yaiVarImp: Reports or plots importance scores for yai method...
In yaImpute: Nearest Neighbor Observation Imputation and Evaluation Tools

Description Usage Arguments Value Author(s) See Also Examples

When method randomforest is used to build a yai object, the randomForest package computes variable importance scores. This function computes a composite of the scores and scales them using scale. By default the scores are plotted and scores themselves are invisibly returned. For classification, the scores are derived from "MeanDecreaseAccuracy" and for regression they are based in " using importance.

1	yaiVarImp(object, nTop=20, plot=TRUE, ...)

`object`	an object of class `yai`
`nTop`	the `nTop` most important variables are plotted (returned); if NA or zero, all are returned
`plot`	if FALSE, no plotting is done, but the scores are returned.
`...`	passed to the `boxplot` function.

A data frame with the rows corresponding to the randomForest built for each Y-variable and the columns corresponding to the nTop most important Y-variables in sorted order.

Nicholas L. Crookston ncrookston.fs@gmail.com

yai, yaiRFsummary, compare.yai

if (require(randomForest))
{
  data(MoscowMtStJoe)

  # get the basal area by species columns
  yba  <- MoscowMtStJoe[,1:17]
  ybaB <- whatsMax(yba,nbig=7)  # see help on whatsMax
  
  ba <- cbind(ybaB,TotalBA=MoscowMtStJoe[,18])
  x <- MoscowMtStJoe[,37:64]
  x <- x[,-(4:5)]
  rf <- yai(x=x,y=ba,method="randomForest")
  
  yaiVarImp(rf)
  
  keep=colnames(yaiVarImp(rf,plot=FALSE,nTop=9))
  
  newx <- x[,keep]
  rf2 <- yai(x=newx,y=ba,method="randomForest")
  
  yaiVarImp(rf2,col="gray")
  
  compare.yai(rf,rf2)
}