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

significant {MatrixCorrelation} | R Documentation

## 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

[Package MatrixCorrelation version 0.9.5 Index]