Fix in estfun() method for npmodel()
objects for the case when some impact groups are empty (which is not
unusual when fitting trees).
Updated examples and reference output to avoid differences in
output of summary with NAs vs. NA's.
Add packages utilized in
demo(package = "psychotools") to
DESCRIPTION/Suggests. Also use hcl.colors()
instead of package colorspace.
Don’t test visualization of likert objects that can
be coerced from itemresp objects because its
plot() method currently throws a warning.
$call in IRT
functions raschmodel(), rsmodel(),
pcmodel(), nplmodel(), gpcmodel()
(suggested by Rainer W. Alexandrowicz).Fixed calculation of person parameter covariance matrix estimate
(and thus confidence intervals) in personpar() methods for
Rasch models (reported by Rainer W. Alexandrowicz).
Fixed bug in threshpar() method of
pcmodel objects fitted to binary items reported by Rainer
W. Alexandrowicz).
Fixed bug in pcmodel() which failed when the
reference category was a null category (without observations). Now the
first category with observations is chosen as the reference category
(reported by Rainer W. Alexandrowicz).
Improved raschmodel(), rsmodel(), and
pcmodel() so that an NA variance-covariance
matrix is returned (with a warning) if the Hessian cannot be inverted
instead of stopping with a technical error message (suggested by Rainer
W. Alexandrowicz).
Fixed names of "mscale" attribute in
itemresp variable in MathExam14W data, now
consistent with column names.
Bug fix in discrpar() method for
nplmodel(..., type = "1PL") so that the variance covariance
matrix is computed correctly.
Documentation enhancements in the mathematical notation
(suggested by Kurt Hornik) and in the description of the 1PL/Rasch model
in nplmodel() (suggested by Feng Lin).
The function (and resulting class) for fitting n-PL type
parametric logistic IRT models has been renamed from
plmodel() to nplmodel(). For now, a copy
called plmodel() is also preserved (but this also returns
an object of class "nplmodel").
Fix logicals of length > 1 in some if()
constructs.
Accept .replicates for user-provided model
specifications in mptmodel().
Changed default constant anchor selection method in
anchor() and anchortest() from MPT to Gini
based on Strobl et al. (2021, Applied Psychological Measurement). The
Gini-based anchor selection is simpler because it is based on single
anchors while at the same time performing very well under DIF.
The print() method for anchortest() now
just displays the anchor item(s) and the final DIF tests while the
summary() method displays the full information including
anchored item parameters etc. (rather than vice versa).
The print() and summary() methods for
anchor() and anchortest() now display item
labels (rather than indexes) for the selected anchor item(s) and for the
full vector of criterion values.
Added the "proCNI" multinomial processing tree
specification in mptspec() for mptmodel().
This provides the CNI model of moral dilemma judgment for proscriptive
norms.
vignette("toolbox-simulation", package = "psychotools") on
how to conduct simulation studies investigating the performance of
score-based tests of measurement invariance of IRT models. Accompanies
the PsyArXiv preprint mentioned below.New anchor() selection strategy using
inequality-based alignment, either based on the Gini index or the
component loss function (CLF). Thus, also directly available in
anchortest(). Improved print() and
plot() methods.
Changed method to invert Hessian for raschmodel(),
rsmodel(), and pcmodel() from
qr.solve() to chol2inv(chol()).
Added two demos on how to conduct simulation studies for score-based test of measurement invariance.
All IRT models now have a function to simulate IRT data, see
rrm(), rrsm(), rpcm(),
rpl(), and rgpcm().
An accompanying new PsyArXiv Preprint “An R Toolbox for Score-Based Measurement Invariance Tests in IRT Models” by Lennart Schneider, Carolin Strobl, Achim Zeileis, and Rudolf Debelak is available at https://doi.org/10.31234/osf.io/r9w34
Infrastructure for IRT modeling in the unified
psychotools framework is extended by marginal maximum
likelihood (MML) estimation of generalized partial credit models and
parametric logistic models, respectively. The corresponding fitting
functions (see below for details) call mirt() or
multipleGroup() from the mirt package but
return objects for which all standard extractor methods (item
parameters, person parameters, etc.) and visualization methods (item
response curves, parameter profiles, person-item maps, etc.) are
available.
