# mptmodel

##### 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),
init = NULL),
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:

a matrix with the response frequencies,

estimated parameters (for extraction, the `coef`

function is preferred),

log-likelihood of the fitted model,

number of estimated parameters,

the weights used (if any),

number of observations (with non-zero weights),

the aggregate response frequencies,

see `mpt`

in the mpt
package.

##### See Also

`btmodel`

, `pcmodel`

, `gpcmodel`

,
`rsmodel`

, `raschmodel`

, `plmodel`

,
`mptspec`

, the mpt package

##### Examples

```
# NOT RUN {
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 0.5-1, License: GPL-2 | GPL-3*