npltree | R Documentation |
Recursive partitioning (also known as trees) based on parametric logistic (n-PL) item response theory (IRT) models for global testing of differential item functioning (DIF).
npltree(formula, data, type = c("Rasch", "1PL", "2PL", "3PL", "3PLu", "4PL"),
start = NULL, weights = NULL, grouppars = FALSE,
vcov = TRUE, method = "BFGS", maxit = 500L,
reltol = 1e-10, deriv = "sum", hessian = TRUE,
full = TRUE, minsize = NULL, ...)
## S3 method for class 'npltree'
plot(x, type = c("profile", "regions"), terminal_panel = NULL,
tp_args = list(...), tnex = 2L, drop_terminal = TRUE, ...)
formula |
A symbolic description of the model to be fit. This should be of type |
data |
a data frame containing the variables in the model. |
type |
character, specifying either the type of IRT model in |
start |
an optional vector or list of starting values (see |
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? (See |
vcov |
logical or character specifying the type of variance-covariance matrix (if any) computed for
the final models when fitted using MML (see |
method |
control parameter for the optimizer used by |
maxit |
control parameter for the optimizer used by |
reltol |
control parameter for the optimizer used by |
deriv |
character. Which type of derivatives should be used for computing gradient and Hessian matrix
when fitting Rasch models with the conditional maximum likelihood (CML) method (see |
hessian |
logical. Should the Hessian be computed for Rasch models fitted with the CML method
(see |
full |
logical. Should a full model object be returned for Rasch models fitted with the CML method
(see |
minsize |
The minimum number of observations in each node, which is passed to |
... |
arguments passed to |
x |
an object of class |
terminal_panel , tp_args , tnex , drop_terminal |
arguments passed to |
Parametric logistic (n-PL) model trees are an application of model-based recursive partitioning
(implemented in mob
) to item response theory (IRT) models (implemented in
raschmodel
and nplmodel
). While the "Rasch"
model is estimated by conditional maximum likelihood (CML) all other n-PL models are estimated by
marginal maximum likelihood (MML) via the standard EM algorithm. The latter allow the specification
of multiple-group model to capture group impact on the ability distributions.
Various methods are provided for "npltree"
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).
An object of S3 class "npltree"
inheriting from class "modelparty"
.
mob
, nplmodel
,
rstree
, pctree
, raschtree
, gpcmtree
o <- options(digits = 4)
# fit a Rasch (1PL) tree on the SPISA data set
library("psychotree")
data("SPISA", package = "psychotree")
nplt <- npltree(spisa[, 1:9] ~ age + gender + semester + elite + spon,
data = SPISA, type = "Rasch")
nplt
# visualize
plot(nplt)
# compute summaries of the models fitted in nodes 1 and 2
summary(nplt, 1:2)
options(digits = o$digits)
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