maigesPack (version 1.36.0)

bootstrapCor: Calculate bootstrap p-values for correlation measures

Description

This function takes a numerical matrix (or two vectors) and calculates bootstrapped (by permutation) p-values to test if the correlation value is equal to zero. If the first argument is a matrix, the p-values are calculated between all pairs of rows of the matrix.

Usage

bootstrapCor(x, y=NULL, bRep, type="Rpearson", ret="p-value", alternative="two.sided")

Arguments

x

numerical matrix or vector to be analysed. If a vector, the argument y must be informed.

y

numerical vector. Must be informed if x is a vector. If x is a matrix, this argument is ignored. Defaults to NULL.

bRep

number of permutation to be done in the test.

type

character string specifying the type of correlation statistic to be used. Possible values are 'Rpearson', 'pearson', 'spearman' or 'kendall'.

ret

character string with the value to return. Must be 'p-value' (default) for the usual p-value or 'max', to return the maximum absolute correlation value obtained by the permutation.

alternative

character specifying the type of test to do, must be 'two.sided' (default), 'less' or 'greater'.

Value

The result of this function is a square matrix (length equal to the number of rows of x) if x is a matrix or a numerical value if x and y are vectors. The result is the p-values or maximum correlation values calculated by permutation tests.

Details

Pearson, spearman and kendall types of correlation values are calculated by cor function from package stats. The method Rpearson was developed in this package and is a generalisation of the jackniffe correlation proposed by Heyer et al. (1999), it is calculated using the function robustCorr.

References

Heyer, L.J.; Kruglyak, S. and Yooseph, S. Exploring expression data: identification and analysis of coexpressed genes, Genome Research, 9, 1106-1115, 1999 (http://www.genome.org/cgi/content/full/9/11/1106)

See Also

cor, robustCorr

Examples

x <- runif(50, 0, 1)
y <- rbeta(50, 1, 2)
bootstrapCor(x, y, bRep=100)

z <- matrix(rnorm(100, 0, 1), 4, 25)
bootstrapCor(z, bRep=100)