mpttree | R Documentation |
Recursive partitioning (also known as trees) based on multinomial processing tree (MPT) models.
mpttree(formula, data, na.action, cluster, spec, treeid = NULL,
optimargs = list(control = list(reltol = .Machine$double.eps^(1/1.2),
maxit = 1000)), ...)
formula |
a symbolic description of the model to be fit. This should be
of type |
data |
an optional data frame containing the variables in the model. |
na.action |
a function which indicates what should happen when the data
contain |
cluster |
optional vector (typically numeric or factor) with a cluster ID to be employed for clustered covariances in the parameter stability tests. |
spec , treeid , optimargs |
arguments for the MPT model passed on to
|
... |
arguments passed to |
MPT trees (Wickelmaier & Zeileis, 2018) are an application of
model-based recursive partitioning (implemented in
mob
) to MPT models (implemented in
mptmodel
).
Various methods are provided for "mpttree"
objects, most of them
inherit their behavior from "mob"
objects (e.g., print
,
summary
, etc.). The plot
method employs the
node_mptplot
panel-generating function.
An object of S3 class "mpttree"
inheriting from class
"modelparty"
.
Wickelmaier F, Zeileis A (2018). Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models. Behavior Research Methods, 50(3), 1217–1233. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-017-0937-z")}
mob
, mptmodel
.
o <- options(digits = 4)
## Source Monitoring data
data("SourceMonitoring", package = "psychotools")
## MPT tree
sm_tree <- mpttree(y ~ sources + gender + age, data = SourceMonitoring,
spec = mptspec("SourceMon", .restr = list(d1 = d, d2 = d)))
plot(sm_tree, index = c("D1", "D2", "d", "b", "g"))
## extract parameter estimates
coef(sm_tree)
## parameter instability tests in root node
if(require("strucchange")) sctest(sm_tree, node = 1)
## storage and retrieval deficits in psychiatric patients
data("MemoryDeficits", package = "psychotools")
MemoryDeficits$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)
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