generic.cv {randomUniformForest}

Generic k-fold cross-validation

Description

Performs k-fold cross-validation 'n' times for any specified algorithm, using two of many metrics(test error, AUC, precision,...)

Usage

generic.cv(X, Y, 
nTimes = 1, 
k = 10, 
seed = 2014, 
regression = TRUE, 
genericAlgo = NULL, 
specificPredictFunction = NULL, 
metrics = c("none", "AUC", "precision", "F-score", "L1", "geometric mean", 
"geometric mean (precision)"))

Arguments

X

a matrix or dataframe of observations

Y

a vector (a factor for classification) for the observed data.

nTimes

number of times that k-fold cross-validation need to be performed.

k

how many folds ?

seed

the seed for reproducibility.

regression

if TRUE, performs regression.

genericAlgo

wrapper function to embed the algorithm that one needs to assess. One can eventually add options. NULL is only for convenience. Wrapper function is needed to assess cross-validation.

specificPredictFunction

if the assessed model does not support the R generic method 'predict', one has to define here, with a function, how predictions have to be generated.

metrics

One of many other metrics one can call with the standard one, test error (or MSE for regression).

Value

a list with the following components :

testError

the values of test error.

avgError

mean of test error.

stdDev

standard deviation of test error.

metric

values of the other chosen metric.

Author(s)

Saip Ciss [e-mail]

Examples

## not run
# data(iris)
# Y <- iris$Species
# X <- iris[,-which(colnames(iris) == "Species")]

## 10-fold cross-validation for the randomUniformForest algorithm:

## create the wrapper function (setting 'threads = 1' since data are small)
# genericAlgo.ruf <- function(X, Y) randomUniformForest(X, Y, 
# OOB = FALSE, importance = FALSE, threads = 1)

## run
# rUF.10cv.iris <- generic.cv(X, as.factor(Y), 
# genericAlgo = genericAlgo.ruf, regression = FALSE)
  
## 10-fold cross-validation for the randomForest algorithm:

## create the wrapper function
# require(randomForest) || install.packages("randomForest")
# genericAlgo.rf <- function(X, Y) randomForest(X, Y)

## run
# RF.10cv.iris <- generic.cv(X, as.factor(Y), 
# genericAlgo = genericAlgo.rf, regression = FALSE)

## 10-fold cross-validation for Gradient Boosting Machines algorithm (gbm package)

## create the wrapper function
# require(gbm) || install.packages("gbm")
# genericAlgo.gbm <- function(X, Y) gbm.fit(X, Y, distribution = "multinomial",
# n.trees = 500, shrinkage = 0.05, interaction.depth = 24, n.minobsinnode = 1) 

## create a wrapper for the prediction function of gbm
# nClasses = length(unique(Y))
# specificPredictFunction.gbm <- function(model, newdata)
# {
#	modelPrediction = predict(model, newdata, 500) 
#	predictions = matrix(modelPrediction, ncol = nClasses )
#	colnames(predictions) = colnames(modelPrediction)
#	return(as.factor(apply(predictions, 1, function(Z) names(which.max(Z)))))
# }

## run
# gbm.10cv.iris <- generic.cv(X, Y, genericAlgo = genericAlgo.gbm, 
# specificPredictFunction = specificPredictFunction.gbm, regression = FALSE)

## 10-fold cross-validation for CART algorithm (rpart package):

# genericAlgo.CART <- function(X, Y) 
#{
#	ZZ = data.frame(Y, X)
#	if (is.factor(Y)) { modelObject = rpart(Y ~., data = ZZ, method = "class", ...)	}
#	else { 	modelObject = rpart(Y ~., data = ZZ, ...) }
#	return(modelObject) 
#}

# specificPredictFunction.CART <- function(model, newdata)
# predict(model, data.frame(newdata), type= "vector")

# CART.10cv.iris <- generic.cv(X, as.factor(Y), genericAlgo = genericAlgo.CART, 
# specificPredictFunction = specificPredictFunction.CART, regression = FALSE)