More Datasets and Examples (Similar to ConQuest Examples)
Datasets and examples similar to the ones in the ConQuest manual (Wu, Adams, Wilson, & Haldane, 2007).
data(data.cqc01) data(data.cqc02) data(data.cqc03) data(data.cqc04) data(data.cqc05)
data.cqc01 contains 512 persons on
12 dichotomous items of following format
'data.frame': 512 obs. of 12 variables: $ BSMMA01: int 1 1 0 1 1 1 1 1 0 0 ... $ BSMMA02: int 1 0 1 1 0 1 1 1 0 0 ... $ BSMMA03: int 1 1 0 1 1 1 1 1 1 0 ... [...] $ BSMSA12: int 0 0 0 0 1 0 1 1 0 0 ...
data.cqc02 contains 431 persons on 8 polytomous
variables of following format
'data.frame': 431 obs. of 8 variables: $ It1: int 1 1 2 0 2 1 2 2 2 1 ... $ It2: int 3 0 1 2 2 3 2 2 1 1 ... $ It3: int 1 1 1 0 1 1 0 0 1 0 ... [...] $ It8: int 3 1 0 0 3 1 3 0 3 0 ...
data.cqc03 contains 11200 observations for
5600 persons, 16 raters and 2 items (crit1 and crit2)
'data.frame': 11200 obs. of 4 variables: $ pid : num 10001 10001 10002 10002 10003 ... $ rater: chr "R11" "R12" "R13" "R14" ... $ crit1: int 2 2 2 1 3 2 2 1 1 1 ... $ crit2: int 3 3 2 1 2 2 2 2 2 1 ...
data.cqc04 contains 1452 observations for 363 persons,
4 raters, 4 topics and 5 items (spe, coh, str,
gra, con)
'data.frame': 1452 obs. of 8 variables: $ pid : num 10010 10010 10010 10010 10016 ... $ rater: chr "BE" "CO" "BE" "CO" ... $ topic: chr "Spor" "Spor" "Spor" "Spor" ... $ spe : int 2 0 2 1 3 3 3 3 3 2 ... $ coh : int 1 1 2 0 3 3 3 3 3 3 ... $ str : int 0 1 3 0 3 2 3 2 3 0 ... $ gra : int 0 0 2 0 3 3 3 3 2 1 ... $ con : int 0 0 0 0 3 1 2 2 3 0 ...
data.cqc05 contains 1500 persons,
3 covariates and 157 items.
'data.frame': 1500 obs. of 160 variables: $ gender: int 1 0 1 0 0 0 0 1 0 1 ... $ level : int 0 1 1 0 0 0 1 0 1 1 ... $ gbyl : int 0 0 1 0 0 0 0 0 0 1 ... $ A001 : num 0 0 0 1 0 1 1 1 0 1 ... $ A002 : num 1 1 0 1 1 1 1 1 1 0 ... $ A003 : num 0 0 0 0 1 1 1 0 0 1 ... [...]
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 the sirt::R2conquest function
for running ConQuest software from within R.
See the WrightMap package for functions
connected to reading ConQuest files and creating Wright maps.
ConQuest output files can be read into R with the help of
the WrightMap::CQmodel function.
See also the IRT.WrightMap function in TAM.
See also the eat package (https://r-forge.r-project.org/projects/eat/) for elaborate functionality for communication of ConQuest with R.
## Not run:
library(sirt)
library(WrightMap)
# In the following, ConQuest will also be used for estimation.
path.conquest <- "C:/Conquest" # path of the ConQuest console.exe
setwd( "p:/my_files/ConQuest_analyses" ) # working directory
#############################################################################
# EXAMPLE 01: Rasch model data.cqc01
#############################################################################
data(data.cqc01)
dat <- data.cqc01
#********************************************
#*** Model 01: Estimate Rasch model
mod01 <- TAM::tam.mml(dat)
summary(mod01)
#------- ConQuest
# estimate model
cmod01 <- sirt::R2conquest( dat, name="mod01", path.conquest=path.conquest)
summary(cmod01) # summary output
# read shw file with some terms
shw01a <- sirt::read.show( "mod01.shw" )
cmod01$shw.itemparameter
# read person item maps
pi01a <- sirt::read.pimap( "mod01.shw" )
cmod01$shw.pimap
# read plausible values (npv=10 plausible values)
pv01a <- sirt::read.pv(pvfile="mod01.pv", npv=10)
cmod01$person
# read ConQuest model
res01a <- WrightMap::CQmodel(p.est="mod01.wle", show="mod01.shw", p.type="WLE" )
print(res01a)
# plot item fit
WrightMap::fitgraph(res01a)
# Wright map
plot(res01a, label.items.srt=90 )
#############################################################################
# EXAMPLE 02: Partial credit model and rating scale model data.cqc02
#############################################################################
data(data.cqc02)
dat <- data.cqc02
#********************************************
# Model 02a: Partial credit model in ConQuest parametrization 'item+item*step'
mod02a <- TAM::tam.mml( dat, irtmodel="PCM2" )
summary(mod02a, file="mod02a")
fit02a <- TAM::tam.fit(mod02a)
summary(fit02a)
#--- ConQuest
# estimate model
maxK <- max( dat, na.rm=TRUE )
cmod02a <- sirt::R2conquest( dat, itemcodes=0:maxK, model="item+item*step",
name="mod02a", path.conquest=path.conquest)
summary(cmod02a) # summary output
# read ConQuest model
res02a <- WrightMap::CQmodel(p.est="mod02a.wle", show="mod02a.shw", p.type="WLE" )
print(res02a)
# Wright map
plot(res02a, label.items.srt=90 )
plot(res02a, item.table="item")
#********************************************
# Model 02b: Rating scale model
mod02b <- TAM::tam.mml( dat, irtmodel="RSM" )
