IRT.jackknife.RdThis function performs a Jackknife procedure for estimating
standard errors for an item response model. The replication
design must be defined by IRT.repDesign.
Model fit is also assessed via Jackknife.
Statistical inference for derived parameters is performed
by IRT.derivedParameters with a fitted object of
class IRT.jackknife and a list with defining formulas.
IRT.jackknife(object,repDesign, ... ) IRT.derivedParameters(jkobject, derived.parameters ) # S3 method for gdina IRT.jackknife(object, repDesign, ...) # S3 method for IRT.jackknife coef(object, bias.corr=FALSE, ...) # S3 method for IRT.jackknife vcov(object, ...)
| object | Objects for which S3 method |
|---|---|
| repDesign | Replication design generated by |
| jkobject | Object of class |
| derived.parameters | List with defined derived parameters (see Example 2, Model 2). |
| bias.corr | Optional logical indicating whether a bias correction should be employed. |
| ... | Further arguments to be passed. |
List with following entries
Parameter table with Jackknife estimates
Matrix with replicated statistics
Variance covariance matrix of parameters
if (FALSE) { library(BIFIEsurvey) ############################################################################# # EXAMPLE 1: Multiple group DINA model with TIMSS data | Cluster sample ############################################################################# data(data.timss11.G4.AUT.part, package="CDM") dat <- data.timss11.G4.AUT.part$data q.matrix <- data.timss11.G4.AUT.part$q.matrix2 # extract items items <- paste(q.matrix$item) # generate replicate design rdes <- CDM::IRT.repDesign( data=dat, wgt="TOTWGT", jktype="JK_TIMSS", jkzone="JKCZONE", jkrep="JKCREP" ) #--- Model 1: fit multiple group GDINA model mod1 <- CDM::gdina( dat[,items], q.matrix=q.matrix[,-1], weights=dat$TOTWGT, group=dat$female +1 ) # jackknife Model 1 jmod1 <- CDM::IRT.jackknife( object=mod1, repDesign=rdes ) summary(jmod1) coef(jmod1) vcov(jmod1) ############################################################################# # EXAMPLE 2: DINA model | Simple random sampling ############################################################################# data(sim.dina, package="CDM") data(sim.qmatrix, package="CDM") dat <- sim.dina q.matrix <- sim.qmatrix # generate replicate design with 50 jackknife zones (50 random groups) rdes <- CDM::IRT.repDesign( data=dat, jktype="JK_RANDOM", ngr=50 ) #--- Model 1: DINA model mod1 <- CDM::gdina( dat, q.matrix=q.matrix, rule="DINA") summary(mod1) # jackknife DINA model jmod1 <- CDM::IRT.jackknife( object=mod1, repDesign=rdes ) summary(jmod1) #--- Model 2: DINO model mod2 <- CDM::gdina( dat, q.matrix=q.matrix, rule="DINO") summary(mod2) # jackknife DINA model jmod2 <- CDM::IRT.jackknife( object=mod2, repDesign=rdes ) summary(jmod2) IRT.compareModels( mod1, mod2 ) # statistical inference for derived parameters derived.parameters <- list( "skill1"=~ 0 + I(prob_skillV1_lev1_group1), "skilldiff12"=~ 0 + I( prob_skillV2_lev1_group1 - prob_skillV1_lev1_group1 ), "skilldiff13"=~ 0 + I( prob_skillV3_lev1_group1 - prob_skillV1_lev1_group1 ) ) jmod2a <- CDM::IRT.derivedParameters( jmod2, derived.parameters=derived.parameters ) summary(jmod2a) coef(jmod2a) }