RFcluster: Random forests estimate of predictive accuracy for clustered...

In gamclass: Functions and Data for a Course on Modern Regression and Classification

Random forests estimate of predictive accuracy for clustered data

Description

This function adapts random forests to work (albeit clumsily and inefficiently) with clustered categorical outcome data. For example, there may be multiple observations on individuals (clusters). Predictions are made fof the OOB (out of bag) clusters

Usage

RFcluster(formula, id, data, nfold = 15,
              ntree=500, progress=TRUE, printit = TRUE, seed = 29)

Arguments

formula	Model formula
id	numeric, identifies clusters
data	data frame that supplies the data
nfold	numeric, number of folds
ntree	numeric, number of trees (number of bootstrap samples)
progress	Print information on progress of calculations
printit	Print summary information on accuracy
seed	Set seed, if required, so that results are exactly reproducible

Details

Bootstrap samples are taken of observations in the in-bag clusters. Predictions are made for all observations in the OOB clusters.

Value

class	Predicted values from cross-validation
OOBaccuracy	Cross-validation estimate of accuracy
confusion	Confusion matrix

Author(s)

John Maindonald

References

https://maths-people.anu.edu.au/~johnm/nzsr/taws.html

Examples

## Not run: 
library(mlbench)
library(randomForest)
data(Vowel)
RFcluster(formula=Class ~., id = V1, data = Vowel, nfold = 15,
              ntree=500, progress=TRUE, printit = TRUE, seed = 29)

## End(Not run)

gamclass documentation built on Aug. 21, 2023, 5:07 p.m.