The new gpcmodel() function interfaces
mirt (see above) and fits (generalized) partial credit
models (GPCMs) by MML.
The new plmodel() function interfaces
mirt (see above) and fits various parametric IRT logistic
models using MML: 1PL (Rasch), 2PL, 3PL, 3PLu, and 4PL.
New functions and eponymous classes guesspar(), and
upperpar() to extract/represent so-called guessing
parameters and upper asymptote parameters of IRT models.
personpar() now distinguishes between parameters of
the assumed person ability distribution
(personwise = FALSE) and the individual person parameters
for each person/subject in the underlying data set
(personwise = TRUE). In the CML case, the latter simply
computes the raw score for each person and then extracts the
corresponding person parameter. In the MML case, this necessitates
(numerically) integrating out the individual person parameters (also
known as factor scores or latent trait estimates) based on the
underlying normal distribution.
Added new data set ConspiracistBeliefs2016 from the
Open Source Psychometrics Project (2016).
Added new simulated data set Sim3PL for fitting
dichotomous IRT models, especially the 3PL and 3PLu.
Conditionally register all estfun() and
bread() S3 methods for model objects, provided that the
sandwich package is attached.
Added native routine registration for
esf.c.
Use R version of elementary_symmetric_functions() by
default on Win/i386 due to small numeric differences on that
platform.
The estfun() method for btmodel objects
always computed the scores with the last object for the reference
category - even if a different ref= was specified in the
model. (Thanks to Heather Turner for pointing out the problem.)
The itempar() method for btmodel
objects miscomputed the variance covariance matrix (unless the first
object was used as the ref when estimating the model). (Thanks to
Heather Turner for pointing out the problem.)
Added new data set PairClustering from Klauer
(2006).
Fixed replication code in example of
StereotypeThreat (reported by Ed Merkle).
Basil Abou El-Komboz changed his name to Basil Komboz.
Properly imported grDevices and utils
in NAMESPACE.
Added new item response data set MathExam14W with
esponses of 729 students to 13 items in a written exam of introductory
mathematics along with several covariates.
New function mptmodel() and corresponding extractor
functions for fitting multinomial processing tree (MPT) models. These
functions are somewhat experimental, and their user interface might
change in future releases.
Bug fix in itempar() method for
raschmodel objects if alias = FALSE. In the
previous version the methods had an erroneous trailing
NA.
Improved item names labeling in plot() method for
itemresp objects to conform with regionplot()
function for IRT models.
mscale<-() method for itemresp has
been improved so that categories can be easily collapsed (e.g.,
dichotomized).
Infrastructure for IRT modeling in psychotools is
greatly enhanced. Therefore the main modeling functions are now called
raschmodel() for Rasch models, rsmodel() for
rating scale models, pcmodel() for partial credit models,
and btmodel() for Bradley-Terry models. The old
*.fit() functions from previous versions of the package
still exist but now internally call the new *model()
functions. Also, the classes returned have the same names as the
*model functions.
A unified visualization framework for fitted IRT models has been added: For all types of models (Rasch, RSM, PCM) one can visualize profiles of the item parameters, regions for the most likely response, item or category characteristic curves, item information, and person-item plots. All of these rely on the unified framework for extracting parameters and predictions (see below).
New functions and eponymous classes itempar(),
threshpar(), and discrpar() to
extract/represent item, threshold, and discrimination parameters of item
response models. Methods for the IRT models (Rasch, RSM, PCM) are
provided. In addition, several methods for standard generic functions
(print(), coef(), vcov()) are
available.
The worth() generic now internally calls the methods
for itempar().
Estimation of person parameters for a given item response model
is now available via the generic function personpar().
