PPforest: Projection Pursuit Random Forest

In PPforest: Projection Pursuit Classification Forest

Projection Pursuit Random Forest

Description

PPforest implements a random forest using projection pursuit trees algorithm (based on PPtreeViz package).

Usage

PPforest(data, class, std = TRUE, size.tr, m, PPmethod, size.p,
 lambda = .1, parallel = FALSE, cores = 2, rule = 1)

Arguments

data	Data frame with the complete data set.
class	A character with the name of the class variable.
std	if TRUE standardize the data set, needed to compute global importance measure.
size.tr	is the size proportion of the training if we want to split the data in training and test.
m	is the number of bootstrap replicates, this corresponds with the number of trees to grow. To ensure that each observation is predicted a few times we have to select this number no too small. m = 500 is by default.
PPmethod	is the projection pursuit index to optimize in each classification tree. The options are LDA and PDA, linear discriminant and penalized linear discriminant. By default it is LDA.
size.p	proportion of variables randomly sampled in each split.
lambda	penalty parameter in PDA index and is between 0 to 1 . If lambda = 0, no penalty parameter is added and the PDA index is the same as LDA index. If lambda = 1 all variables are treated as uncorrelated. The default value is lambda = 0.1.
parallel	logical condition, if it is TRUE then parallelize the function
cores	number of cores used in the parallelization
rule	split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size

Value

An object of class PPforest with components.

prediction.training	predicted values for training data set.
training.error	error of the training data set.
prediction.test	predicted values for the test data set if testap = TRUE(default).
error.test	error of the test data set if testap = TRUE(default).
oob.error.forest	out of bag error in the forest.
oob.error.tree	out of bag error for each tree in the forest.
boot.samp	information of bootrap samples.
output.trees	output from a trees_pp for each bootrap sample.
proximity	Proximity matrix, if two cases are classified in the same terminal node then the proximity matrix is increased by one in PPforest there are one terminal node per class.
votes	a matrix with one row for each input data point and one column for each class, giving the fraction of (OOB) votes from the PPforest.
n.tree	number of trees grown in PPforest.
n.var	number of predictor variables selected to use for spliting at each node.
type	classification.
confusion	confusion matrix of the prediction (based on OOB data).
call	the original call to PPforest.
train	is the training data based on size.tr sample proportion
test	is the test data based on 1-size.tr sample proportion

References

Natalia da Silva, Dianne Cook & Eun-Kyung Lee (2021) A Projection Pursuit Forest Algorithm for Supervised Classification, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2020.1870480

Examples

#crab example with all the observations used as training

pprf.crab <- PPforest(data = crab, class = 'Type',
 std = FALSE, size.tr = 1, m = 200, size.p = .5, 
 PPmethod = 'LDA' , parallel = TRUE, cores = 2, rule=1)
pprf.crab

PPforest documentation built on Sept. 10, 2022, 1:05 a.m.