R2conquest: Running ConQuest From Within R

Description

The function R2conquest runs the IRT software ConQuest (Wu, Adams, Wilson & Haldane, 2007) from within R. Other functions are utility functions for reading item parameters, plausible values or person-item maps.

Usage

R2conquest(dat, path.conquest, conquest.name="console", converge=0.001, 
    deviancechange=1e-04, iter=800, nodes=20, show.conquestoutput=FALSE, 
    name="rasch", pid=1:(nrow(dat)), wgt=NULL, X=NULL, set.constraints=NULL, 
    model="item", regression=NULL, itemcodes=seq(0,max(dat,na.rm=TRUE)), 
    constraints=NULL, digits=5, onlysyntax=FALSE, qmatrix=NULL, import.regression=NULL, 
    anchor.regression=NULL, anchor.covariance=NULL, pv=TRUE, designmatrix=NULL, 
    only.calibration=FALSE, init_parameters=NULL, n_plausible=10,  persons.elim=TRUE, 
    est.wle=TRUE, save.bat=TRUE , use.bat=FALSE , read.output=TRUE , ignore.pid=FALSE)
    
rasch.conquest(dat, path.conquest, conquest.name="console", converge=0.001, 
    deviancechange=1e-04, iter=800, nodes=20, show.conquestoutput=FALSE, 
    name="rasch", pid=1:(nrow(dat)), wgt=NULL, X=NULL, set.constraints=NULL, 
    model="item", regression=NULL, itemcodes=seq(0,max(dat,na.rm=TRUE)), 
    constraints=NULL, digits=5, onlysyntax=FALSE, qmatrix=NULL, import.regression=NULL, 
    anchor.regression=NULL, anchor.covariance=NULL, pv=TRUE, designmatrix=NULL, 
    only.calibration=FALSE, init_parameters=NULL, n_plausible=10,  persons.elim=TRUE, 
    est.wle=TRUE, save.bat=TRUE , use.bat=FALSE , read.output=TRUE, ignore.pid=FALSE)

## S3 method for class 'R2conquest':
summary(object, \dots)    
## S3 method for class 'rasch.conquest':
summary(object, \dots)    

# read all terms in a show file or only some terms 
read.show(showfile)
read.show.term(showfile, term)

# read regression parameters in a show file
read.show.regression(showfile)

# read unidimensional plausible values form a pv file
read.pv(pvfile, npv = 5)
# read multidimensional plausible values
read.multidimpv(pvfile, ndim, npv = 5)

# read person-item map
read.pimap(showfile)

Arguments

dat

Data frame of item responses

path.conquest

Directory where the ConQuest executable file is located

conquest.name

Name of the ConQuest executable.

converge

Maximal change in parameters

deviancechange

Maximal change in deviance

iter

Maximum number of iterations

nodes

Number of nodes for integration

show.conquestoutput

Show ConQuest run log file on console?

name

Name of the output files. The default is 'rasch'.

pid

Person identifier

wgt

Vector of person weights

Matrix of covariates for the latent regression model (e.g. gender, socioeconomic status, ..) or for the item design (e.g. raters, booklets, ...)

set.constraints

This is the set.constraints in ConQuest. It can be "cases" (constraint for persons), "items" or "none"

model

Definition model statement. It can be for example "item+item*step" or "item+booklet+rater"

regression

The ConQuest regression statement (for example "gender+status")

itemcodes

Vector of valid codes for item responses. E.g. for partial credit data with at most 3 points it must be c(0,1,2,3).

constraints

Matrix of item parameter constraints. 1st column: Item names, 2nd column: Item parameters. It only works correctly for dichotomous data.

digits

Number of digits for covariates in the latent regression model

onlysyntax

Should only be ConQuest syntax generated?

qmatrix

Matrix of item loadings on dimensions in a multidimensional IRT model

import.regression

Name of an file with initial covariance parameters (follow the ConQuest specification rules!)

anchor.regression

Name of an file with anchored regression parameters

anchor.covariance

Name of an file with anchored covariance parameters (follow the ConQuest specification rules!)

