# Predict function for GPCMlasso Predict function for a `GPCMlasso` object. Predictions can be linear predictors or probabilities separately for each person and each item. ```r ## S3 method for class 'GPCMlasso' predict( object, coefs = NULL, newdata = NULL, type = c("link", "response"), ... ) ``` ## Arguments - `object`: `GPCMlasso` object - `coefs`: Optional vector of coefficients, can be filled with a specific row from `object$coefficients`. If not specified, `coefs` are specififed to be the BIC-optimal coefficients or, if cross-validation was performed, the optimal coefficients according to cross-validation. - `newdata`: List possibly containing slots Y, X, Z1 and Z2 to use new data for prediction. - `type`: Type "link" gives vectors of linear predictors for separate categories (of length $k_i-1$) and type "response" gives the respective probabilities (of length $k_i$). - `...`: Further predict arguments. ## Details Results are lists of vectors with length equal to the number of response categories $k_i$ in case of probabilities (`type="response"`) or $k_i-1$ in case of linear predictors (`type="link"`). ## 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) ``` ## See Also `GPCMlasso` ## Author(s) Gunther Schauberger gunther@stat.uni-muenchen.de

predict.GPCMlasso function