Nothing
# enumerate linear combinations
enumLC <- function(object, ...) UseMethod("enumLC")
enumLC.default <- function(object, ...)
{
# there doesn't seem to be a reasonable default, so
# we'll throw an error
stop(paste('enumLC does not support ', class(object), 'objects'))
}
enumLC.matrix <- function(object, ...)
{
# factor the matrix using QR decomposition and then process it
internalEnumLC(qr(object))
}
enumLC.lm <- function(object, ...)
{
# extract the QR decomposition and the process it
internalEnumLC(object$qr)
}
enumLC.formula <- function(object, ...)
{
# create an lm fit object from the formula, and then call
# appropriate enumLC method
enumLC(lm(object))
}
# this function does the actual work for all of the enumLC methods
internalEnumLC <- function(qrObj, ...)
{
R <- qr.R(qrObj) # extract R matrix
numColumns <- dim(R)[2] # number of columns in R
rank <- qrObj$rank # number of independent columns
pivot <- qrObj$pivot # get the pivot vector
if (is.null(numColumns) || rank == numColumns)
{
list() # there are no linear combinations
} else {
p1 <- 1:rank
X <- R[p1, p1] # extract the independent columns
Y <- R[p1, -p1, drop = FALSE] # extract the dependent columns
b <- qr(X) # factor the independent columns
b <- qr.coef(b, Y) # get regression coefficients of
# the dependent columns
b[abs(b) < 1e-6] <- 0 # zap small values
# generate a list with one element for each dependent column
lapply(1:dim(Y)[2],
function(i) c(pivot[rank + i], pivot[which(b[,i] != 0)]))
}
}
findLinearCombos <- function(x)
{
if(!is.matrix(x)) x <- as.matrix(x)
lcList <- enumLC(x)
initialList <- lcList
badList <- NULL
if(length(lcList) > 0)
{
continue <- TRUE
while(continue)
{
# keep removing linear dependencies until it resolves
tmp <- unlist(lapply(lcList, function(x) x[1]))
tmp <- unique(tmp[!is.na(tmp)])
badList <- unique(c(tmp, badList))
lcList <- enumLC(x[,-badList])
continue <- (length(lcList) > 0)
}
} else badList <- NULL
list(linearCombos = initialList, remove = badList)
}
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