Type: Package
Title: Model Response Styles in Partial Credit Models
Version: 0.1-5
Date: 2025-07-22
Maintainer: Gunther Schauberger <gunther.schauberger@tum.de>
Description: Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> . PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: Rcpp (≥ 0.12.4)
Depends: ltm, statmod, cubature, mvtnorm, parallel
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2025-07-22 07:32:55 UTC; ge29weh
Author: Gunther Schauberger [aut, cre]
Repository: CRAN
Date/Publication: 2025-07-22 08:32:38 UTC

Model Response Styles in Partial Credit Models

Description

Performs PCMRS, a method to model response styles in Partial Credit Models

Author(s)

Maintainer: Gunther Schauberger gunther.schauberger@tum.de

Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/

References

Tutz, Gerhard, Schauberger, Gunther and Berger, Moritz (2018): Response Styles in the Partial Credit Model, Applied Psychological Measurement, doi:10.1177/0146621617748322

See Also

PCMRS, person.posterior, tenseness, emotion

Examples


## Not run: 
################################################
## Small example to illustrate model and person estimation
################################################

data(tenseness)

set.seed(5)
samples <- sample(1:nrow(tenseness), 100)
tense_small <- tenseness[samples,1:4]

m_small <- PCMRS(tense_small, cores = 2)
m_small
plot(m_small)

persons <- person.posterior(m_small, cores = 2)
plot(jitter(persons, 100))

################################################
## Example from Tutz et al. 2017:
################################################

data(emotion)
m.emotion <- PCMRS(emotion)
m.emotion

plot(m.emotion)

## End(Not run)

Model Response Styles in Partial Credit Models

Description

Performs PCMRS, a method to model response styles in Partial Credit Models

Usage

PCMRS(
  Y,
  Q = 10,
  scaled = TRUE,
  method = c("L-BFGS-B", "nlminb"),
  cores = 30,
  lambda = 0
)

Arguments

Y

Data frame containing the ordinal item response data (as ordered factors), one row per obeservation, one column per item.

Q

Number of nodes to be used (per dimension) in two-dimensional Gauss-Hermite-Quadrature.

scaled

Should the scaled version of the response style parameterization be used? Default is TRUE.

method

Specifies optimization algorithm used by optim, either L-BFGS-B or nlminb.

cores

Number of cores to be used in parallelized computation.

lambda

Tuning parameter for optional L2 penalty on coefficient vector (for stabilized estimation)

Value

delta

Matrix containing all item parameters for the PCMRS model, one row per item, one column per category.

Sigma

2*2 covariance matrix for both random effects, namely the ability parameters theta and the response style parameters gamma.

delta.PCM

Matrix containing all item parameters for the simple PCM model, one row per item, one column per category.

sigma.PCM

Estimate for variance of ability parameters theta in the simple PCM model.

Y

Data frame containing the ordinal item response data, one row per obeservation, one column per item.

scaled

Logical, TRUE if scaled version of the response style parameterization is used.

neg.loglik

Negative marginal log-likelihood

Author(s)

Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/

References

Tutz, Gerhard, Schauberger, Gunther and Berger, Moritz (2018): Response Styles in the Partial Credit Model, Applied Psychological Measurement, doi:10.1177/0146621617748322

See Also

person.posterior PCMRS-package

Examples


## Not run: 
################################################
## Small example to illustrate model and person estimation
################################################

data(tenseness)

set.seed(5)
samples <- sample(1:nrow(tenseness), 100)
tense_small <- tenseness[samples,1:4]

m_small <- PCMRS(tense_small, cores = 2)
m_small
plot(m_small)

persons <- person.posterior(m_small, cores = 2)
plot(jitter(persons, 100))

################################################
## Example from Tutz et al. 2017:
################################################

data(emotion)
m.emotion <- PCMRS(emotion)
m.emotion

plot(m.emotion)

## End(Not run)

Emotional reactivity data from the Freiburg Complaint Checklist (emotion)

Description

Data from the Freiburg Complaint Checklist. The data contain all 8 items corresponding to the scale Emotional reactivity for 2032 participants of the standardization sample of the Freiburg Complaint Checklist.

