tenseness_small: Subset of Tenseness Data from the Freiburg Complaint...

In GPCMlasso: Regularized Explanatory Generalized Partial Credit Models

Subset of Tenseness Data from the Freiburg Complaint Checklist

Description

A small subset of the Freiburg Complaint Checklist data. The data contain 5 items, out of the 8 items in the full data set, corresponding to the scale Tenseness, together with two covariates.

Format

A data frame containing a subset of the Freiburg Complaint Checklist data with 200 observations. The complete data set is available as tenseness. The item responses refer to the scale Tenseness and are measured on a 5-point Likert scale, where low values correspond to low frequencies or low intensities of the respective complaint and high values correspond to high frequencies or high intensities.

The data frame contains the following variables:

Clammy_hands

Do you have clammy hands?

Sweat_attacks

Do you have sudden attacks of sweating?

Clumsiness

Do you notice that you behave clumsily?

Wavering_hands

Do your hands shake frequently, for example when lighting 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.

Source

ZPID (2013). PsychData of the Leibniz Institute for Psychology Information ZPID. Trier: Center for Research Data in Psychology.

Fahrenberg, J. (2010). Freiburg Complaint Checklist [Freiburger Beschwerdenliste (FBL)]. Goettingen: Hogrefe.

See Also

GPCMlasso, ctrl_GPCMlasso, plot.GPCMlasso, trait.posterior

Examples

data(tenseness_small)

## Formula with five ordinal items and two covariates
form_cov <- as.formula(
  paste(
    "cbind(",
    paste(colnames(tenseness_small)[1:5], collapse = ","),
    ") ~ Gender + Age"
  )
)

## Not run: 
## ------------------------------------------------------------
## Classical DIF analysis
## ------------------------------------------------------------
fit_dif <- GPCMlasso(
  formula = form_cov,
  data = tenseness_small,
  model = "GPCM",
  control = ctrl_GPCMlasso(
    cores = 1,
    l.lambda = 10,
    trace = FALSE
  )
)

summary(fit_dif)
plot(
  fit_dif,
  type = "DIF",
  panels_per_page = 2,
  columns = 2,
  ask_new = FALSE
)

## ------------------------------------------------------------
## Explanatory GPCM-X with global and item-specific effects
## and fusion penalty
## ------------------------------------------------------------
set.seed(1860)

fit_fusion <- GPCMlasso(
  formula = form_cov,
  data = tenseness_small,
  model = "GPCM",
  main.effects = TRUE,
  cv = TRUE,
  control = ctrl_GPCMlasso(
    cores = 1,
    l.lambda = 10,
    folds = 2,
    penalize.main.effects = TRUE,
    include.item.effects = TRUE,
    fuse.per.variable = TRUE,
    scale.covariate.effects = FALSE,
    trace = FALSE
  )
)

summary(
  fit_fusion,
  effect.scale = "effect"
)

## Available covariate panels
fit_fusion$design_list$x.names

plot(
  fit_fusion,
  type = "Variable",
  panels = fit_fusion$design_list$x.names,
  panels_per_page = 2,
  columns = 2,
  variable.scale = "effect",
  select = "cv",
  ask_new = FALSE
)

## End(Not run)

GPCMlasso documentation built on Sept. 8, 2026, 5:08 p.m.