responses are stored in df
|
||||||
|
#' @param grp vector containing the name of the column where an optional group membership variable is stored in df
|
||||||
|
#' @return A data.frame containing a column listing the detected DIF item and another listing detected DIF forms
|
||||||
|
#' @import vcrpart
|
||||||
|
#' @export
|
||||||
|
|
||||||
|
residif <- function(df=NULL,items=NULL,grp=NULL,verbose=T) {
|
||||||
|
if (any(!(items %in% colnames(df)))) {
|
||||||
|
stop("ERROR: provided item name does not exist in df")
|
||||||
|
}
|
||||||
|
if (any(!(grp %in% colnames(df)))) {
|
||||||
|
stop("ERROR: provided grp variable name does not exist in df")
|
||||||
|
}
|
||||||
|
if (any(is.null(grp))) {
|
||||||
|
stop("ERROR: no grp variable provided")
|
||||||
|
}
|
||||||
|
if (any(is.null(items))) {
|
||||||
|
stop("ERROR: no items provided")
|
||||||
|
}
|
||||||
|
|
||||||
|
maxcat <- max(df[,items])
|
||||||
|
nbitems <- length(items)
|
||||||
|
nbitems_o <- nbitems
|
||||||
|
|
||||||
|
if (verbose) {
|
||||||
|
cat('\n')
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
cat("##################################### COMPUTING INITIAL PCM #####################################\n")
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
}
|
||||||
|
startt <- Sys.time()
|
||||||
|
pcm_initial <- pcm(df = df,items = items,grp = grp,verbose=F)
|
||||||
|
dat <- df
|
||||||
|
dat$score <- rowSums(dat[,items])
|
||||||
|
nqt <- ifelse(length(unique(quantile(dat$score,seq(0,1,0.2))))==6,5,length(unique(quantile(dat$score,seq(0,1,0.2))))-1)
|
||||||
|
while (length(unique(quantile(dat$score,seq(0,1,1/nqt))))!=nqt+1) {
|
||||||
|
nqt <- nqt-1
|
||||||
|
}
|
||||||
|
dat$score_q5 <- cut(dat$score,unique(quantile(dat$score,seq(0,1,1/nqt))),labels=1:nqt,include.lowest=T)
|
||||||
|
res.anova <- rep(NA,nbitems)
|
||||||
|
pval <- rep(NA,nbitems)
|
||||||
|
fval <- rep(NA,nbitems)
|
||||||
|
for (i in 1:nbitems) {
|
||||||
|
dat[,paste0('res_',i)] <- pcm_initial$residuals[,i]
|
||||||
|
res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~get(grp)*score_q5,data=dat))
|
||||||
|
pval[c(i,i+nbitems)] <- c(res.anova[[i]][1,"Pr(>F)"],res.anova[[i]][3,"Pr(>F)"])
|
||||||
|
fval[c(i,i+nbitems)] <- c(res.anova[[i]][1,'F value'],res.anova[[i]][3,"F value"])
|
||||||
|
}
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE\n')
|
||||||
|
cat('#################################################################################################\n')
|
||||||
|
}
|
||||||
|
res.items <- c()
|
||||||
|
res.uniform <- c()
|
||||||
|
resp <- df[,items]
|
||||||
|
k <- 1
|
||||||
|
while(any(pval<0.05/(nbitems_o*3))) {
|
||||||
|
k <- k+1
|
||||||
|
if (verbose) {
|
||||||
|
cat(paste("######################################## COMPUTING STEP",k,"#######################################\n"))
