mat.ginv: Computes the generalized inverse of a matrix

In dae: Functions Useful in the Design and ANOVA of Experiments

Computes the generalized inverse of a matrix

Description

Computes the Moore-Penrose generalized inverse of a matrix.

Usage

mat.ginv(x, tol = .Machine$double.eps ^ 0.5)

Arguments

x	A matrix whose generalized inversed is to be computed.
tol	A numeric specifying the relative tolerance to determine whether an eigenvalue of x is nonzero.

Value

A matrix. An NA is returned if svd fails during the compution of the generalized inverse.

Author(s)

Chris Brien

Examples

## PBIBD(2) from p. 379 of Cochran and Cox (1957) Experimental Designs. 
## 2nd edn Wiley, New York
PBIBD2.unit <- list(Block = 6, Unit = 4)
PBIBD2.nest <- list(Unit = "Block")
trt <- factor(c(1,4,2,5, 2,5,3,6, 3,6,1,4, 4,1,5,2, 5,2,6,3, 6,3,4,1))
PBIBD2.lay <- designRandomize(allocated = trt, 
                              recipient = PBIBD2.unit, 
                              nested.recipients = PBIBD2.nest)
## Compute the projector for a linear trend across Blocks
PBIBD2.lay <- within(PBIBD2.lay,
                     {
                       cBlock <- as.numfac(Block)
                       cBlock <- cBlock - mean(unique(cBlock))
                     })
X <- model.matrix(~ cBlock, data = PBIBD2.lay)
Q.cB <- projector((X %*% mat.ginv(t(X) %*% X) %*% t(X)))

dae documentation built on June 22, 2024, 9:07 a.m.