GPCMlasso (version 0.1-7)

tenseness_small: Subset of tenseness data from the Freiburg Complaint Checklist

Description

Data from the Freiburg Complaint Checklist. The data contain 5 items (out of 8) corresponding to the scale Tenseness for a subset of 200 participants of the standardization sample of the Freiburg Complaint Checklist.

Format

A data frame containing data from the Freiburg Complaint Checklist a subset of 200 observations. The complete data set with 1847 observations can be found in tenseness. All items refer to the scale Tenseness and are measured on a 5-point Likert scale where low numbers correspond to low frequencies or low intensitites of the respective complaint and vice versa.

Clammy_hands

Do you have clammy hands?

Sweat_attacks

Do you have sudden attacks of sweating?

Clumsiness

Do you notice that you behave clumsy?

Wavering_hands

Are your hands wavering frequently, e.g. when lightning a cigarette or when holding a cup?

Restless_hands

Do you notice that your hands are restless?

Gender

Gender of the person

Age

Age of the person

See Also

GPCMlasso, ctrl_GPCMlasso, trait.posterior

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

if (FALSE) {
## 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)
}