# Calculate Posterior Estimates for Trait Parameters Calculates posterior estimates for trait/person parameters using the assumption of Gaussian distributed parameters. ```r trait.posterior(model, coefs = NULL, cores = 25, tol = 1e-04) ``` ## 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`. ## Returns Vector containing all estimates of trait/person parameters. ## Examples ```r 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) ``` ## 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](https://link.springer.com/article/10.3758/s13428-019-01224-2) ## See Also `GPCMlasso` `GPCMlasso-package` ## Author(s) Gunther Schauberger gunther.schauberger@tum.de

trait.posterior function