SSBtools (version 1.7.0)

GaussIndependent: Linearly independent rows and columns by Gaussian elimination

Description

The function is written primarily for large sparse matrices with integers and even more correctly it is primarily written for dummy matrices (0s and 1s in input matrix).

Usage

GaussIndependent(
  x,
  printInc = FALSE,
  tolGauss = (.Machine$double.eps)^(1/2),
  testMaxInt = 0,
  allNumeric = FALSE
)
GaussRank(x, printInc = FALSE)

Value

List of logical vectors specifying independent rows and columns

Arguments

x

A (sparse) matrix

printInc

Printing "..." to console when TRUE

tolGauss

A tolerance parameter for sparse Gaussian elimination and linear dependency. This parameter is used only in cases where integer calculation cannot be used.

testMaxInt

Parameter for testing: The Integer overflow situation will be forced when testMaxInt is exceeded

allNumeric

Parameter for testing: All calculations use numeric algorithm (as integer overflow) when TRUE

Details

GaussRank returns the rank

Examples

x <- ModelMatrix(SSBtoolsData("z2"), formula = ~fylke + kostragr * hovedint - 1)

GaussIndependent(x)
GaussRank(x)
GaussRank(t(x))

if (FALSE) {
# For comparison, qr-based rank may not work
rankMatrix(x, method = "qr")

# Dense qr works 
qr(as.matrix(x))$rank
}