Draw plausible values?

designmatrix

Design matrix for item parameters (see the ConQuest manual)

only.calibration

Estimate only item parameters and not person parameters (no WLEs or plausible values are estimated)?

init_parameters

Name of an file with initial item parameters (follow the ConQuest specification rules!)

n_plausible

Number of plausible values

persons.elim

Eliminate persons with only missing item responses?

est.wle

Estimate weighted likelihood estimate?

save.bat

Save bat file?

use.bat

Run ConQuest from within Rdue a direct call via the system command (use.bat=FALSE) or via a system call of a bat file in the working directory (use.bat=TRUE)

read.output

Should ConQuest output files be processed? Default is TRUE.

ignore.pid

Logical indicating whether person identifiers (pid) should be processed in ConQuest input syntax.

object

Object of class R2conquest

showfile

A ConQuest show file (shw file)

term

Name of the term to be extracted in the show file

pvfile

File with plausible values

ndim

Number of dimensions

npv

Number of plausible values

...

Further arguments to be passed

Value

A list with several entries
itemData frame with item parameters and item statistics
personData frame with person parameters
shw.itemparameterConQuest output table for item parameters
shw.regrparameterConQuest output table for regression parameters
...More values

Details

Consult the ConQuest manual (Wu et al., 2007) for specification details.

References

Wu, M. L., Adams, R. J., Wilson, M. R. & Haldane, S. (2007). ACER ConQuest Version 2.0. Mulgrave. {https://shop.acer.edu.au/acer-shop/group/CON3}.

Examples

Run this code

# define ConQuest path
path.conquest <- "C:/Conquest/"

#############################################################################
# EXAMPLE 1: Dichotomous data (data.pisaMath)
#############################################################################
library(sirt)
data(data.pisaMath)
dat <- data.pisaMath$data

# select items
items <- colnames(dat)[ which( substring( colnames(dat) , 1 , 1)=="M" ) ]

#***
# Model 11: Rasch model
mod11 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
             pid=dat$idstud , name="mod11")
summary(mod11)
# read show file
shw11 <- read.show( "mod11.shw" )
# read person-item map
pi11 <- read.pimap(showfile="mod11.shw")

#***
# Model 12: Rasch model with fixed item difficulties (from Model 1)
mod12 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
             pid=dat$idstud , constraints = mod11$item[ , c("item","itemdiff")] ,
             name="mod12")
summary(mod12)

#***
# Model 13: Latent regression model with predictors female, hisei and migra
mod13a <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
             pid=dat$idstud , X = dat[ , c("female" , "hisei" , "migra") ] ,
             name="mod13a")
summary(mod13a)

# latent regression with a subset of predictors
mod13b <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
             pid=dat$idstud , X = dat[ , c("female" , "hisei" , "migra") ] ,
             regression= "hisei migra" , name="mod13b")

#***
# Model 14: Differential item functioning (female)
mod14 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
             pid=dat$idstud , X = dat[ , c("female") , drop=FALSE] ,
             model="item+female+item*female" ,  regression="" ,  name="mod14")

#############################################################################
# EXAMPLE 2: Polytomous data (data.Students)
#############################################################################
library(CDM)
data(data.Students)
dat <- data.Students

# select items
items <- grep.vec( "act" , colnames(dat) )$x

#***
# Model 21: Partial credit model
mod21 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
              model="item+item*step" ,  name="mod21")

#***
# Model 22: Rating scale model
mod22 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
              model="item+step" , name="mod22")

#***
# Model 23: Multidimensional model
items <- grep.vec( c("act" , "sc" ) , colnames(dat) ,  "OR" )$x
qmatrix <- matrix( 0 , nrow=length(items) , 2 )
qmatrix[1:5,1] <- 1
qmatrix[6:9,2] <- 1
mod23 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
            model="item+item*step" , qmatrix=qmatrix , name="mod23")

#############################################################################
# EXAMPLE 3: Multi facet models (data.ratings1)
#############################################################################
library(sirt)
data(data.ratings1)
dat <- data.ratings1

items <- paste0("k",1:5)

# use numeric rater ID's
raters <- as.numeric( substring( paste( dat$rater ) , 3 ) )

#***
# Model 31: Rater model 'item+item*step+rater'
mod31 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
              itemcodes= 0:3 , model="item+item*step+rater" , 
              pid=dat$idstud , X=data.frame("rater"=raters)  , 
              regression="" , name="mod31")

#***
# Model 32: Rater model 'item+item*step+rater+item*rater'
mod32 <- R2conquest(dat=dat[,items] , path.conquest=path.conquest , 
              model="item+item*step+rater+item*rater" , 
              pid=dat$idstud , X=data.frame("rater"=raters)  , 
              regression="" , name="mod32")

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