View source: R/predict.GPCMlasso.R
predict.GPCMlasso  R Documentation 
Predict function for a GPCMlasso
object.
Predictions can be linear predictors or probabilities separately for each person and each item.
## S3 method for class 'GPCMlasso' predict( object, coefs = NULL, newdata = NULL, type = c("link", "response"), ... )
object 

coefs 
Optional vector of coefficients, can be filled with a specific
row from 
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 $k_i1$) and type "response" gives the respective probabilities (of length $k_i$). 
... 
Further predict arguments. 
Results are lists of vectors with length equal to the number
of response categories $k_i$ in case of
probabilities (type="response"
) or
$k_i1$ in case of linear predictors (type="link"
).
Gunther Schauberger
gunther@stat.unimuenchen.de
GPCMlasso
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 crossvalidate 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.