Nothing
or.relimp.default <- function(model, ui, ci = NULL, index = 2:ncol(model),
meq = 0, tol = sqrt(.Machine$double.eps), ...)
{
## check input model
if (!(is.matrix(model)))
stop("ERROR: model must be of class lm or a covariance matrix.")
else
if (!(nrow(model)==ncol(model)))
stop("ERROR: If it is not a linear model, model must be a quadratic matrix.")
else
if (!(all(eigen(model,TRUE,only.values=TRUE)$values>0)))
stop("ERROR: matrix model must be positive definite.")
namen <- colnames(model)
if (is.null(namen)) namen <- c("y",paste("X",1:(ncol(model)-1),sep=""))
## work is done by functions all.R2 from this package
## and function Shapley.value from package kappalab
## output is currently very limited
## prepare data for calculation of sub models
aus <- Shapley.value(set.func(all.R2(model, ui, ci = ci, index = index,
meq = meq, tol = tol, ...)))
names(aus) <- namen[-1]
aus
}
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