setup_GLMTree: Setup GLMTree Hyperparameters

In rtemis: Schema-Driven Machine Learning and Visualization

Setup GLMTree Hyperparameters

Description

Setup hyperparameters for GLMTree: model-based recursive partitioning, a tree carrying a generalized linear model in each leaf.

Usage

setup_GLMTree(
  alpha = 0.05,
  minsize = NULL,
  maxdepth = NULL,
  mtry = NULL,
  trim = 0.1,
  prune = NULL,
  numsplit = "left",
  catsplit = "binary",
  ordinal = "chisq",
  ifw = FALSE,
  regressors = NULL,
  partitioning_variables = NULL,
  bonferroni = TRUE,
  breakties = FALSE,
  restart = TRUE,
  dfsplit = TRUE,
  vcov = "opg",
  nrep = 10000L
)

Arguments

alpha	(Tunable) Numeric (0, 1]: Significance level a parameter-instability test must reach before a node is split.
minsize	(Tunable) Optional Integer [1, Inf): Fewest cases a node may hold. NULL uses ten times the number of parameters in a leaf's model.
maxdepth	(Tunable) Optional Integer [1, Inf): Deepest the tree may grow. NULL is unlimited.
mtry	(Tunable) Optional Integer [1, Inf): Partitioning variables sampled at each node. NULL tests every one.
trim	(Tunable) Numeric [0, Inf): Trimming for the split-point search; a fraction of cases below 1, a count at or above it.
prune	(Tunable) Optional Character {"AIC", "BIC"}: Post-prune the grown tree by an information criterion. NULL keeps every split.
numsplit	(Tunable) Character {"left", "center"}: Where a numeric split is placed.
catsplit	(Tunable) Character {"binary", "multiway"}: How a categorical variable is split.
ordinal	(Tunable) Character {"chisq", "max", "L2"}: Test statistic for an ordered partitioning variable.
ifw	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.
regressors	Optional Character vector: Features entering the linear model in each leaf. NULL uses every feature.
partitioning_variables	Optional Character vector: Features the tree may split on. NULL uses every feature.
bonferroni	Logical: If TRUE, Bonferroni-adjust the test p-values for the number of partitioning variables.
breakties	Logical: If TRUE, break ties in numeric partitioning variables by adding noise.
restart	Logical: If TRUE, restart each leaf's model from scratch rather than from its parent's fit.
dfsplit	Logical: If TRUE, count each split against the degrees of freedom when pruning.
vcov	Character {"opg", "info", "sandwich"}: Covariance estimator the instability test is built on.
nrep	Integer [1, Inf): Simulation replicates for the p-value of an ordinal split statistic.

Details

Where a decision tree predicts a constant in each leaf, a GLMTree fits a regression there, so the model reads as "in this subgroup, these are the coefficients". partykit::lmtree() fits a regression, partykit::glmtree() a binary logistic one; rtemis selects between them from the outcome.

Value

GLMTreeHyperparameters object.

How it splits

Not by loss reduction. A node is split when a parameter-instability test says its model's coefficients are not constant across a candidate partitioning variable – a structural change test, at significance level alpha. That is the method's distinguishing feature and the source of both its strengths and its limits:

It has a hypothesis-testing foundation

Splits carry p-values, and alpha is a significance level with a direct interpretation.

It sees a slope change with no shift in the mean

Which a tree splitting on the response cannot: there is nothing in the mean to find.

It needs statistical power

With few cases, many partitioning variables, or both, the tests do not reach alpha and the tree does not split at all – returning a single global model.

When it will not split, check minsize before alpha

The default minsize is ten times the number of parameters in a leaf's model, and that scales with the number of regressors. A split has to leave minsize cases on each side, so with p regressors no split is possible below n = 20 * (p + 1) – 420 cases at 20 regressors, whatever alpha says. A tree that returns one leaf on a wide design has usually hit this, not the significance level, and raising alpha will not move it. Set minsize directly, or narrow regressors.

regressors and partitioning_variables are separable, and the separation is the point: the model in each leaf need not read the variables the tree splits on. Both default to every feature.

Interpretability

Every leaf carries a full model over regressors – there is no selection or shrinkage, so a leaf holds one coefficient per regressor whether or not it earned one. Restrict regressors to keep that number down.

What it does not provide

No variable-importance measure: partykit has one for cforest and for constant-fit trees, and none for a model-based tree. get_varimp() therefore returns NULL, and get_varimp(explain(mod, newdata)) is the route to a per-feature measure.

Author(s)

EDG

References

Zeileis A, Hothorn T, Hornik K (2008). Model-Based Recursive Partitioning. Journal of Computational and Graphical Statistics, 17(2), 492-514. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/106186008X319331")}

Examples

glmtree_hyperparams <- setup_GLMTree(alpha = 0.01)
glmtree_hyperparams

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.