R/relWeights.R

In jgodet/utilitR: Lots of useful functions

# relWeights.r
# written by JuG
# Fri Dec  1 21:58:59 2017"



#' Functions for predictor selection
#' @author JuG
#' @description Predictor selection.The total amount of variance accounted for by the model is divided amoong the predicator variables.
#' @param fit  a models lm object.
#' @return graphic for rredictor selection
#' @examples
#'
#' form = as.formula("Fertility ~ .")
#' allsubreg(formula = form, data = swiss, nbest=6)
#'
#' if(!require('MASS')){install.packages('MASS')}
#' library('MASS')
#' fit1 <- lm(Fertility ~ ., data = swiss)
#' relWeights(fit1, col="lightgrey",las=1)
#'
#'
#' @export


#
relWeights <- function(fit,...){
  R <- cor(fit$model)
  nvar <- ncol(R)
  rxx <- R[2:nvar, 2:nvar]
  rxy <- R[2:nvar, 1]
  svd <- eigen(rxx)
  evec <- svd$vectors
  ev <- svd$values
  delta <- diag(sqrt(ev))
  lambda <- evec %*% delta %*% t(evec)
  lambdasq <- lambda ^ 2
  beta <- solve(lambda) %*% rxy
  rsquare <- colSums(beta ^ 2)
  rawwgt <- lambdasq %*% beta ^ 2
  import <- (rawwgt / rsquare) * 100
  lbls <- names(fit$model[2:nvar])
  rownames(import) <- lbls
  colnames(import) <- "Weights"
  barplot(t(import),names.arg=lbls,
          ylab="% of R-Square",
          xlab="Predictor Variables",
          main="Relative Importance of Predictor Variables",
          sub=paste("R-Square=", round(rsquare, digits=3)),
          ...)
  return(import)
}