CORmat2vecPermu {conMItion} | R Documentation |
Permuted Correlation Between Matrix and Vector
Description
Computes the correlation between a randomly sampled vector from a matrix and a given vector. The sampling is done multiple times to generate a distribution.
Usage
CORmat2vecPermu(
mat,
vec,
cor_type = "pearson",
bulkIdx = 0,
permutationTimes = 1000,
seedNum = 99999999
)
Arguments
mat |
A numeric matrix. For example, each row represents a gene and each column represents a sample. |
vec |
A numeric vector, with length equal to the number of columns in 'mat'. |
cor_type |
Type of correlation to calculate: "Pearson", "Kendall", or "Spearman". Default is "Pearson". |
bulkIdx |
Index to divide the task when processing many permutations. Default is 0. |
permutationTimes |
Number of permutations for sampling. Default is 1000. |
seedNum |
Seed for random number generation. Default is 99999999. |
Value
A numeric vector of correlation values for each permutation.
Examples
mat <- matrix(rnorm(10000), nrow = 100, ncol = 100)
vec <- rnorm(100)
CORmat2vecPermu(mat, vec)
[Package conMItion version 0.2.0 Index]