R: Multinomial Processing Tree (MPT) Model Fitting Function

mptmodel {psychotools} | R Documentation

## Multinomial Processing Tree (MPT) Model Fitting Function

### Description

`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)),
      start = NULL, vcov = TRUE, estfun = FALSE, ...)

### Arguments

`y` | matrix of response frequencies.
`weights` | an optional vector of weights (interpreted as case weights).
`spec` | an object of class `mptspec`: typically result of a call to `mptspec`. A symbolic description of the model to be fitted.
`treeid` | a vector that identifies each tree in a joint multinomial model.
`optimargs` | a list of arguments passed to the optimization function (`optim`).
`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.

### Details

`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`.

### Value

`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,
`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 `mpt` in the mpt package.

### See Also

`btmodel`, `pcmodel`, `rsmodel`, `raschmodel`, `mptspec`, the 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)

[Package psychotools version 0.4-2 Index]