significant: Significance estimation for Similarity of Matrices Index...

In MatrixCorrelation: Matrix Correlation Coefficients

Significance estimation for Similarity of Matrices Index (SMI)

Description

Permutation based hypothesis testing for SMI. The nullhypothesis is that a linear function of one matrix subspace is included in the subspace of another matrix.

Usage

significant(smi, B = 10000, replicates = NULL)

Arguments

smi	smi object returned by call to SMI.
B	integer number of permutations, default = 10000.
replicates	integer vector of replicates.

Details

For each combination of components significance is estimated by sampling from a null distribution of no similarity, i.e. when the rows of one matrix is permuted B times and corresponding SMI values are computed. If the vector replicates is included, replicates will be kept together through permutations.

Value

A matrix containing P-values for all combinations of components.

Author(s)

Kristian Hovde Liland

References

Similarity of Matrices Index - Ulf G. Indahl, Tormod Næs Kristian Hovde Liland

See Also

plot.SMI (print.SMI/summary.SMI), RV (RV2/RVadj), r1 (r2/r3/r4/GCD), allCorrelations (matrix correlation comparison).

Examples

X1  <- scale( matrix( rnorm(100*300), 100,300), scale = FALSE)
usv <- svd(X1)
X2  <- usv$u[,-3] %*% diag(usv$d[-3]) %*% t(usv$v[,-3])

(smi <- SMI(X1,X2,5,5))
significant(smi, B = 1000) # default B = 10000

MatrixCorrelation documentation built on April 19, 2022, 1:06 a.m.