psychotree (version 0.15-3)

mpttree: MPT Trees

Description

Recursive partitioning (also known as trees) based on multinomial processing tree (MPT) models.

Usage

mpttree(formula, data, na.action, cluster, spec, treeid = NULL,
  optimargs = list(control = list(reltol = .Machine$double.eps^(1/1.2),
                                  maxit = 1000)), …)

Arguments

formula

a symbolic description of the model to be fit. This should be of type y ~ x1 + x2 where y should be a matrix of response frequencies and x1 and x2 are used as partitioning variables.

data

an optional data frame containing the variables in the model.

na.action

a function which indicates what should happen when the data contain NAs, defaulting to na.pass.

cluster

optional vector (typically numeric or factor) with a cluster ID to be employed for clustered covariances in the parameter stability tests.

spec, treeid, optimargs

arguments for the MPT model passed on to mptmodel.

…

arguments passed to mob_control.

Value

An object of S3 class "mpttree" inheriting from class "modelparty".

Details

MPT trees (Wickelmaier & Zeileis, 2018) are an application of model-based recursive partitioning (implemented in mob) to MPT models (implemented in mptmodel).

Various methods are provided for "mpttree" objects, most of them inherit their behavior from "mob" objects (e.g., print, summary, etc.). The plot method employs the node_mptplot panel-generating function.

References

Wickelmaier F, Zeileis A (2018). Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models. Behavior Research Methods, 50(3), 1217--1233. 10.3758/s13428-017-0937-z

See Also

mob, mptmodel.

Examples

# NOT RUN {
o <- options(digits = 4)

## Source Monitoring data
data("SourceMonitoring", package = "psychotools")

## MPT tree
sm_tree <- mpttree(y ~ sources + gender + age, data = SourceMonitoring,
  spec = mptspec("SourceMon", .restr = list(d1 = d, d2 = d)))
plot(sm_tree, index = c("D1", "D2", "d", "b", "g"))

## extract parameter estimates
coef(sm_tree)

## parameter instability tests in root node
if(require("strucchange")) sctest(sm_tree, node = 1)

## storage and retrieval deficits in psychiatric patients
data("MemoryDeficits", package = "psychotools")
MemoryDeficits$trial <- ordered(MemoryDeficits$trial)

## MPT tree
sr_tree <- mpttree(cbind(E1, E2, E3, E4) ~ trial + group,
  data = MemoryDeficits, cluster = ID, spec = mptspec("SR2"), alpha = 0.1)

## extract parameter estimates
coef(sr_tree)

options(digits = o$digits)
# }