Recursive partitioning (also known as trees) based on generalized partial credit models (GPCMs) for global testing of differential item functioning (DIF).
gpcmtree(formula, data, weights = NULL,
grouppars = FALSE, vcov = TRUE, nullcats = "downcode",
start = NULL, method = "BFGS", maxit = 500L,
reltol = 1e-10, minsize = 500, ...)# S3 method for gpcmtree
plot(x, type = c("regions", "profile"), terminal_panel = NULL,
tp_args = list(...), tnex = 2L, drop_terminal = TRUE, ...)
An object of S3 class "gpcmtree" inheriting from class "modelparty".
A symbolic description of the model to be fit. This should be of type y ~ x1 + x2 where
y should be an item response matrix and x1 and x2 are used as partitioning variables.
Additionally, it is poosible to allow for impact of a group variable so that different ability distributions
are estimated in each group. This can be specified by extending the previous formula by a group factor
g as y ~ g | x1 + x2.
a data frame containing the variables in the model.
an optional vector of weights (interpreted as case weights).
logical. Should the estimated distributional group parameters of a multiple group model be included in the model parameters?
logical or character specifying the type of variance-covariance matrix (if any) computed for the
final models (see gpcmodel).
character string, specifying how items with null categories (i.e., categories not observed)
should be treated. See gpcmodel, currently only "downcode" is available.
an optional vector or list of starting values (see gpcmodel).
control parameter for the optimizer employed by mirt for the EM algorithm
(see gpcmodel).
control parameter for the optimizer employed by gpcmodel.
control parameter for the optimizer employed by gpcmodel.
integer specification of minimum number of observations in each node, which is passed to
mob_control.
arguments passed to mob_control for gpcmtree, or to the
underlying plot method, respectively.
an object of class gpcmtree.
character specifying the type of plot.
arguments passed to mob.
Generalized partial credit model (GPCM) trees are an application of model-based recursive partitioning
(implemented in mob) to GPCM models (implemented in gpcmodel).
Various methods are provided for "gpcmtree" objects, most of them inherit their behavior from
"modelparty" objects (e.g., print, summary). Additionally, dedicated extractor
functions or provided for the different groups of model parameters in each node of the tree:
itempar (item parameters),
threshpar (threshold parameters),
guesspar (guessing parameters),
upperpar (upper asymptote parameters).
mob, plmodel,
rstree, pctree, raschtree, npltree