PCMRS | R Documentation |
Performs PCMRS, a method to model response styles in Partial Credit Models
PCMRS( Y, Q = 10, scaled = TRUE, method = c("L-BFGS-B", "nlminb"), cores = 30, lambda = 0 )
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 |
method |
Specifies optimization algorithm used by |
cores |
Number of cores to be used in parallelized computation. |
lambda |
Tuning parameter for optional L2 penalty on coefficient vector (for stabilized estimation) |
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, |
neg.loglik |
Negative marginal log-likelihood |
Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/
Tutz, Gerhard, Schauberger, Gunther and Berger, Moritz (2018): Response Styles in the Partial Credit Model, Applied Psychological Measurement, https://journals.sagepub.com/doi/10.1177/0146621617748322
person.posterior
PCMRS-package
## 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)
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