Specific methods for Rasch, rating scale and partial credit models allow
the estimatation of person parameters via joint maximum likelihood
estimation. Methods for standard generic functions
(print(), coef(), vcov()) are
available for the resulting objects of class
personpar.
predict() methods for Rasch, rating scale and
partial credit models have been added. For a given fitted model object,
these can be used to predict various types of response probabilities or
actual reponses.
New functions anchor() and anchortest()
provide a variety of anchor methods for the detection of uniform
differential item functioning (DIF) between two pre-specified groups in
the Rasch model. To test for DIF, the itemwise Wald test is
implemented.
itemresp() is the class constructor for responses of
n subjects to k items which can be polytomous and have different
measurement scales. A wide range of methods to standard generics is
provided as well as to generics created for the paircomp
class. Thus, features can be easily extracted/replaced,
summaries/visualizations can be produced, subsetting/merging/etc. is
facilitated.
The handling of argument ref when producing a region
plot (previously called effect plot) was changed. Whereas in the
previous implementation, the restriction specified in this argument was
applied to the cumulative absolute item threshold parameters, it now is
applied to the absolute item threshold parameters.
A bug occuring in pcmodel() when null categories are
present and nullcats = "keep" was fixed. (Thanks to Oliver
Prosperi for reporting this.)
The processing of the minimal category zero in the function
rsmodel() was changed. Only if for all items, the minimal
category is above zero, downcoding takes place. Otherwise, the missing
minimal categories are treated as not observed, i.e., with a frequency
of zero.
Major update with new model fitting functions (partial credit and rating scale model) and improved infrastructure for conditional maximum likelihood estimation (C implementation of elementary symmetric functions).
Partial credit models (PCMs) can be fitted with the function
PCModel.fit(). The interface and return value is similar to
that of RaschModel.fit().
Rating scale models (RSMs) can be fitted with the function
RSModel.fit(). The interface and return value is similar to
that of RaschModel.fit() and
PCModel.fit().
The function elementary_symmetric_functions() for
computing ESFs is extended and now part of the exported user interface.
The R implementation for binary items up to order 2 is complemented by a
C implementation for both binary and polytomous items up to order
1.
Due to numerical instabilities in the coefficients and standard
errors between different architectures, the optimization method for
Rasch/RSModel/PCModel.fit() was
changed from nlm(...) to
optim(..., method = "BFGS"). Consequently, the arguments
reltol and maxit are used now instead of
gradtol and iterlim. For backward
compatibility RaschModel.fit() still supports the old
arguments but might cease to do so in future releases.
YouthGratitude data from Froh, Fan, Emmons, Bono,
Huebner, Watkins (2011, Psychological Assessment), provided by Jeff Froh
and Jinyan Fan. Some approximate replication code is provided in the
examples (the parts depending on lavaan are in
\dontrun).elementary_symmetric_functions(). (An
extended C implementation is under development and will be included in
future releases.)Support of non-integer weights in btReg.fit(). To
facilitate this, summary.paircomp() gained a weights
argument so that optionally the weights are aggregated instead of
observations counted.
Actually pass on nlm() arguments from
RaschModel.fit(). Also support iterlim = 0,
i.e., set up model at pre-specified parameters.
Added StereotypeThreat data from Wicherts, Conor,
Hessen (2005, Journal of Personality and Social Psychology), provided by
Jelte M. Wicherts. Replication code is provided in the examples (the
parts depending on lavaan are in
\dontrun).
New psychotools package containing all “base”
infrastructure previously contained in psychotree. This is
in order to provide both methods and data that can be reused by
psychotree and the new package psychomix (as
well as potentially further packages).
Classes: paircomp and associated methods.
Models: btReg.fit() and
RaschModel.fit() and associated methods.
Data: Firstnames, GermanParties2009,
Soundquality (previoulsy in psychotree) and
VerbalAggression (new data, contained in other formatting
in difR as verbal and lme4 as
VerbAgg).
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