View source: R/SMI_significance.R
significant | R Documentation |
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.
significant(smi, B = 10000, replicates = NULL)
smi |
|
B |
integer number of permutations, default = 10000. |
replicates |
integer vector of replicates. |
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.
A matrix containing P-values for all combinations of components.
Kristian Hovde Liland
Similarity of Matrices Index - Ulf G. Indahl, Tormod Næs Kristian Hovde Liland
plot.SMI
(print.SMI/summary.SMI), RV
(RV2/RVadj), r1
(r2/r3/r4/GCD), allCorrelations
(matrix correlation comparison).
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
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