vip: Variable importance plots
In vip: Variable Importance Plots

Plot variable importance scores for the predictors in a model.

vip(object, ...)

## Default S3 method:
vip(
  object,
  num_features = 10L,
  geom = c("col", "point", "boxplot", "violin"),
  mapping = NULL,
  aesthetics = list(),
  horizontal = TRUE,
  all_permutations = FALSE,
  jitter = FALSE,
  include_type = FALSE,
  ...
)

## S3 method for class 'model_fit'
vip(object, ...)

`object`	A fitted model object (e.g., a `"randomForest"` object) or an object that inherits from class `"vi"`.
`...`	Additional optional arguments to be passed on to `vi`.
`num_features`	Integer specifying the number of variable importance scores to plot. Default is `10`.
`geom`	Character string specifying which type of plot to construct. The currently available options are described below. `geom = "col"` uses `geom_col` to construct a bar chart of the variable importance scores. `geom = "point"` uses `geom_point` to construct a Cleveland dot plot of the variable importance scores. `geom = "boxplot"` uses `geom_boxplot` to construct a boxplot plot of the variable importance scores. This option can only for the permutation-based importance method with `nsim > 1` and `keep = TRUE`; see `vi_permute` for details. `geom = "violin"` uses `geom_violin` to construct a violin plot of the variable importance scores. This option can only for the permutation-based importance method with `nsim > 1` and `keep = TRUE`; see `vi_permute` for details.
`mapping`	Set of aesthetic mappings created by `aes` or `aes_`. See example usage below.
`aesthetics`	List specifying additional arguments passed on to `layer`. These are often aesthetics, used to set an aesthetic to a fixed value, like `colour = "red"` or `size = 3`. See example usage below.
`horizontal`	Logical indicating whether or not to plot the importance scores on the x-axis (`TRUE`). Default is `TRUE`.
`all_permutations`	Logical indicating whether or not to plot all permutation scores along with the average. Default is `FALSE`. (Only used for permutation scores when `nsim > 1`.)
`jitter`	Logical indicating whether or not to jitter the raw permutation scores. Default is `FALSE`. (Only used when `all_permutations = TRUE`.)
`include_type`	Logical indicating whether or not to include the type of variable importance computed in the axis label. Default is `FALSE`.

#
# A projection pursuit regression example
#

# Load the sample data
data(mtcars)

# Fit a projection pursuit regression model
model <- ppr(mpg ~ ., data = mtcars, nterms = 1)

# Construct variable importance plot
vip(model, method = "firm")

# Better yet, store the variable importance scores and then plot
vi_scores <- vi(model, method = "firm")
vip(vi_scores, geom = "point", horiz = FALSE)
vip(vi_scores, geom = "point", horiz = FALSE, aesthetics = list(size = 3))

# The `%T>\%` operator is imported for convenience; see ?magrittr::`%T>%`
# for details
vi_scores <- model %>%
  vi(method = "firm") %T>%
  {print(vip(.))}
vi_scores

# Permutation scores (barplot w/ raw values and jittering)
pfun <- function(object, newdata) predict(object, newdata = newdata)
vip(model, method = "permute", train = mtcars, target = "mpg", nsim = 10,
    metric = "rmse", pred_wrapper = pfun,
    aesthetics = list(color = "grey50", fill = "grey50"),
    all_permutations = TRUE, jitter = TRUE)

# Permutation scores (boxplot)
vip(model, method = "permute", train = mtcars, target = "mpg", nsim = 10,
    metric = "rmse", pred_wrapper = pfun, geom = "boxplot")

# Permutation scores (boxplot colored by feature)
library(ggplot2)  # for `aes_string()` function
vip(model, method = "permute", train = mtcars, target = "mpg", nsim = 10,
    metric = "rmse", pred_wrapper = pfun, geom = "boxplot",
    all_permutations = TRUE, mapping = aes_string(fill = "Variable"),
    aesthetics = list(color = "grey35", size = 0.8))

Attaching package: ‘vip’

The following object is masked from ‘package:utils’:

    vi

# A tibble: 10 x 2
   Variable Importance
   <chr>         <dbl>
 1 wt           3.41  
 2 hp           2.54  
 3 gear         1.82  
 4 qsec         1.52  
 5 cyl          0.725 
 6 am           0.663 
 7 vs           0.433 
 8 drat         0.236 
 9 carb         0.0844
10 disp         0.0242