# Plot function for GPCMlasso Plot function for a `GPCMlasso` object. Plots show coefficient paths of DIF (or DSF) parameters along (a transformation of) the tuning parameter lambda. One plot per item is created, every single parameter corresponding to this item is depicted by a single path. The optimal model is highlighted with a red dashed line. ```r ## S3 method for class 'GPCMlasso' plot(x, select = c("BIC", "AIC", "cAIC", "cv"), log.lambda = TRUE, items_per_page = 1, items = "all", columns = NULL, ask_new = TRUE, lambda.lines = TRUE, equal_range = TRUE, ...) ``` ## Arguments - `x`: `GPCMlasso` object - `select`: Specifies which criterion to use for the optimal model, we recommend the default value "BIC". If cross-validation was performed, automatically the optimal model according to cross-validation is used. The chosen optimal model is highlighted with a red dashed line. - `log.lambda`: A logical value indicating whether lambda or a log-transformation of lambda should be used as x-axis in the plots. - `items_per_page`: By default, each plot/item is put on a separate page. For example, `items_per_page=4` would put four plots/items on one page. - `items`: By default, all items are plotted. If `items=c(1,3)`, only the first and the third item are plotted. - `columns`: Specifies the number of columns to use when several plots are on one page. Only relevant if `items_per_page`>1. - `ask_new`: If TRUE, the user is asked to confirm before the next item is plotted. - `lambda.lines`: A logical value indicating whether a thin gray line plotted for each value from the vector of tuning parameters from `object` - `equal_range`: A logical value indicating whether for each plot equal limits on the y-axis shall be used. - `...`: Further plot arguments. ## 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` ## Author(s) Gunther Schauberger gunther.schauberger@tum.de

plot.GPCMlasso function