Package 'glmtree'

Generates data from two logistic regression trees.

Description

This function generates data from two logistic regression trees: one with three apparent clusters (in terms of variance of the features) but a single logistic regression generating y | x, and one with a single apparent cluster but three different logistic regressions generating y | x given a categorical feature.

Usage

generateData(n = 100, scenario = "tree", visualize = FALSE)

Arguments

n	The number of observations to draw.
scenario	The "no tree" scenario denotes the first scenario where there is a single logistic regression generating the data. The "tree" scenario generates data from the second data generating mechanism where there are three logistic regressions.
visualize	Whether (TRUE) or not (FALSE) to plot the generated data.

Value

Generates and returns data according to a true logistic regression tree (if scenario = "tree") or a single regression tree (if scenario = "no tree"). Eventually plots this dataset (if visualize = TRUE).

Author(s)

Adrien Ehrhardt

Examples

generateData(scenario = "tree")

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Class glmtree

Description

Class glmtree represents a logistic regression tree scheme associated with its optimal logistic regression models.

Slots

parameters

The parameters associated with the method.

best.tree

The best discretization scheme found by the method given its parameters.

performance

The performance obtained with the method given its parameters.

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Logistic regression tree by Stochastic-Expectation-Maximization

Description

This function produces a logistic regression tree: a decision tree with logistic regressions at its leaves.

Usage

glmtree(
  x,
  y,
  K = 10,
  iterations = 200,
  test = FALSE,
  validation = FALSE,
  proportions = c(0.3),
  criterion = "bic",
  ctree_controls = partykit::ctree_control(alpha = 0.1, minbucket = 100, maxdepth = 5)
)

Arguments

x	The features to use for prediction.
y	The binary / boolean labels to predict.
K	The number of segments to start with (maximum number of segments there'll be in the end).
iterations	The number of iterations to do in the SEM protocole (default: 200).
test	Boolean : True if the algorithm should use predictors to construct a test set on which to calculate the provided criterion using the best discretization scheme (chosen thanks to the provided criterion on either the test set (if true) or the training set (otherwise)) (default: TRUE).
validation	Boolean : True if the algorithm should use predictors to construct a validation set on which to search for the best discretization scheme using the provided criterion (default: TRUE).
proportions	The list of the proportions wanted for test and validation set. Not used when both test and validation are false. Only the first is used when there is only one of either test or validation that is set to TRUE. Produces an error when the sum is greater to one. Default: list(0.2,0.2) so that the training set has 0.6 of the input observations.
criterion	The criterion ('gini','aic','bic') to use to choose the best discretization scheme among the generated ones (default: 'gini'). Nota Bene: it is best to use 'gini' only when test is set to TRUE and 'aic' or 'bic' when it is not. When using 'aic' or 'bic' with a test set, the likelihood is returned as there is no need to penalize for generalization purposes.
ctree_controls	The controls to use for 'partykit::ctree'.

Value

An S4 object of class 'glmtree' that contains the parameters used to search for the logistic regression tree, the best tree it found, and its performance.

Author(s)

Adrien Ehrhardt

Examples

data <- generateData(n = 100, scenario = "no tree")
glmtree(x = data[, c("x1", "x2")], y = data$y, K = 5, iterations = 80, criterion = "aic")

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Calculating the normalized Gini index

Description

This function calculates the Gini index of a classification rule outputting probabilities. It is a classical metric in the context of Credit Scoring. It is equal to 2 times the AUC (Area Under ROC Curve) minus 1.

Usage

normalizedGini(actual, predicted)

Arguments

actual	The numeric binary vector of the actual labels observed.
predicted	The vector of the probabilities predicted by the classification rule.

Value

The Gini index of the predicted probabilities as a single 'num'.

Author(s)

Adrien Ehrhardt

Examples

normalizedGini(c(1, 1, 1, 0, 0), c(0.7, 0.9, 0.5, 0.6, 0.3))

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