minv {vcvComp} | R Documentation |
Matrix pseudoinverse
Description
Computes the inverse or the pseudoinverse of a matrix
Usage
minv(M, method = 0, pa = 0)
Arguments
M |
a numeric matrix (square matrix) |
method |
an integer for the method of inversion. If method = 0, only the nonzero eigenvalues are kept; if method = 1, only the eigenvalues above a threshold are kept; if method = 2, only the several first eigenvalues are kept; if method = 3, a Tikhonov regularization (= ridge regression) is performed. |
pa |
an integer for the parameter of inversion. If method = 1, pa is the threshold below which the eigenvalues are not kept; if method = 2, pa is an positive integer number corresponding to number of eigenvalues that are kept; if method = 3, pa is the scaling factor for the identity matrix |
Value
A numeric matrix corresponding to the pseudoinverse of M
Examples
# Data matrix of 2D landmark coordinates
data("Tropheus.IK.coord")
coords <- which(names(Tropheus.IK.coord) == "X1"):which(names(Tropheus.IK.coord) == "Y19")
proc.coord <- as.matrix(Tropheus.IK.coord[coords])
# Covariance matrix of each population
S.phen.pop <- cov.group(proc.coord, groups = Tropheus.IK.coord$POP.ID)
# Pseudo-inversion of a square matrix (covariance matrix of the population IKS5)
S2 <- S.phen.pop[, , "IKS5"]
invS2 <- minv(S2, method = 0, pa = 0) # Pseudoinverse keeping non-zero eigenvalues
invS2 <- minv(S2, method = 1, pa = 10^-8) # Pseudoinverse keeping eigenvalues above 10^-8
invS2 <- minv(S2, method = 2, pa = 5) # Pseudoinverse keeping the first five eigenvalues
invS2 <- minv(S2, method = 3, pa = 0.5) # Ridge regression with Tikhonov factor of 0.5
[Package vcvComp version 1.0.2 Index]