betareg (version 3.2-1)

betatree: Beta Regression Trees

Description

Fit beta regression trees via model-based recursive partitioning.

Usage

betatree(formula, partition,
  data, subset = NULL, na.action = na.omit, weights, offset, cluster,
  link = "logit", link.phi = "log", control = betareg.control(),
  ...)

Value

betatree() returns an object of S3 class "betatree" which inherits from "modelparty".

Arguments

formula

symbolic description of the model of type y ~ x or y ~ x | z, specifying the variables influencing mean and precision of y, respectively. For details see betareg.

partition

symbolic description of the partitioning variables, e.g., ~ p1 + p2. The argument partition can be omitted if formula is a three-part formula of type y ~ x | z | p1 + p2.

data, subset, na.action, weights, offset, cluster

arguments controlling data/model processing passed to mob.

link

character specification of the link function in the mean model (mu). Currently, "logit", "probit", "cloglog", "cauchit", "log", "loglog" are supported. Alternatively, an object of class "link-glm" can be supplied.

link.phi

character specification of the link function in the precision model (phi). Currently, "identity", "log", "sqrt" are supported. Alternatively, an object of class "link-glm" can be supplied.

control

a list of control arguments for the beta regression specified via betareg.control.

...

further control arguments for the recursive partitioning passed to mob_control.

Details

Beta regression trees are an application of model-based recursive partitioning (implemented in mob, see Zeileis et al. 2008) to beta regression (implemented in betareg, see Cribari-Neto and Zeileis 2010). See also Grün at al. (2012) for more details.

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

References

Cribari-Neto F, Zeileis A (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1--24. tools:::Rd_expr_doi("10.18637/jss.v034.i02")

Grün B, Kosmidis I, Zeileis A (2012). Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned. Journal of Statistical Software, 48(11), 1--25. tools:::Rd_expr_doi("10.18637/jss.v048.i11")

Zeileis A, Hothorn T, Hornik K (2008). Model-Based Recursive Partitioning. Journal of Computational and Graphical Statistics, 17(2), 492--514.

See Also

betareg, betareg.fit, mob

Examples

options(digits = 4)
suppressWarnings(RNGversion("3.5.0"))

## data with two groups of dyslexic and non-dyslexic children
data("ReadingSkills", package = "betareg")
## additional random noise (not associated with reading scores)
set.seed(1071)
ReadingSkills$x1 <- rnorm(nrow(ReadingSkills))
ReadingSkills$x2 <- runif(nrow(ReadingSkills))
ReadingSkills$x3 <- factor(rnorm(nrow(ReadingSkills)) > 0)

## fit beta regression tree: in each node
##   - accurcay's mean and precision depends on iq
##   - partitioning is done by dyslexia and the noise variables x1, x2, x3
## only dyslexia is correctly selected for splitting
bt <- betatree(accuracy ~ iq | iq, ~ dyslexia + x1 + x2 + x3,
  data = ReadingSkills, minsize = 10)
plot(bt)

## inspect result
coef(bt)
if(require("strucchange")) sctest(bt)
## IGNORE_RDIFF_BEGIN
summary(bt, node = 2)
summary(bt, node = 3)
## IGNORE_RDIFF_END

## add a numerical variable with relevant information for splitting
ReadingSkills$x4 <- rnorm(nrow(ReadingSkills), c(-1.5, 1.5)[ReadingSkills$dyslexia])

bt2 <- betatree(accuracy ~ iq | iq, ~ x1 + x2 + x3 + x4,
  data = ReadingSkills, minsize = 10)
plot(bt2)

## inspect result
coef(bt2)
if(require("strucchange")) sctest(bt2)
## IGNORE_RDIFF_BEGIN
summary(bt2, node = 2)
summary(bt2, node = 3)
## IGNORE_RDIFF_END