Plot a gg_vimp object, extracted variable importance of a rfsrc object

Plot a gg_vimp object, extracted variable importance of a rfsrc object

Usage

# S3 method for class 'gg_vimp'
plot(x, relative, lbls, ...)

Arguments

x

gg_vimp object created from a rfsrc object

relative

should we plot vimp or relative vimp. Defaults to vimp.

lbls

A vector of alternative variable labels. Item names should be the same as the variable names.

...

optional arguments passed to gg_vimp if necessary

Value

ggplot object

References

Breiman L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Ishwaran H. and Kogalur U.B. (2013). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.4.

See also

gg_vimp

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
rfsrc_iris <- rfsrc(Species ~ ., data = iris)
gg_dta <- gg_vimp(rfsrc_iris)
#> Warning: rfsrc object does not contain VIMP information. Calculating...
plot(gg_dta)
#> Warning: All aesthetics have length 1, but the data has 16 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.


## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------
## -------- air quality data
rfsrc_airq <- rfsrc(Ozone ~ ., airquality)
gg_dta <- gg_vimp(rfsrc_airq)
#> Warning: rfsrc object does not contain VIMP information. Calculating...
plot(gg_dta)
#> Warning: All aesthetics have length 1, but the data has 5 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.