getSplitMethod {riskRegression}

Input for data splitting algorithms

Description

Parse hyperparameters for data splitting algorithm

Usage

getSplitMethod(split.method, B, N, M, seed)

Arguments

split.method

A character string specifying the algorithm for data splitting:

• "loob" leave one out bootstrap

• "bootcv" bootstrap cross validation

• "cv5" 5-fold cross validation

• "loocv" leave one out cross validation aka N-1 fold cross validation

• "632plus" Efron's .632+ bootstrap

B

Number of repetitions of bootstrap or k-fold cross-validation

N

Sample size

M

Subsample size. Default is N (no subsampling).

seed

Integer passed to set.seed. If not given or NA no seed is set.

Value

A list with the following elements:

• split.methodName: the print name of the algorithm

• split.method: the internal name of the algorithm

• index: the index for data splitting. For bootstrap splitting this is a matrix with B columns and M rows identifying the in-bag subjects. For k-fold cross-validation this is a matrix with B columns identifying the membership to the k groups.

• k: the k of k-fold cross-validation

• N: the sample size

• M: the subsample size

Author(s)

Thomas A. Gerds <[e-mail]>

See Also

Score

Examples

# 3-fold crossvalidation
getSplitMethod("cv3",B=4,N=37)

# bootstrap with replacement
getSplitMethod("loob",B=4,N=37)

# bootstrap without replacement
getSplitMethod("loob",B=4,N=37,M=20)