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
}
|
||||||
|
numitem <- ifelse(which.max(fval)%%(length(fval)/2)!=0,which.max(fval)%%(length(fval)/2),length(fval)/2)
|
||||||
|
res.item <- gsub("[a-z]", "",colnames(resp)[numitem])
|
||||||
|
res.items <- c(res.items,res.item)
|
||||||
|
res.uni <- res.anova[[numitem]][3,"Pr(>F)"]>0.05
|
||||||
|
res.uniform <- c(res.uniform,res.uni)
|
||||||
|
pcm_while <- pcm(df = df,items = items,grp = grp,dif.items = res.items,type.dif = res.uniform,verbose=F)
|
||||||
|
res.anova <- rep(NA,nbitems)
|
||||||
|
pval <- rep(NA,nbitems_o)
|
||||||
|
fval <- rep(NA,nbitems_o)
|
||||||
|
numitems <- 1:nbitems_o
|
||||||
|
numitems <- numitems[-which(numitems%in%res.items)]
|
||||||
|
for (i in numitems) {
|
||||||
|
dat[,paste0('res_',i)] <- pcm_while$residuals[,i]
|
||||||
|
res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~dat[,grp]*score_q5,data=dat))
|
||||||
|
pval[c(i,i+nbitems)] <- c(res.anova[[i]][1,"Pr(>F)"],res.anova[[i]][3,"Pr(>F)"])
|
||||||
|
fval[c(i,i+nbitems)] <- c(res.anova[[i]][1,'F value'],res.anova[[i]][3,"F value"])
|
||||||
|
}
|
||||||
|
for (i in 1:nbitems_o) {
|
||||||
|
pval[i] <- ifelse(is.na(pval[i]),999,pval[i])
|
||||||
|
fval[i] <- ifelse(is.na(fval[i]),-999,fval[i])
|
||||||
|
}
|
||||||
|
if (verbose) {
|
||||||
|
cat('DONE\n')
|
||||||
|
if (any(pval<0.05/(nbitems_o*3))) {
|
||||||
|
cat('#################################################################################################\n')
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
endt <- Sys.time()
|
||||||
|
cat(paste(c('Algorithm ran for',round(endt-startt,4),"seconds\n")))
|
||||||
|
if (verbose) {
|
||||||
|
cat('#################################################################################################\n')
|
||||||
|
cat("###################################### DETECTED DIF ITEMS #######################################\n")
|
||||||
|
cat("#################################################################################################\n")
|
||||||
|
}
|
||||||
|
if (length(res.items>0)) {
|
||||||
|
results <- data.frame(dif.items=res.items,
|
||||||
|
uniform=ifelse(res.uniform==1,TRUE,FALSE))
|
||||||
|
return(results)
|
||||||
|
}
|
||||||
|
else {
|
||||||
|
if (verbose) {
|
||||||
|
cat("No DIF was detected\n")
|
||||||
|
}
|
||||||
|
return(NULL)
|
||||||
|
}
|
||||||
|
}
|
||||||
20
SPT.Rproj
Normal file
20
SPT.Rproj
Normal file
@ -0,0 +1,20 @@
|
|||||||
|
Version: 1.0
|
||||||
|
|
||||||
|
RestoreWorkspace: Default
|
||||||
|
SaveWorkspace: Default
|
||||||
|
AlwaysSaveHistory: Default
|
||||||
|
|
||||||
|
EnableCodeIndexing: Yes
|
||||||
|
UseSpacesForTab: Yes
|
||||||
|
NumSpacesForTab: 2
|
||||||
|
Encoding: UTF-8
|
||||||
|
|
||||||
|
RnwWeave: Sweave
|
||||||
|
LaTeX: pdfLaTeX
|
||||||
|
|
||||||
|
