bttree {psychotree} | R Documentation |
Recursive partitioning based on Bradley-Terry models.
bttree(formula, data, na.action = na.pass, type = "loglin", ref = NULL, undecided = NULL, position = NULL, minsplit = 10, ...)
formula |
A symbolic description of the model to be fit. This
should be of type y ~ x1 + x2
where y should be an object of class paircomp
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 . |
type, ref, undecided, position |
arguments for the Bradley-Terry
model passed on to btReg . |
minsplit, ... |
arguments passed to mob_control . |
Bradley-Terry tree models are an application of model-based recursive partitioning
(implemented in mob
) to Bradley-Terry models for
paired comparison data (implemented in btReg
).
For all details about the underlying theory and further explanations
of the illustrations from the example section can be found in
Strobl, Wickelmaier, Zeileis (2010).
Various methods are provided for "bttree"
objects, most of them
inherit their behavior from "mob"
objects (e.g., print
, summary
,
etc.). worth
behaves analogously to coef
and extracts the
worth parameters from the BT models in the nodes of the tree. The plot
method employs the node_btplot
panel-generating function.
An object of S3 class "bttree"
which is a list containing only
a single element of S4 class "mob"
(because this is currently not
exported).
Carolin Strobl, Florian Wickelmaier, Achim Zeileis (2010). Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning. Journal of Educational and Behavioral Statistics, Forthcoming. Preprint at http://statmath.wu.ac.at/~zeileis/papers/Strobl+Wickelmaier+Zeileis-2010.pdf
## package library("psychotree") ## Germany's Next Topmodel 2007 data data("Topmodel2007", package = "psychotree") ## BT tree tm_tree <- bttree(preference ~ ., data = Topmodel2007, minsplit = 5, ref = "Barbara") plot(tm_tree, abbreviate = 1, yscale = c(0, 0.5)) ## parameter instability tests in root node sctest(tm_tree, node = 1) ## worth parameters in terminal nodes worth(tm_tree) ## CEMS university choice data data("CEMSChoice", package = "psychotree") summary(CEMSChoice$preference) ## BT tree cems_tree <- bttree(preference ~ french + spanish + italian + study + work + gender + intdegree, data = CEMSChoice, minsplit = 5, ref = "London") plot(cems_tree, abbreviate = 1, yscale = c(0, 0.5)) worth(cems_tree)