Running ConQuest From Within R
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.
R2conquest(dat, path.conquest, conquest.name="console", converge=0.001,
deviancechange=1e-04, iter=800, nodes=20, minnode=-6, maxnode=6,
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, ...)
# 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)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 |
minnode |
Minimum value of discrete grid of θ nodes |
maxnode |
Maximum value of discrete grid of θ nodes |
show.conquestoutput |
Show ConQuest run log file on console? |
name |
Name of the output files. The default is |
pid |
Person identifier |
wgt |
Vector of person weights |
X |
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
|
model |
Definition model statement. It can be for example
|
regression |
The ConQuest regression statement (for example |
itemcodes |
Vector of valid codes for item responses. E.g. for partial credit
data with at most 3 points it must be |
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!) |
pv |
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 R due a direct call via the |
read.output |
Should ConQuest output files be processed? Default is |
ignore.pid |
Logical indicating whether person identifiers ( |
object |
Object of class |
showfile |
A ConQuest show 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 |
Consult the ConQuest manual (Wu et al., 2007) for specification details.
A list with several entries
item |
Data frame with item parameters and item statistics |
person |
Data frame with person parameters |
shw.itemparameter |
ConQuest output table for item parameters |
shw.regrparameter |
ConQuest output table for regression parameters |
... |
More values |
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.
See also the eat package (https://r-forge.r-project.org/projects/eat/) for elaborate functionality of using ConQuest from within R. See also the conquestr package for another R wrapper to the ConQuest software (at least version 4 of ConQuest has to be installed).
See also the TAM package for similar (and even extended) functionality for specifying item response models.
## Not run:
# define ConQuest path
path.conquest <- "C:/Conquest/"
#############################################################################
# EXAMPLE 1: Dichotomous data (data.pisaMath)
#############################################################################
library(sirt)
data(data.pisaMath)
dat <- data.pisaMath$data
# select items
items <- colnames(dat)[ which( substring( colnames(dat), 1, 1)=="M" ) ]
#***
# Model 11: Rasch model
mod11 <- sirt::R2conquest(dat=dat[,items], path.conquest=path.conquest,
pid=dat$idstud, name="mod11")
summary(mod11)
# read show file
shw11 <- sirt::read.show( "mod11.shw" )
# read person-item map
pi11 <- sirt::read.pimap(showfile="mod11.shw")
#***
# Model 12: Rasch model with fixed item difficulties (from Model 1)
mod12 <- sirt::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 <- sirt::R2conquest(dat=dat[,items], path.conquest=path.conquest,
pid=dat$idstud, X=dat[, c("female", "hisei", "migra") ],
name="mod13a")
summary(mod13a)
# latent regression with a subset of predictors
mod13b <- sirt::R2conquest(dat=dat[,items], path.conquest=path.conquest,
pid=dat$idstud, X=dat[, c("female", "hisei", "migra") ],
regression="hisei migra", name="mod13b")
#***
# Model 14: Differential item functioning (female)
mod14 <- sirt::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 <- sirt::R2conquest(dat=dat[,items], path.conquest=path.conquest,
model="item+item*step", name="mod21")
#***
# Model 22: Rating scale model
mod22 <- sirt::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" )$x
qmatrix <- matrix( 0, nrow=length(items), 2 )
qmatrix[1:5,1] <- 1
qmatrix[6:9,2] <- 1
mod23 <- sirt::R2conquest(dat=dat[,items], path.conquest=path.conquest,
model="item+item*step", qmatrix=qmatrix, name="mod23")
#############################################################################
# EXAMPLE 3: Multi facet models (data.ratings1)
#############################################################################
library(sirt)
data(data.ratings1)
dat <- data.ratings1
items <- paste0("k",1:5)
# use numeric rater ID's
raters <- as.numeric( substring( paste( dat$rater ), 3 ) )
#***
# Model 31: Rater model 'item+item*step+rater'
mod31 <- sirt::R2conquest(dat=dat[,items], path.conquest=path.conquest,
itemcodes=0:3, model="item+item*step+rater",
pid=dat$idstud, X=data.frame("rater"=raters),
regression="", name="mod31")
#***
# Model 32: Rater model 'item+item*step+rater+item*rater'
mod32 <- sirt::R2conquest(dat=dat[,items], path.conquest=path.conquest,
model="item+item*step+rater+item*rater",
pid=dat$idstud, X=data.frame("rater"=raters),
regression="", name="mod32")
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