predict.GPCMlasso: Predict function for GPCMlasso

In GPCMlasso: Differential Item Functioning in Generalized Partial Credit Models

Predict function for GPCMlasso

Description

Predict function for a GPCMlasso object. Predictions can be linear predictors or probabilities separately for each person and each item.

Usage

## 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").

Author(s)

Gunther Schauberger
[e-mail]

See Also

GPCMlasso

Examples

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)

GPCMlasso documentation built on May 3, 2022, 5:05 p.m.