partykit (version 1.2-2)

lmtree: Linear Model Trees

Description

Model-based recursive partitioning based on least squares regression.

Usage

lmtree(formula, data, subset, na.action, weights, offset, cluster, …)

Arguments

formula

symbolic description of the model (of type y ~ z1 + … + zl or y ~ x1 + … + xk | z1 + … + zl; for details see below).

data, subset, na.action

arguments controlling formula processing via model.frame.

weights

optional numeric vector of weights. By default these are treated as case weights but the default can be changed in mob_control.

offset

optional numeric vector with an a priori known component to be included in the model y ~ x1 + … + xk (i.e., only when x variables are specified).

cluster

optional vector (typically numeric or factor) with a cluster ID to be employed for clustered covariances in the parameter stability tests.

…

optional control parameters passed to mob_control.

Value

An object of class lmtree inheriting from modelparty. The info element of the overall party and the individual nodes contain various informations about the models.

Details

Convenience interface for fitting MOBs (model-based recursive partitions) via the mob function. lmtree internally sets up a model fit function for mob, using either lm.fit or lm.wfit (depending on whether weights are used or not). Then mob is called using the residual sum of squares as the objective function.

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.

References

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

See Also

mob, mob_control, glmtree

Examples

# NOT RUN {
if(require("mlbench")) {

## Boston housing data
data("BostonHousing", package = "mlbench")
BostonHousing <- transform(BostonHousing,
  chas = factor(chas, levels = 0:1, labels = c("no", "yes")),
  rad = factor(rad, ordered = TRUE))

## linear model tree
bh_tree <- lmtree(medv ~ log(lstat) + I(rm^2) | zn +
  indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
  data = BostonHousing, minsize = 40)

## printing whole tree or individual nodes
print(bh_tree)
print(bh_tree, node = 7)

## plotting
plot(bh_tree)
plot(bh_tree, tp_args = list(which = "log(lstat)"))
plot(bh_tree, terminal_panel = NULL)

## estimated parameters
coef(bh_tree)
coef(bh_tree, node = 9)
summary(bh_tree, node = 9)

## various ways for computing the mean squared error (on the training data)
mean((BostonHousing$medv - fitted(bh_tree))^2)
mean(residuals(bh_tree)^2)
deviance(bh_tree)/sum(weights(bh_tree))
deviance(bh_tree)/nobs(bh_tree)

## log-likelihood and information criteria
logLik(bh_tree)
AIC(bh_tree)
BIC(bh_tree)
## (Note that this penalizes estimation of error variances, which
## were treated as nuisance parameters in the fitting process.)

## different types of predictions
bh <- BostonHousing[c(1, 10, 50), ]
predict(bh_tree, newdata = bh, type = "node")
predict(bh_tree, newdata = bh, type = "response")
predict(bh_tree, newdata = bh, type = function(object) summary(object)$r.squared)

}


if(require("AER")) {

## Demand for economics journals data
data("Journals", package = "AER")
Journals <- transform(Journals,
  age = 2000 - foundingyear,
  chars = charpp * pages)

## linear regression tree (OLS)
j_tree <- lmtree(log(subs) ~ log(price/citations) | price + citations +
  age + chars + society, data = Journals, minsize = 10, verbose = TRUE)

## printing and plotting
j_tree
plot(j_tree)

## coefficients and summary
coef(j_tree, node = 1:3)
summary(j_tree, node = 1:3)

}


if(require("AER")) {

## Beauty and teaching ratings data
data("TeachingRatings", package = "AER")

## linear regression (WLS)
## null model
tr_null <- lm(eval ~ 1, data = TeachingRatings, weights = students,
  subset = credits == "more")
## main effects
tr_lm <- lm(eval ~ beauty + gender + minority + native + tenure + division,
  data = TeachingRatings, weights = students, subset = credits == "more")
## tree
tr_tree <- lmtree(eval ~ beauty | minority + age + gender + division + native + tenure,
   data = TeachingRatings, weights = students, subset = credits == "more",
   caseweights = FALSE)

## visualization
plot(tr_tree)

## beauty slope coefficient
coef(tr_lm)[2]
coef(tr_tree)[, 2]

## R-squared
1 - deviance(tr_lm)/deviance(tr_null)
1 - deviance(tr_tree)/deviance(tr_null)
}

# }