rf.cv: The Cross-Validation of Classification and Regression models...
In wind22zhu/BioMedR: Generating Various Molecular Representations for Chemicals, Proteins, DNAs, RNAs and Their Interactions

Description Usage Arguments Details Value Author(s) References See Also Examples

The Cross-Validation of Classification and Regression models using Random Forest

1 2	rf.cv(xtr, ytr, cv.fold = 5, type = "regression", trees = 500, mtrysize = 10)

`xtr`	A data frame or a matrix of predictors.
`ytr`	A response vector. If a factor, classification is assumed, otherwise regression is assumed.
`cv.fold`	The fold, the defalut is 5.
`type`	method type.
`trees`	Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
`mtrysize`	Number of variables randomly sampled as candidates at each split. Note that the default values are different for classification (sqrt(p) where p is number of variables in `xtr`) and regression (p/3)

rf.cv implements Breiman's random forest algorithm for classification and regression. here we use it to make a k-fold cross-validation

if type is regression, the retrun a list containing four components:

RFpred - the predicted values of the input data based on cross-validation
Error - error for all samples
RMSECV - Root Mean Square Error for cross-validation
Q2 - R2 for cross-validation

if type is classification, the retrun a list containing four components:

table - confusion matrix
ACC - accuracy
SE - sensitivity
SP - specifivity
F1 - a measure of a test's accuracy.
MCC - Mathews correlation coefficient
RFPred - the predicted values
prob - the predicted probability values

Min-feng Zhu <wind2zhu@163.com>

Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

See pls.cv for the Cross-Validation of Classification and Regression models using PLS

training = read.csv(system.file('sysdata/training2.csv', package = 'BioMedR'), header = TRUE)
y = training[, 1]
x = training[, -1]
rf.tr <- rf.cv(x, y)