1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
|
\name{mptmodel}
\alias{mptmodel}
\alias{coef.mptmodel}
\alias{confint.mptmodel}
\alias{deviance.mptmodel}
\alias{estfun.mptmodel}
\alias{logLik.mptmodel}
\alias{predict.mptmodel}
\alias{print.mptmodel}
\alias{summary.mptmodel}
\alias{print.summary.mptmodel}
\alias{vcov.mptmodel}
\alias{mptspec}
\alias{print.mptspec}
\alias{update.mptspec}
\title{Multinomial Processing Tree (MPT) Model Fitting Function}
\description{
\code{mptmodel} is a basic fitting function for multinomial processing tree
(MPT) models.
}
\usage{
mptmodel(y, weights = NULL, spec, treeid = NULL,
optimargs = list(control = list(reltol = .Machine$double.eps^(1/1.2),
maxit = 1000),
init = NULL),
start = NULL, vcov = TRUE, estfun = FALSE, \dots)
}
\arguments{
\item{y}{matrix of response frequencies.}
\item{weights}{an optional vector of weights (interpreted as case weights).}
\item{spec}{an object of class \code{mptspec}: typically result of a call to
\code{\link{mptspec}}. A symbolic description of the model to be fitted.}
\item{treeid}{a vector that identifies each tree in a joint multinomial
model.}
\item{optimargs}{a list of arguments passed to the optimization function
(\code{\link{optim}}).}
\item{start}{a vector of starting values for the parameter estimates between
zero and one.}
\item{vcov}{logical. Should the estimated variance-covariance be included in
the fitted model object?}
\item{estfun}{logical. Should the empirical estimating functions
(score/gradient contributions) be included in the fitted model object?}
\item{\dots}{further arguments passed to functions.}
}
\details{
\code{mptmodel} provides a basic fitting function for multinomial processing
tree (MPT) models, intended as a building block for fitting MPT trees in the
\pkg{psychotree} package. While \code{mptmodel} is intended for individual
response frequencies, the \pkg{mpt} package provides functions for aggregate
data.
MPT models are specified using the \code{mptspec} function. See the
documentation in the \pkg{mpt} package for details.
\code{mptmodel} returns an object of class \code{"mptmodel"} for which
several basic methods are available, including \code{print}, \code{plot},
\code{summary}, \code{coef}, \code{vcov}, \code{logLik}, \code{estfun}
and \code{\link{predict}}.
}
\value{
\code{mptmodel} returns an S3 object of class \code{"mptmodel"},
i.e., a list with components as follows:
\item{y}{a matrix with the response frequencies,}
\item{coefficients}{estimated parameters (for extraction, the \code{coef}
function is preferred),}
\item{loglik}{log-likelihood of the fitted model,}
\item{npar}{number of estimated parameters,}
\item{weights}{the weights used (if any),}
\item{nobs}{number of observations (with non-zero weights),}
\item{ysum}{the aggregate response frequencies,}
\item{fitted, goodness.of.fit, ...}{see \code{mpt} in the \pkg{mpt}
package.}
}
\seealso{\code{\link{btmodel}}, \code{\link{pcmodel}}, \code{\link{gpcmodel}},
\code{\link{rsmodel}}, \code{\link{raschmodel}}, \code{\link{plmodel}},
\code{\link{mptspec}}, the \pkg{mpt} package}
\examples{
o <- options(digits = 4)
## data
data("SourceMonitoring", package = "psychotools")
## source-monitoring MPT model
mpt1 <- mptmodel(SourceMonitoring$y, spec = mptspec("SourceMon"))
summary(mpt1)
plot(mpt1)
options(digits = o$digits)
}
\keyword{regression}
|
