Generalized Linear Model Trees
Model-based recursive partitioning based on generalized linear models.
glmtree(formula, data, subset, na.action, weights, offset, cluster, family = gaussian, epsilon = 1e-8, maxit = 25, method = "glm.fit", ...)
formula |
symbolic description of the model (of type
|
data, subset, na.action |
arguments controlling formula processing
via |
weights |
optional numeric vector of weights. By default these are
treated as case weights but the default can be changed in
|
offset |
optional numeric vector with an a priori known component to be
included in the model |
cluster |
optional vector (typically numeric or factor) with a cluster ID to be employed for clustered covariances in the parameter stability tests. |
family, method |
specification of a family and fitting method for
|
epsilon, maxit |
control parameters passed to
|
... |
optional control parameters passed to
|
Compared to calling mob by hand, the implementation tries to avoid
unnecessary computations while growing the tree. Also, it provides a more
elaborate plotting function.
An object of class glmtree inheriting from modelparty.
The info element of the overall party and the individual
nodes contain various informations about the models.
Zeileis A, Hothorn T, Hornik K (2008). Model-Based Recursive Partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514.
if(require("mlbench")) {
## Pima Indians diabetes data
data("PimaIndiansDiabetes", package = "mlbench")
## recursive partitioning of a logistic regression model
pid_tree2 <- glmtree(diabetes ~ glucose | pregnant +
pressure + triceps + insulin + mass + pedigree + age,
data = PimaIndiansDiabetes, family = binomial)
## printing whole tree or individual nodes
print(pid_tree2)
print(pid_tree2, node = 1)
## visualization
plot(pid_tree2)
plot(pid_tree2, tp_args = list(cdplot = TRUE))
plot(pid_tree2, terminal_panel = NULL)
## estimated parameters
coef(pid_tree2)
coef(pid_tree2, node = 5)
summary(pid_tree2, node = 5)
## deviance, log-likelihood and information criteria
deviance(pid_tree2)
logLik(pid_tree2)
AIC(pid_tree2)
BIC(pid_tree2)
## different types of predictions
pid <- head(PimaIndiansDiabetes)
predict(pid_tree2, newdata = pid, type = "node")
predict(pid_tree2, newdata = pid, type = "response")
predict(pid_tree2, newdata = pid, type = "link")
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