R: Generalized Partial Credit Model Trees

gpcmtree {psychotree}

R Documentation

Generalized Partial Credit Model Trees

Description

Recursive partitioning (also known as trees) based on generalized partial credit models (GPCMs) for global testing of differential item functioning (DIF).

Usage

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 class 'gpcmtree'
plot(x, type = c("regions", "profile"), terminal_panel = NULL,
  tp_args = list(...), tnex = 2L, drop_terminal = TRUE, ...)

Arguments

`formula`	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`.
`data`	a data frame containing the variables in the model.
`weights`	an optional vector of weights (interpreted as case weights).
`grouppars`	logical. Should the estimated distributional group parameters of a multiple group model be included in the model parameters?
`vcov`	logical or character specifying the type of variance-covariance matrix (if any) computed for the final models (see `gpcmodel`).
`nullcats`	character string, specifying how items with null categories (i.e., categories not observed) should be treated. See `gpcmodel`, currently only `"downcode"` is available.
`start`	an optional vector or list of starting values (see `gpcmodel`).
`method`	control parameter for the optimizer employed by `mirt` for the EM algorithm (see `gpcmodel`).
`maxit`	control parameter for the optimizer employed by `gpcmodel`.
`reltol`	control parameter for the optimizer employed by `gpcmodel`.
`minsize`	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.
`x`	an object of class `gpcmtree`.
`type`	character specifying the type of plot.
`terminal_panel`, `tp_args`, `tnex`, `drop_terminal`	arguments passed to `mob`.

Details

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).

Value