GPCMlasso (version 0.1-7)

trait.posterior: Calculate Posterior Estimates for Trait Parameters

Description

Calculates posterior estimates for trait/person parameters using the assumption of Gaussian distributed parameters.

Usage

trait.posterior(model, coefs = NULL, cores = 25, tol = 1e-04)

Value

Vector containing all estimates of trait/person parameters.

Arguments

model

Object of class GPCMlasso.

coefs

Vector of coefficients to be used for prediction. If coefs = NULL, the parameters from the BIC-optimal model will be used. If cross-validation was performed, automatically the parameters from the optimal model according to cross-validation are used.

cores

Number of cores to be used in parallelized computation.

tol

The maximum tolerance for numerical integration, for more details see pcubature.

Author

Gunther Schauberger

References

Schauberger, Gunther and Mair, Patrick (2019): A Regularization Approach for the Detection of Differential Item Functioning in Generalized Partial Credit Models, Behavior Research Methods, https://link.springer.com/article/10.3758/s13428-019-01224-2

See Also

GPCMlasso GPCMlasso-package

Examples

data(tenseness_small)

## formula for simple model without covariates
form.0 <- as.formula(paste("cbind(",paste(colnames(tenseness_small)[1:5],collapse=","),")~0"))

######
## fit simple RSM where loglikelihood and score function are evaluated parallel on 2 cores
rsm.0 <- GPCMlasso(form.0, tenseness_small, model = "RSM", 
control= ctrl_GPCMlasso(cores=2))
rsm.0

if (FALSE) {
## formula for model with covariates (and DIF detection)
form <- as.formula(paste("cbind(",paste(colnames(tenseness_small)[1:5],collapse=","),")~."))

######
## fit GPCM model with 10 different tuning parameters
gpcm <- GPCMlasso(form, tenseness_small, model = "GPCM", 
                  control = ctrl_GPCMlasso(l.lambda = 10))
gpcm
plot(gpcm)
pred.gpcm <- predict(gpcm)
trait.gpcm <- trait.posterior(gpcm)

######
## fit RSM, detect differential step functioning (DSF)
rsm.DSF <- GPCMlasso(form, tenseness_small, model = "RSM", DSF = TRUE, 
                     control = ctrl_GPCMlasso(l.lambda = 10))
rsm.DSF
plot(rsm.DSF)

## create binary data set
tenseness_small_binary <- tenseness_small
tenseness_small_binary[,1:5][tenseness_small[,1:5]>1] <- 2

######
## fit and cross-validate Rasch model
set.seed(1860)
rm.cv <- GPCMlasso(form, tenseness_small_binary, model = "RM", cv = TRUE, 
                   control = ctrl_GPCMlasso(l.lambda = 10))
rm.cv
plot(rm.cv)
}