summary( mod02b )
#############################################################################
# EXAMPLE 03: Faceted Rasch model for rating data data.cqc03
#############################################################################
data(data.cqc03)
# select items
resp <- data.cqc03[, c("crit1","crit2") ]
#********************************************
# Model 03a: 'item+step+rater'
mod03a <- TAM::tam.mml.mfr( resp, facets=data.cqc03[,"rater",drop=FALSE],
formulaA=~ item+step+rater, pid=data.cqc03$pid )
summary( mod03a )
#--- ConQuest
X <- data.cqc03[,"rater",drop=FALSE]
X$rater <- as.numeric(substring( X$rater, 2 )) # convert 'rater' in numeric format
maxK <- max( resp, na.rm=TRUE)
cmod03a <- sirt::R2conquest( resp, X=X, regression="", model="item+step+rater",
name="mod03a", path.conquest=path.conquest, set.constraints="cases" )
summary(cmod03a) # summary output
# read ConQuest model
res03a <- WrightMap::CQmodel(p.est="mod03a.wle", show="mod03a.shw", p.type="WLE" )
print(res03a)
# Wright map
plot(res03a)
#********************************************
# Model 03b: 'item:step+rater'
mod03b <- TAM::tam.mml.mfr( resp, facets=data.cqc03[,"rater",drop=FALSE],
formulaA=~ item + item:step+rater, pid=data.cqc03$pid )
summary( mod03b )
#********************************************
# Model 03c: 'step+rater' for first item 'crit1'
# Restructuring the data is necessary.
# Define raters as items in the new dataset 'dat1'.
persons <- unique( data.cqc03$pid )
raters <- unique( data.cqc03$rater )
dat1 <- matrix( NA, nrow=length(persons), ncol=length(raters) + 1 )
dat1 <- as.data.frame(dat1)
colnames(dat1) <- c("pid", raters )
dat1$pid <- persons
for (rr in raters){
dat1.rr <- data.cqc03[ data.cqc03$rater==rr, ]
dat1[ match(dat1.rr$pid, persons),rr] <- dat1.rr$crit1
}
## > head(dat1)
## pid R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26
## 1 10001 2 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 10002 NA NA 2 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 10003 NA NA 3 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 10004 NA NA 2 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 10005 NA NA 1 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 10006 NA NA 1 1 NA NA NA NA NA NA NA NA NA NA NA NA
# estimate model 03c
mod03c <- TAM::tam.mml( dat1[,-1], pid=dat1$pid )
summary( mod03c )
#############################################################################
# EXAMPLE 04: Faceted Rasch model for rating data data.cqc04
#############################################################################
data(data.cqc04)
resp <- data.cqc04[,4:8]
facets <- data.cqc04[, c("rater", "topic") ]
#********************************************
# Model 04a: 'item*step+rater+topic'
formulaA <- ~ item*step + rater + topic
mod04a <- TAM::tam.mml.mfr( resp, facets=facets,
formulaA=formulaA, pid=data.cqc04$pid )
summary( mod04a )
#********************************************
# Model 04b: 'item*step+rater+topic+item*rater+item*topic'
formulaA <- ~ item*step + rater + topic + item*rater + item*topic
mod04b <- TAM::tam.mml.mfr( resp, facets=facets,
formulaA=formulaA, pid=data.cqc04$pid )
summary( mod04b )
#********************************************
# Model 04c: 'item*step' with fixing rater and topic parameters to zero
formulaA <- ~ item*step + rater + topic
mod04c0 <- TAM::tam.mml.mfr( resp, facets=facets,
formulaA=formulaA, pid=data.cqc04$pid, control=list(maxiter=4) )
summary( mod04c0 )
# fix rater and topic parameter to zero
xsi.est <- mod04c0$xsi
xsi.fixed <- cbind( seq(1,nrow(xsi.est)), xsi.est$xsi )
rownames(xsi.fixed) <- rownames(xsi.est)
xsi.fixed <- xsi.fixed[ c(8:13),]
xsi.fixed[,2] <- 0
## > xsi.fixed
## [,1] [,2]
## raterAM 8 0
## raterBE 9 0
## raterCO 10 0
## topicFami 11 0
## topicScho 12 0
## topicSpor 13 0
mod04c1 <- TAM::tam.mml.mfr( resp, facets=facets,
formulaA=formulaA, pid=data.cqc04$pid, xsi.fixed=xsi.fixed )
summary( mod04c1 )
#############################################################################
# EXAMPLE 05: Partial credit model with latent regression and
# plausible value imputation
#############################################################################
data(data.cqc05)
resp <- data.cqc05[, -c(1:3) ] # select item responses
#********************************************
# Model 05a: Partial credit model
mod05a <-tam.mml(resp=resp, irtmodel="PCM2" )
#********************************************
# Model 05b: Partial credit model with latent regressors
mod05b <-tam.mml(resp=resp, irtmodel="PCM2", Y=data.cqc05[,1:3] )
# Plausible value imputation
pvmod05b <- TAM::tam.pv( mod05b )
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