Input for data splitting algorithms
Parse hyperparameters for data splitting algorithm
getSplitMethod(split.method, B, N, M, seed)
split.method |
A character string specifying the algorithm for data splitting:
|
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. |
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
Thomas A. Gerds <tag@biostat.ku.dk>
Score
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.