Methods for VarImp Objects
VarImp-methods.Rd
Methods for computing on VarImp
objects..
Usage
# S3 method for class 'VarImp'
plot(x, nVar = length(x$values), type = c("bar", "box", "dot", "rank"),
sort = TRUE, interval = c( "no", "quantile", "sd"),
intervalProbs = c(.25, .75), intervalColor = NULL,
horizontal = FALSE, col = NULL, pch = NULL,
main = NULL, margin = NULL, ...)
# S3 method for class 'VarImp'
print(x, ranks = FALSE, ...)
# S3 method for class 'VarImp'
subset(x, subset, ...)
Arguments
- x
an object of the class
VarImp
.- nVar
an integer specifying the number of predictor variables that should be included in the plot. The
nVar
predictor variables with the highest variable importance measure are retained.- type
a character string that indicates the type of plot. Must be one of the following: "bar", "box", "dot" or "rank" (see Details).
- sort
a logical that specifies whether the predictors should be ranked according to the importance measures.
- interval
a character string that indicates if, and which type of intervals should be added to the plot. Must be one of the following:
"no"
,"quantile"
, or"sd"
(see Details).- intervalProbs
a numerical vector of the form c(bottom, top), specifying the two quantiles that should be used for the interval. Only meaningful when
interval = "quantile"
.- intervalColor
a color code or name, see
par
.- horizontal
a logical that specifies whether the plot should be horizontal (= importance values on the x-axis. The default is
FALSE
.- col
a color code or name, see
par
.- pch
Either a single character or an integer code specifying the plotting 'character', see
par
.- main
an overall title for the plot: see
title
.- margin
a numerical vector of the form
c(bottom, left, top, right)
, which gives the number of lines of margin to be specified on the four sides of the plot. Seepar
.- ranks
a logical specifying whether the (reversed) rankings should be printed instead of the importance values.
- subset
a character, integer or logical vector, specifying the subset of predictor variables.
- ...
additional arguments.
Details
plot
gives visualization of the variable importance values. print
prints the importance values, or their (reversed) rankings if ranks = TRUE
. ranks
returns the reversed rankings of the variable importance values. The subset
method for VarImp objects returns a VarImp object for only a subset of the original predictors in the random forest.
In plot
, the type = "bar"
results in a barplot, type = "dot"
in a point-plot, type = "rank"
in a point-plot with the importance rankings as the plotting 'characters', see ranks
. In each of these three options an interval (based on either two quantiles or on the standard deviation of the perTree
values) can be added to the plot. type = "box"
results in boxplots, and is only meaningful when perTree
values are available.
Examples
## Fit a random forest (using cforest)
set.seed(290875)
readingSkills.cf <- party::cforest(score ~ ., data = party::readingSkills,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
## compute permutation variable importance:
set.seed(290875)
permVIM <- permimp(readingSkills.cf)
## print the variable importance values
permVIM
#> nativeSpeaker age shoeSize
#> 12.42492 76.58328 15.52237
print(permVIM, ranks = TRUE)
#> Reversed ranking: 1 denotes the highest value.
#> nativeSpeaker age shoeSize
#> 3 1 2
ranks(permVIM)
#> Reversed ranking: 1 denotes the highest value.
#>
#> nativeSpeaker age shoeSize
#> 3 1 2
## Visualize the variable importance values
plot(permVIM, type = "bar", margin = c(6,3,3,1))
plot(permVIM, nVar = 2, type = "box", horizontal = TRUE)
## note the rankings
plot(subset(permVIM, c("age", "nativeSpeaker")), intervalColor = "pink")
plot(subset(permVIM, c("shoeSize", "nativeSpeaker")), intervalColor = "pink")