Multinomial Processing Tree (MPT) Model Fitting Function
mptmodel is a basic fitting function for multinomial processing tree
(MPT) models.
mptmodel(y, weights = NULL, spec, treeid = NULL,
optimargs = list(control = list(reltol = .Machine$double.eps^(1/1.2),
maxit = 1000),
init = NULL),
start = NULL, vcov = TRUE, estfun = FALSE, ...)y |
matrix of response frequencies. |
weights |
an optional vector of weights (interpreted as case weights). |
spec |
an object of class |
treeid |
a vector that identifies each tree in a joint multinomial model. |
optimargs |
a list of arguments passed to the optimization function
( |
start |
a vector of starting values for the parameter estimates between zero and one. |
vcov |
logical. Should the estimated variance-covariance be included in the fitted model object? |
estfun |
logical. Should the empirical estimating functions (score/gradient contributions) be included in the fitted model object? |
... |
further arguments passed to functions. |
mptmodel provides a basic fitting function for multinomial processing
tree (MPT) models, intended as a building block for fitting MPT trees in the
psychotree package. While mptmodel is intended for individual
response frequencies, the mpt package provides functions for aggregate
data.
MPT models are specified using the mptspec function. See the
documentation in the mpt package for details.
mptmodel returns an object of class "mptmodel" for which
several basic methods are available, including print, plot,
summary, coef, vcov, logLik, estfun
and predict.
mptmodel returns an S3 object of class "mptmodel",
i.e., a list with components as follows:
y |
a matrix with the response frequencies, |
coefficients |
estimated parameters (for extraction, the |
loglik |
log-likelihood of the fitted model, |
npar |
number of estimated parameters, |
weights |
the weights used (if any), |
nobs |
number of observations (with non-zero weights), |
ysum |
the aggregate response frequencies, |
fitted, goodness.of.fit, ... |
see |
o <- options(digits = 4)
## data
data("SourceMonitoring", package = "psychotools")
## source-monitoring MPT model
mpt1 <- mptmodel(SourceMonitoring$y, spec = mptspec("SourceMon"))
summary(mpt1)
plot(mpt1)
options(digits = o$digits)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.