Dataset from Book 'Diagnostic Measurement' of Rupp, Templin and Henson (2010)
Dataset from Chapter 9 of the book 'Diagnostic Measurement' (Rupp, Templin & Henson, 2010).
data(data.dcm)
The format of the data is a list containing the dichotomous item
response data data (10000 persons at 7 items)
and the Q-matrix q.matrix (7 items and 3 skills):
List of 2 $ data :'data.frame': ..$ id: int [1:10000] 1 2 3 4 5 6 7 8 9 10 ... ..$ D1: num [1:10000] 0 0 0 0 1 0 1 0 0 1 ... ..$ D2: num [1:10000] 0 0 0 0 0 1 1 1 0 1 ... ..$ D3: num [1:10000] 1 0 1 0 1 1 0 0 0 1 ... ..$ D4: num [1:10000] 0 0 1 0 0 1 1 1 0 0 ... ..$ D5: num [1:10000] 1 0 0 0 1 1 1 0 1 0 ... ..$ D6: num [1:10000] 0 0 0 0 1 1 1 0 0 1 ... ..$ D7: num [1:10000] 0 0 0 0 0 1 1 0 1 1 ... $ q.matrix: num [1:7, 1:3] 1 0 0 1 1 0 1 0 1 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:7] "D1" "D2" "D3" "D4" ... .. ..$ : chr [1:3] "skill1" "skill2" "skill3"
For supplementary material of the Rupp, Templin and Henson book (2010) see http://dcm.coe.uga.edu/.
The dataset was downloaded from http://dcm.coe.uga.edu/supplemental/chapter9.html.
Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic Measurement: Theory, Methods, and Applications. New York: The Guilford Press.
## Not run:
data(data.dcm, package="CDM")
dat <- data.dcm$data[,-1]
Q <- data.dcm$q.matrix
#*****************************************************
# Model 1: DINA model
#*****************************************************
mod1 <- CDM::din( dat, q.matrix=Q )
summary(mod1)
#--------
# Model 1m: estimate model in mirt package
library(mirt)
library(sirt)
#** define theta grid of skills
# use the function skillspace.hierarchy just for convenience
hier <- "skill1 > skill2"
skillspace <- CDM::skillspace.hierarchy( hier, skill.names=colnames(Q) )
Theta <- as.matrix(skillspace$skillspace.complete)
#** create mirt model
mirtmodel <- mirt::mirt.model("
skill1=1
skill2=2
skill3=3
(skill1*skill2)=4
(skill1*skill3)=5
(skill2*skill3)=6
(skill1*skill2*skill3)=7
" )
#** mirt parameter table
mod.pars <- mirt::mirt( dat, mirtmodel, pars="values")
# use starting values of .20 for guessing parameter
ind <- which( mod.pars$name=="d" )
mod.pars[ind,"value"] <- stats::qlogis(.20) # guessing parameter on the logit metric
# use starting values of .80 for anti-slipping parameter
ind <- which( ( mod.pars$name %in% paste0("a",1:20 ) ) & (mod.pars$est) )
mod.pars[ind,"value"] <- stats::qlogis(.80) - stats::qlogis(.20)
mod.pars
#** prior for the skill space distribution
I <- ncol(dat)
lca_prior <- function(Theta,Etable){
TP <- nrow(Theta)
if ( is.null(Etable) ){ prior <- rep( 1/TP, TP ) }
if ( ! is.null(Etable) ){
prior <- ( rowSums(Etable[, seq(1,2*I,2)]) + rowSums(Etable[,seq(2,2*I,2)]) )/I
}
prior <- prior / sum(prior)
return(prior)
}
#** estimate model in mirt
mod1m <- mirt::mirt(dat, mirtmodel, pars=mod.pars, verbose=TRUE,
technical=list( customTheta=Theta, customPriorFun=lca_prior) )