Format

A data frame containing data from the Freiburg Complaint Checklist with 2032 observations. All items refer to the scale Emotional reactivity 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.

Feel upset in whole body

Do you feel it in the whole body when you get upset about something?

Eyes well up with tears

Do your eyes well up with tears in certain situations?

Stammer

Do you sometimes start stammering in certain situations?

Blush

Do you blush?

Gasp for air

Do you have to gasp for air in exciting situations, so that you have to take a deep breath?

Rapid heartbeat in excitement

Do you feel a rapid heartbeat in excitement?

Urge to defecate in excitement

Do you feel the urge to defecate in excitement?

Trembling knees

Do you start trembling in excitement or do you get trembling knees?

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.

References

Tutz, Gerhard, Schauberger, Gunther and Berger, Moritz (2018): Response Styles in the Partial Credit Model, Applied Psychological Measurement, doi:10.1177/0146621617748322

Examples

## Not run: 
data(emotion)
m.emotion <- PCMRS(emotion)
m.emotion

plot(m.emotion)

## End(Not run)

Calculate Posterior Estimates for Person Parameters

Description

Calculates posterior estimates for both person parameters, namely the ability parameters theta and the response style parameters gamma.

Usage

person.posterior(model, cores = 30, tol = 1e-04, maxEval = 600, which = NULL)

Arguments

model

Object of class PCMRS.

cores

Number of cores to be used in parallelized computation.

tol

The maximum tolerance for numerical integration, default 1e-4. For more details see adaptIntegrate.

maxEval

The maximum number of function evaluations needed in numerical integration. If specified as 0 implies no limit. For more details see adaptIntegrate.

which

Optional vector to specify that only for a subset of all persons the posterior estimate is calculated.

Value

Matrix containing all estimates of person parameters, both theta and gamma.

Author(s)

Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/

References

Tutz, Gerhard, Schauberger, Gunther and Berger, Moritz (2018): Response Styles in the Partial Credit Model, Applied Psychological Measurement, doi:10.1177/0146621617748322

See Also

PCMRS PCMRS-package

Examples


## Not run: 
################################################
## Small example to illustrate model and person estimation
################################################

data(tenseness)

set.seed(5)
samples <- sample(1:nrow(tenseness), 100)
tense_small <- tenseness[samples,1:4]

m_small <- PCMRS(tense_small, cores = 2)
m_small
plot(m_small)

persons <- person.posterior(m_small, cores = 2)
plot(jitter(persons, 100))

################################################
## Example from Tutz et al. 2017:
################################################

data(emotion)
m.emotion <- PCMRS(emotion)
m.emotion

plot(m.emotion)

## End(Not run)

Tenseness data from the Freiburg Complaint Checklist (tenseness)

Description

Data from the Freiburg Complaint Checklist. The data contain all 8 items corresponding to the scale Tenseness for 2042 participants of the standardization sample of the Freiburg Complaint Checklist.

Format

A data frame containing data from the Freiburg Complaint Checklist with 2042 observations. 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?

Restless feet

Do you notice that your feet are restless?

Twitching eyes

Do you notice unvoluntary twitching of your eyes?

Twitching mouth

Do you notice unvoluntary twitching of your mouth?

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.

References

Tutz, Gerhard, Schauberger, Gunther and Berger, Moritz (2018): Response Styles in the Partial Credit Model, Applied Psychological Measurement, doi:10.1177/0146621617748322

Examples

## Not run: 
data(tenseness)

set.seed(1860)
samples <- sample(1:nrow(tenseness), 300)
tense_small <- tenseness[samples,]

m_small <- PCMRS(tense_small, cores = 25)
m_small
plot(m_small)

persons <- person.posterior(m_small, cores = 25)
plot(jitter(persons,100))

## End(Not run)