Post-Prune modelparty Objects
Post-pruning of modelparty objects based on information
criteria like AIC, BIC, or related user-defined criteria.
prune.modelparty(tree, type = "AIC", ...)
tree |
object of class |
type |
pruning type. Can be |
... |
additional arguments. |
In mob-based model trees, pre-pruning based on p-values
is used by default and often no post-pruning is necessary in such trees.
However, if pre-pruning is switched off (by using a large alpha)
or does is not sufficient (e.g., possibly in large samples) the prune
method can be used for subsequent post-pruning based on information criteria.
The function prune.modelparty can be called directly but it is also
registered as a method for the generic prune function
from the rpart package. Thus, if rpart is attached,
prune(tree, type = "AIC", ...) also works (see examples below).
To customize the post-pruning strategy,
type can be set to a function(objfun, df, nobs)
which either returns TRUE to signal that a current node can be pruned
or FALSE. All supplied arguments are of length two: objfun is the sum of objective
function values in the current node and its child nodes, respectively.
df is the degrees of freedom in the current node and its child nodes,
respectively. nobs is vector with the number of observations in the
current node and the total number of observations in the dataset, respectively.
For "AIC" and "BIC" type is transformed so that AIC
or BIC are computed. However, this assumes that the objfun used in tree
is actually the negative log-likelihood. The degrees of freedom assumed for a split
can be set via the dfsplit argument in mob_control when computing
the tree or manipulated later by changing the value of tree$info$control$dfsplit.
An object of class modelparty where the associated tree is either the
same as the original or smaller.
set.seed(29)
n <- 1000
d <- data.frame(
x = runif(n),
z = runif(n),
z_noise = factor(sample(1:3, size = n, replace = TRUE))
)
d$y <- rnorm(n, mean = d$x * c(-1, 1)[(d$z > 0.7) + 1], sd = 3)
## glm versus lm / logLik versus sum of squared residuals
fmla <- y ~ x | z + z_noise
lm_big <- lmtree(formula = fmla, data = d, maxdepth = 3, alpha = 1)
glm_big <- glmtree(formula = fmla, data = d, maxdepth = 3, alpha = 1)
AIC(lm_big)
AIC(glm_big)
## load rpart for prune() generic
## (otherwise: use prune.modelparty directly)
if (require("rpart")) {
## pruning
lm_aic <- prune(lm_big, type = "AIC")
lm_bic <- prune(lm_big, type = "BIC")
width(lm_big)
width(lm_aic)
width(lm_bic)
glm_aic <- prune(glm_big, type = "AIC")
glm_bic <- prune(glm_big, type = "BIC")
width(glm_big)
width(glm_aic)
width(glm_bic)
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