# The number of estimated parameters is incorrect because mirt does not correctly count
# estimated parameters from the user customized prior distribution.
mod1m@nest <- as.integer(sum(mod.pars$est) + nrow(Theta) - 1)
# extract log-likelihood
mod1m@logLik
# compute AIC and BIC
( AIC <- -2*mod1m@logLik+2*mod1m@nest )
( BIC <- -2*mod1m@logLik+log(mod1m@Data$N)*mod1m@nest )
#** extract item parameters
cmod1m <- sirt::mirt.wrapper.coef(mod1m)$coef
# compare estimated guessing and slipping parameters
dfr <- data.frame( "din.guess"=mod1$guess$est,
"mirt.guess"=plogis(cmod1m$d), "din.slip"=mod1$slip$est,
"mirt.slip"=1-plogis( rowSums( cmod1m[, c("d", paste0("a",1:7) ) ] ) )
)
round(t(dfr),3)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## din.guess 0.217 0.193 0.189 0.135 0.143 0.135 0.162
## mirt.guess 0.226 0.189 0.184 0.132 0.142 0.132 0.158
## din.slip 0.338 0.331 0.334 0.220 0.222 0.211 0.042
## mirt.slip 0.339 0.333 0.336 0.223 0.225 0.214 0.044
# compare estimated skill class distribution
dfr <- data.frame("din"=mod1$attribute.patt$class.prob,
"mirt"=mod1m@Prior[[1]] )
round(t(dfr),3)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## din 0.113 0.083 0.094 0.092 0.064 0.059 0.065 0.429
## mirt 0.116 0.074 0.095 0.064 0.095 0.058 0.066 0.433
#** extract estimated classifications
fsc1m <- sirt::mirt.wrapper.fscores( mod1m )
#- estimated reliabilities
fsc1m$EAP.rel
## skill1 skill2 skill3
## 0.5479942 0.5362595 0.5357961
#- estimated classfications: EAPs, MLEs and MAPs
head( round(fsc1m$person,3) )
## case M EAP.skill1 SE.EAP.skill1 EAP.skill2 SE.EAP.skill2 EAP.skill3 SE.EAP.skill3
## 1 1 0.286 0.508 0.500 0.067 0.251 0.820 0.384
## 2 2 0.000 0.162 0.369 0.191 0.393 0.190 0.392
## 3 3 0.286 0.200 0.400 0.211 0.408 0.607 0.489
## 4 4 0.000 0.162 0.369 0.191 0.393 0.190 0.392
## 5 5 0.571 0.802 0.398 0.267 0.443 0.928 0.258
## 6 6 0.857 0.998 0.045 1.000 0.019 1.000 0.020
## MLE.skill1 MLE.skill2 MLE.skill3 MAP.skill1 MAP.skill2 MAP.skill3
## 1 1 0 1 1 0 1
## 2 0 0 0 0 0 0
## 3 0 0 1 0 0 1
## 4 0 0 0 0 0 0
## 5 1 0 1 1 0 1
## 6 1 1 1 1 1 1
#** estimate model fit in mirt
( fit1m <- mirt::M2( mod1m ) )
#*****************************************************
# Model 2: DINO model
#*****************************************************
mod2 <- CDM::din( dat, q.matrix=Q, rule="DINO")
summary(mod2)
#*****************************************************
# Model 3: log-linear model (LCDM): this model is the GDINA model with the
# logit link function
#*****************************************************
mod3 <- CDM::gdina( dat, q.matrix=Q, link="logit")
summary(mod3)
#*****************************************************
# Model 4: GDINA model with identity link function
#*****************************************************
mod4 <- CDM::gdina( dat, q.matrix=Q )
summary(mod4)
#*****************************************************
# Model 5: GDINA additive model identity link function
#*****************************************************
mod5 <- CDM::gdina( dat, q.matrix=Q, rule="ACDM")
summary(mod5)
#*****************************************************
# Model 6: GDINA additive model logit link function
#*****************************************************
mod6 <- CDM::gdina( dat, q.matrix=Q, link="logit", rule="ACDM")
summary(mod6)
#--------
# Model 6m: GDINA additive model in mirt package
# use data specifications from Model 1m)
#** create mirt model
mirtmodel <- mirt::mirt.model("
skill1=1,4,5,7
skill2=2,4,6,7
skill3=3,5,6,7
" )
#** mirt parameter table
mod.pars <- mirt::mirt( dat, mirtmodel, pars="values")
#** estimate model in mirt
# Theta and lca_prior as defined as in Model 1m
mod6m <- mirt::mirt(dat, mirtmodel, pars=mod.pars, verbose=TRUE,
technical=list( customTheta=Theta, customPriorFun=lca_prior) )
mod6m@nest <- as.integer(sum(mod.pars$est) + nrow(Theta) - 1)
# extract log-likelihood
mod6m@logLik
# compute AIC and BIC
( AIC <- -2*mod6m@logLik+2*mod6m@nest )
( BIC <- -2*mod6m@logLik+log(mod6m@Data$N)*mod6m@nest )
#** skill distribution
mod6m@Prior[[1]]
#** extract item parameters
cmod6m <- mirt.wrapper.coef(mod6m)$coef
print(cmod6m,digits=4)
## item a1 a2 a3 d g u
## 1 D1 1.882 0.000 0.000 -0.9330 0 1
## 2 D2 0.000 2.049 0.000 -1.0430 0 1
## 3 D3 0.000 0.000 2.028 -0.9915 0 1
## 4 D4 2.697 2.525 0.000 -2.9925 0 1
## 5 D5 2.524 0.000 2.478 -2.7863 0 1
## 6 D6 0.000 2.818 2.791 -3.1324 0 1
## 7 D7 3.113 2.918 2.785 -4.2794 0 1
#*****************************************************
# Model 7: Reduced RUM model
#*****************************************************
mod7 <- CDM::gdina( dat, q.matrix=Q, rule="RRUM")
summary(mod7)
#*****************************************************
# Model 8: latent class model with 3 classes and 4 sets of starting values
#*****************************************************
#-- Model 8a: randomLCA package
library(randomLCA)
mod8a <- randomLCA::randomLCA( dat, nclass=3, verbose=TRUE, notrials=4)
#-- Model8b: rasch.mirtlc function in sirt package
library(sirt)
mod8b <- sirt::rasch.mirtlc( dat, Nclasses=3, nstarts=4 )
summary(mod8a)
summary(mod8b)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.