View source: R/person.posterior.R
trait.posterior | R Documentation |
Calculates posterior estimates for trait/person parameters using the assumption of Gaussian distributed parameters.
trait.posterior(model, coefs = NULL, cores = 25, tol = 1e-04)
model |
Object of class |
coefs |
Vector of coefficients to be used for prediction. If |
cores |
Number of cores to be used in parallelized computation. |
tol |
The maximum tolerance for numerical integration,
for more details see |
Vector containing all estimates of trait/person parameters.
Gunther Schauberger
gunther.schauberger@tum.de
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
GPCMlasso
GPCMlasso-package
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
## Not run:
## 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)
## End(Not run)
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