Changes in Version 0.3-0
o 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.
o 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).
o 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.
o The worth() generic now internally calls the methods for itempar().
o 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".
o 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.
o 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.
o 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.
o 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.
o A bug occuring in pcmodel() when null categories are present and
nullcats = "keep" was fixed. (Thanks to Oliver Prosperi for reporting
this.)
o 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.
Changes in Version 0.2-0
o 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).
o Partial credit models (PCMs) can be fitted with the function
PCModel.fit(). The interface and return value is similar to that
of RaschModel.fit().
o 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().
o 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.
o 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.
Changes in Version 0.1-4
o Added YouthGratitude data from Froh, Fan, Emmons, Bono, Huebner, Watkins
(2011, PA), provided by Jeff Froh and Jinyan Fan. Some approximate
replication code is provided in the examples (the parts depending on
lavaan are in \dontrun).
Changes in Version 0.1-3
o Fully exported elementary_symmetric_functions(). (An extended C
implementation is under development and will be included in
future releases.)
Changes in Version 0.1-2
o 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.
o Actually pass on nlm() arguments from RaschModel.fit(). Also
support iterlim = 0, i.e., set up model at pre-specified parameters.
o Added StereotypeThreat data from Wicherts, Conor, Hessen (2005, JPSP),
provided by Jelte M. Wicherts. Replication code is provided in the
examples (the parts depending on lavaan are in \dontrun).
Changes in Version 0.1-1
o 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).
o Classes: "paircomp" and associated methods.
o Models: btReg.fit() and RaschModel.fit() and associated methods.
o Data: Firstnames, GermanParties2009, Soundquality (previoulsy in
psychotree) and VerbalAggression (new data, contained in other
formatting in difR/verbal and lme4/VerbAgg).