# mptmix

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##### Finite Mixtures of Multinomial Processing Tree Models

Fit finite mixtures of multinomial processing tree (MPT) models via maximum likelihood with the EM algorithm.

Keywords
mixture model
##### Usage
mptmix(formula, data, k, subset, weights,
nrep = 3, cluster = NULL, control = NULL,
verbose = TRUE, drop = TRUE, unique = FALSE, which = NULL,
spec, treeid = NULL,
optimargs = list(control = list(reltol =
.Machine\$double.eps^(1/1.2), maxit = 1000)), ...)FLXMCmpt(formula = . ~ ., spec = NULL, treeid = NULL, optimargs = NULL, ...)
##### Arguments
formula

Symbolic description of the model (of type y ~ 1 or y ~ x).

data, subset

Arguments controlling formula processing.

k

A vector of integers indicating the number of components of the finite mixture; passed in turn to the k argument of stepFlexmix.

weights

An optional vector of weights to be used in the fitting process; passed in turn to the weights argument of flexmix.

nrep

Number of runs of the EM algorithm.

cluster

Either a matrix with k columns of initial cluster membership probabilities for each observation; or a factor or integer vector with the initial cluster assignments of observations at the start of the EM algorithm. Default is random assignment into k clusters.

control

An object of class "FLXcontrol" or a named list; controls the EM algorithm and passed in turn to the control argument of flexmix.

verbose

A logical; if TRUE progress information is shown for different starts of the EM algorithm.

drop

A logical; if TRUE and k is of length 1, then a single mptmix object is returned instead of a stepMPTmix object.

unique

A logical; if TRUE, then unique() is called on the result; for details see stepFlexmix.

which

number of model to get if k is a vector of integers longer than one. If character, interpreted as number of components or name of an information criterion.

spec, treeid, optimargs

arguments for the MPT model passed on to mptmodel.

Currently not used.

##### Details

Internally stepFlexmix is called with suitable arguments to fit the finite mixture model with the EM algorithm.

FLXMCmpt is the flexmix driver for MPT mixture models.

The interface is designed along the same lines as raschmix which is introduced in detail in Frick et al. (2012). However, the mptmix function has not yet been fully tested and may change in future versions.

The latent-class MPT model (Klauer, 2006) is equivalent to an MPT mixture model without concomitant variables.

MPT models are specified using the mptspec function. See the documentation in the mpt package for details.

##### Value

Either an object of class "mptmix" containing the best model with respect to the log-likelihood (if k is a scalar) or the one selected according to which (if specified and k is a vector of integers longer than 1) or an object of class "stepMPTmix" (if which is not specified and k is a vector of integers longer than 1).

##### References

Frick, H., Strobl, C., Leisch, F., and Zeileis, A. (2012). Flexible Rasch Mixture Models with Package psychomix. Journal of Statistical Software, 48(7), 1--25. http://www.jstatsoft.org/v48/i07/

Klauer, K.C. (2006). Hierarchical Multinomial Processing Tree Models: A Latent-Class Approach. Psychometrika, 71, 7--31. 10.1007/s11336-004-1188-3

flexmix, stepFlexmix

• mptmix
• FLXMCmpt
##### Examples
# NOT RUN {
# }
# NOT RUN {
## Data
data("PairClustering", package = "psychotools")
pc <- reshape(PairClustering, timevar = "trial", idvar = "ID",
direction = "wide")

## Latent-class MPT model (Klauer, 2006)
suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
m <- mptmix(as.matrix(pc[-1]) ~ 1, data = pc, k = 1:3,
spec = mptspec("SR", .replicates = 2))
m1 <- getModel(m, which = "BIC")

## Inspect results
summary(m1)
parameters(m1)
plot(m1)

library(lattice)
xyplot(m1)
# }
# NOT RUN {
# }

Documentation reproduced from package psychomix, version 1.1-8, License: GPL-2 | GPL-3

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