plot.AUCRF {AUCRF} | R Documentation

## Plot Method for AUCRF

### Description

The plot method for `AUCRF` objects.

### Usage

      ## S3 method for class 'AUCRF'
    plot(x, which=c("auc","ranking","psel"), showOpt=TRUE, digits=4,
           maxvars=NULL, ...)

### Arguments

`x` | an object of class `AUCRF` as the result of `AUCRF` or `AUCRFcv` functions.

`which`

specifies the information to plot. There are three options: (1) `"auc"` (by default) to plot the curve of AUCs in the backwards elimination process, (2) `"ranking"` to plot the importance measure in initial ranking of each variable, and (3) `"psel"` to plot the probability of selection of each variable. The `"psel"` option is only available if a cross validation is performed by `AUCRFcv` function.
For option (1), `showOpt` and `digits` arguments can be specified for more details (see below).
For options (2) and (3), the number of variables to plot and their order preference can be specified by `maxvars` and `order` arguments, respectively (see below).

`showOpt` | (only applied if `"auc"` option is specified in `wich` argument). If `showOpt=TRUE`, the optimal subset is emphasised in the plot.
`digits` | (only applied if `"auc"` option is specified in `wich` argument and `showOpt` or `showThres` are `TRUE`). Specifies the number of decimal digits for representing the optimal AUC in the plot.
`maxvars` | (only applied if `"ranking"` or `"psel"` options are specified in `wich` argument). Number of variables to include in the plot. The specified number of variables with highest importance measure (initial ranking) will be plotted. If `maxvars=NULL` (by default) the selected variables will be plotted. (For large number of variables, their names can be illegible in the plot)
`...` | other graphical parameters (see `par`).

### Examples

      data(fitCV)

      # Plotting the AUC in the AUCRF backward elimination process:
      plot(fitCV)

      # Plotting the probability of selection of the selected variables:
      plot(fitCV, wich="psel")

      # Plotting the 20 variables with highest probability of selection:
      plot(fitCV, wich="psel", maxvars=20)

[Package AUCRF version 1.1 Index]