rf.cv: The Cross-Validation of Classification and Regression models...

In BioMedR: Generating Various Molecular Representations for Chemicals, Proteins, DNAs, RNAs and Their Interactions

Description

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

Usage

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

Arguments

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)

Details

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

Value

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

Author(s)

Min-feng Zhu <[e-mail]>

References

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

See Also

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

Examples

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

Example output

OpenJDK 64-Bit Server VM warning: Can't detect primordial thread stack location - find_vma failed
.....

BioMedR documentation built on July 5, 2019, 9:03 a.m.