forestRK (version 0.0-5)

importance.plot.forestRK: Generates importance ggplot of the covariates considered in the forestRK model

Description

Generates importance ggplot of the covariates considered in the forestRK model.

When the number of covariates under consideration is huge, it can be pretty difficult to read the covariate name from this plot. In this case, user can identify the name of the covariate that he or she is interested in by extracting importance.covariate.names from the importance.forestRK.object that was used in the function call. importance.covariate.names lists the original names of the covariates after ordering them from the most important to the least important. So for example, the exact name of the covariate that has the second highest importance would be the second element of the vector importance.covariate.names, and so on.

Usage

importance.plot.forestRK(importance.forestRK.object = importance.forestRK(),
                          colour.used = "dark green", fill.colour = "dark green",
                          label.size = 10)

Arguments

importance.forestRK.object

an importance.forestRK object.

colour.used

colour used for the border of the importance plot; default is "dark green".

fill.colour

colour used to fill the bars of the importance plot; default is "dark green" (yes, I like dark green).

label.size

size of the labels; default is set to 10.

Value

An importance plot of the covariates considered in the forestRK model, ordered from the most important covariate to the least important covariate.

See Also

forestRK

Examples

# NOT RUN {
  ## example: iris dataset
  ## load the forestRK package
  library(forestRK)

  ## numericize the data
  x.train <- x.organizer(iris[,1:4], encoding = "num")[c(1:25,51:75,101:125),]
  y.train <- y.organizer(iris[c(1:25,51:75,101:125),5])$y.new

  # random forest
  # min.num.obs.end.node.tree is set to 5 by default;
  # entropy is set to TRUE by default
  # typically the nbags and samp.size has to be much larger than 30 and 50
  forestRK.1 <- forestRK(x.train, y.train, nbags = 30, samp.size = 50)
  # execute forestRK.importance function
  imp <- importance.forestRK(forestRK.1)

  # generate importance plot
  importance.plot.forestRK(imp)
# }