AutoAppendNewline: Yes
|
||||||
|
StripTrailingWhitespace: Yes
|
||||||
|
|
||||||
|
BuildType: Package
|
||||||
|
PackageUseDevtools: Yes
|
||||||
|
PackageInstallArgs: --no-multiarch --with-keep.source
|
||||||
36
man/bpcm.Rd
Normal file
36
man/bpcm.Rd
Normal file
@ -0,0 +1,36 @@
|
|||||||
|
% Generated by roxygen2: do not edit by hand
|
||||||
|
% Please edit documentation in R/bpcm.R
|
||||||
|
\name{bpcm}
|
||||||
|
\alias{bpcm}
|
||||||
|
\title{Compute Bayesian Partial Credit Model (BPCM) for polytomous and dichotomous items}
|
||||||
|
\usage{
|
||||||
|
bpcm(
|
||||||
|
df = NULL,
|
||||||
|
grp = NULL,
|
||||||
|
is.dif = NULL,
|
||||||
|
is.unif = NULL,
|
||||||
|
priors = NULL,
|
||||||
|
param = list(),
|
||||||
|
verbose = T,
|
||||||
|
diag.plots = F
|
||||||
|
)
|
||||||
|
}
|
||||||
|
\arguments{
|
||||||
|
\item{df}{data.frame containing the response data}
|
||||||
|
|
||||||
|
\item{grp}{vector containing the column where an optional group membership variable is stored in df}
|
||||||
|
|
||||||
|
\item{is.dif}{indicator vector containing 1 at the inded of each DIF item in df and 0 otherwise}
|
||||||
|
|
||||||
|
\item{is.unif}{indicator vector containing 1 at the inded of each uniform DIF item in df and 0 otherwise}
|
||||||
|
|
||||||
|
\item{diag.plots}{boolean indicating whether the JAGS diagnosis plots should be displayed}
|
||||||
|
|
||||||
|
\item{prior}{prior function to be used}
|
||||||
|
}
|
||||||
|
\value{
|
||||||
|
A data.frame containing various model outputs
|
||||||
|
}
|
||||||
|
\description{
|
||||||
|
This function computes a bayesian PCM, potentially accounting for DIF on specified items
|
||||||
|
}
|
||||||
33
man/pcm.Rd
Normal file
33
man/pcm.Rd
Normal file
@ -0,0 +1,33 @@
|
|||||||
|
% Generated by roxygen2: do not edit by hand
|
||||||
|
% Please edit documentation in R/pcm.R
|
||||||
|
\name{pcm}
|
||||||
|
\alias{pcm}
|
||||||
|
\title{Compute Partial Credit Model (PCM) for polytomous and dichotomous items}
|
||||||
|
\usage{
|
||||||
|
pcm(
|
||||||
|
df = NULL,
|
||||||
|
items = NULL,
|
||||||
|
grp = NULL,
|
||||||
|
dif.items = NULL,
|
||||||
|
type.dif = NULL,
|
||||||
|
verbose = T,
|
||||||
|
fit = "ucminf"
|
||||||
|
)
|
||||||
|
}
|
||||||
|
\arguments{
|
||||||
|
\item{df}{data.frame containing the data}
|
||||||
|
|
||||||
|
\item{items}{vector containing the names of columns where item responses are stored in df}
|
||||||
|
|
||||||
|
\item{grp}{string containing the name of the column where an optional group membership variable is stored in df}
|
||||||
|
|
||||||
|
\item{dif.items}{vector containing the list of indexes in "items" corresponding to dif items}
|
||||||
|
|
||||||
|
\item{type.dif}{vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF}
|
||||||
|
}
|
||||||
|
\value{
|
||||||
|
