lmtree  R Documentation 
Modelbased recursive partitioning based on least squares regression.
lmtree(formula, data, subset, na.action, weights, offset, cluster, ...)
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. 
... 
optional control parameters passed to

Convenience interface for fitting MOBs (modelbased 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.
An object of class lmtree
inheriting from modelparty
.
The info
element of the overall party
and the individual
node
s contain various informations about the models.
Zeileis A, Hothorn T, Hornik K (2008). ModelBased Recursive Partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514.
mob
, mob_control
, glmtree
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$medv  fitted(bh_tree))^2) mean(residuals(bh_tree)^2) deviance(bh_tree)/sum(weights(bh_tree)) deviance(bh_tree)/nobs(bh_tree) ## loglikelihood and information criteria logLik(bh_tree) AIC(bh_tree) BIC(bh_tree) ## (Note that this penalizes estimation of error variances, which ## were treated as nuisance parameters in the fitting process.) ## different types of predictions bh < BostonHousing[c(1, 10, 50), ] predict(bh_tree, newdata = bh, type = "node") predict(bh_tree, newdata = bh, type = "response") predict(bh_tree, newdata = bh, type = function(object) summary(object)$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] ## Rsquared 1  deviance(tr_lm)/deviance(tr_null) 1  deviance(tr_tree)/deviance(tr_null) }
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