# mptmodel

From psychotools v0.4-0
by Achim Zeileis

##### Multinomial Processing Tree (MPT) Model Fitting Function

`mptmodel`

is a basic fitting function for multinomial processing tree
(MPT) models.

- Keywords
- regression

##### 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

- 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.

`mptmodel`

returns an S3 object of class `"mptmodel"`

,
i.e., a list with components as follows:
##### 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)
```

*Documentation reproduced from package psychotools, version 0.4-0, License: GPL-2 | GPL-3*

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