A data.frame containing various model outputs
|
||||||
|
}
|
||||||
|
\description{
|
||||||
|
This function computes a frequentist PCM, potentially accounting for DIF on specified items
|
||||||
|
}
|
||||||
24
man/res_ij.Rd
Normal file
24
man/res_ij.Rd
Normal file
@ -0,0 +1,24 @@
|
|||||||
|
% Generated by roxygen2: do not edit by hand
|
||||||
|
% Please edit documentation in R/res_ij.R
|
||||||
|
\name{res_ij}
|
||||||
|
\alias{res_ij}
|
||||||
|
\title{Compute rasch person-item residuals}
|
||||||
|
\usage{
|
||||||
|
res_ij(thetahat_i, delta_hat_j, res, beta = 0)
|
||||||
|
}
|
||||||
|
\arguments{
|
||||||
|
\item{thetahat_i}{Person parameter estimate for person i}
|
||||||
|
|
||||||
|
\item{delta_hat_j}{Item threshold parameter estimates for item j}
|
||||||
|
|
||||||
|
\item{res}{Response of person i to item j}
|
||||||
|
|
||||||
|
\item{beta}{Estimated latent mean difference between groups, can be ignored}
|
||||||
|
}
|
||||||
|
\value{
|
||||||
|
A single scalar equal to the residual for person i on item j
|
||||||
|
}
|
||||||
|
\description{
|
||||||
|
This function computes person-item residuals for rasch family adjacent-categories models
|
||||||
|
like the Rasch model or the PCM
|
||||||
|
}
|
||||||
21
man/residif.Rd
Normal file
21
man/residif.Rd
Normal file
@ -0,0 +1,21 @@
|
|||||||
|
% Generated by roxygen2: do not edit by hand
|
||||||
|
% Please edit documentation in R/residif.R
|
||||||
|
\name{residif}
|
||||||
|
\alias{residif}
|
||||||
|
\title{RESIDIF procedure for DIF detection as per Andrich and Hagquist (2015)}
|
||||||
|
\usage{
|
||||||
|
residif(df = NULL, items = NULL, grp = NULL, verbose = T)
|
||||||
|
}
|
||||||
|
\arguments{
|
||||||
|
\item{df}{data.frame containing the data}
|
||||||
|
|
||||||
|
\item{items}{vector containing the names of columns where item responses are stored in df}
|
||||||
|
|
||||||
|
\item{grp}{vector containing the name of the column where an optional group membership variable is stored in df}
|
||||||
|
}
|
||||||
|
\value{
|
||||||
|
A data.frame containing a column listing the detected DIF item and another listing detected DIF forms
|
||||||
|
}
|
||||||
|
\description{
|
||||||
|
This function detects DIF on PCM items using ANOVA of person-item residuals
|
||||||
|
}
|
||||||
23
src/bpcm
Normal file
23
src/bpcm
Normal file
@ -0,0 +1,23 @@
|
|||||||
|
model {
|
||||||
|
for (i in 1:n) {
|
||||||
|
for (j in 1:p) {
|
||||||
|
Y[i,j] ~ dcat(prob[i,j,1:K[j]])
|
||||||
|
}
|
||||||
|
theta[i] ~ dnorm(0,1)
|
||||||
|
}
|
||||||
|
for (i in 1:n) {
|
||||||
|
for (j in 1:p) {
|
||||||
|
for (k in 1:K[j] ) {
|
||||||
|
eta[i,j,k] <- theta[i] - delta[j,k]
|
||||||
|
psum[i,j,k] <- sum(eta[i,j,1:k])
|
||||||
|
exp.psum[i,j,k] <- exp(psum[i,j,k])
|
||||||
|
prob[i,j,k] <- exp.psum[i,j,k] / sum(exp.psum[i,j,1:K[j]])
|
||||||
|
} } }
