init_values function

Training and validation samples from data

Draw training and test samples from data. Samples can be accessed by subsctioting original data or by their own references.

init_values(X, Y = NULL, sample.size = 0.5, 
data.splitting = "ALL", 
unit.scaling = FALSE, 
scaling = FALSE, 
regression = FALSE)

Arguments

• X: a matrix or dataframe to be splitted in training and validation sample
• Y: a response vector for the observed data.
• sample.size: size of the needed training sample in proportion of the nulber of observations in original data.
• data.splitting: not currently used.
• unit.scaling: if TRUE, scale all data in X between 0 and 1, if they are all positive, or between -1 and 1.
• scaling: if TRUE, centers and scales data, so each variable willhave mean 0 abd variance 1.
• regression: if TRUE and scaling = TRUE, Y will also be scaled.

Returns

a list with the following components : - xtrain: a matrix or data frame representing the training sample.

• ytrain: a response vector representing the training responses according to the training sample.

• xtest: a matrix or data frame representing the validation sample.

• ytest: a response vector representing the validation responses according to the validation sample.

• train_idx: subscripts of the training sample.

• test_idx: subscripts of the validation sample.

Author(s)

Saip Ciss saip.ciss@wanadoo.fr

Examples

data(iris)
Y <- iris$Species
X <- iris[,-which(colnames(iris) == "Species")]
trainingAndValidationsamples <- init_values(X, Y, sample.size = 0.5)

Xtrain = trainingAndValidationsamples$xtrain
Ytrain = trainingAndValidationsamples$ytrain
Xvalid = trainingAndValidationsamples$xtest
Yvalid = trainingAndValidationsamples$ytest