# mpttree

##### MPT Trees

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

- Keywords
- tree

##### 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

`NA`

s, 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`

.

##### 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.

##### Value

An object of S3 class `"mpttree"`

inheriting from class
`"modelparty"`

.

##### 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

##### 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)
# }
```

*Documentation reproduced from package psychotree, version 0.15-3, License: GPL-2 | GPL-3*