|
||||||
|
for (j in 1:p) {
|
||||||
|
delta[j,1] <- 0.0
|
||||||
|
for (k in 2:K[j]) {
|
||||||
|
delta[j,k] ~ dnorm(m.delta, pr.delta)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
pr.delta <- pow(s.delta, -2)
|
||||||
|
}
|
||||||
25
src/bpcm_beta
Normal file
25
src/bpcm_beta
Normal file
@ -0,0 +1,25 @@
|
|||||||
|
model {
|
||||||
|
for (i in 1:n) {
|
||||||
|
for (j in 1:p) {
|
||||||
|
Y[i,j] ~ dcat(prob[i,j,1:K[j]])
|
||||||
|
}
|
||||||
|
theta[i] ~ dnorm(0,1)
|
||||||
|
}
|
||||||
|
for (i in 1:n) {
|
||||||
|
for (j in 1:p) {
|
||||||
|
for (k in 1:K[j] ) {
|
||||||
|
eta[i,j,k] <- theta[i] + (beta * Z[i,1]) - delta[j,k]
|
||||||
|
psum[i,j,k] <- sum(eta[i,j,1:k])
|
||||||
|
exp.psum[i,j,k] <- exp(psum[i,j,k])
|
||||||
|
prob[i,j,k] <- exp.psum[i,j,k] / sum(exp.psum[i,j,1:K[j]])
|
||||||
|
} } }
|
||||||
|
for (j in 1:p) {
|
||||||
|
delta[j,1] <- 0.0
|
||||||
|
for (k in 2:K[j]) {
|
||||||
|
delta[j,k] ~ dnorm(m.delta, pr.delta)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
beta ~ dnorm(m.beta,pr.beta)
|
||||||
|
pr.delta <- pow(s.delta, -2)
|
||||||
|
pr.beta <- pow(s.beta,-2)
|
||||||
|
}
|
||||||
46
src/bpcm_dif
Normal file
46
src/bpcm_dif
Normal file
@ -0,0 +1,46 @@
|
|||||||
|
model {
|
||||||
|
for (i in 1:n) {
|
||||||
|
for (j in 1:p) {
|
||||||
|
Y[i,j] ~ dcat(prob[i,j,1:K[j]])
|
||||||
|
}
|
||||||
|
theta[i] ~ dnorm(0, 1)
|
||||||
|
}
|
||||||
|
for (i in 1:n) {
|
||||||
|
for (j in 1:p) {
|
||||||
|
for (k in 1:K[j] ) {
|
||||||
|
eta[i,j,k] <- theta[i] + (beta * Z[i,1]) - ( delta[j,k] + Z[i,1]*ifelse(k==1,0,gamma[j,k]) )
|
||||||
|
psum[i,j,k] <- sum(eta[i,j,1:k])
|
||||||
|
exp.psum[i,j,k] <- exp(psum[i,j,k])
|
||||||
|
prob[i,j,k] <- exp.psum[i,j,k] / sum(exp.psum[i,j,1:K[j]])
|
||||||
|
} } }
|
||||||
|
for (j in 1:pnodif) {
|
||||||
|
delta[j,1] <- 0.0
|
||||||
|
gamma[j,1] <- 0.0
|
||||||
|
for (k in 2:K[j]) {
|
||||||
|
delta[j,k] ~ dnorm(m.delta, pr.delta)
|
||||||
|
gamma[j,k] <- 0.0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
for (j in pnodif1:pnounif) {
|
||||||
|
delta[j,1] <- 0.0
|
||||||
|
gamma[j,1] <- 0.0
|
||||||
|
for (k in 2:K[j]) {
|
||||||
|
delta[j,k] ~ dnorm(m.delta, pr.delta)
|
||||||
|
gamma[j,k] ~ dnorm(m.gamma,s.gamma)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
for (j in pnounif1:p) {
|
||||||
|
delta[j,1] <- 0.0
|
||||||
|
gamma[j,1] <- 0.0
|
||||||
|
gamma[j,2] ~ dnorm(m.gamma,pr.gamma)
|
||||||
|
delta[j,2] ~ dnorm(m.delta, pr.delta)
|
||||||
|
for (k in 3:K[j]) {
|
||||||
|
delta[j,k] ~ dnorm(m.delta, pr.delta)
|
||||||
|
gamma[j,k] <- gamma[j,2]
|
||||||
|
}
|
||||||
|
}
|
||||||
|
beta ~ dnorm(m.beta,pr.beta)
|
||||||
|
pr.delta <- pow(s.delta, -2)
|
||||||
|
pr.beta <- pow(s.beta,-2)
|
||||||
|
pr.gamma <- pow(s.gamma,-2)
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user