% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.gg_vimp.R
\name{plot.gg_vimp}
\alias{plot.gg_vimp}
\title{Plot a \code{\link{gg_vimp}} object, extracted variable importance of a
\code{\link[randomForestSRC]{rfsrc}} object}
\usage{
\method{plot}{gg_vimp}(x, relative, lbls, ...)
}
\arguments{
\item{x}{\code{\link{gg_vimp}} object created from a
\code{\link[randomForestSRC]{rfsrc}} object}

\item{relative}{should we plot vimp or relative vimp. Defaults to vimp.}

\item{lbls}{A vector of alternative variable labels. Item names should be
the same as the variable names.}

\item{...}{optional arguments passed to gg_vimp if necessary}
}
\value{
\code{ggplot} object
}
\description{
Draws a horizontal bar chart of the VIMP scores extracted by
\code{\link{gg_vimp}}.  Each bar represents one predictor; bar length is
proportional to its permutation VIMP -- the average rise in OOB prediction
error when that predictor's OOB values are randomly shuffled.  Predictors
are sorted in descending order of importance so the most influential
variables appear at the top.
}
\details{
Bars are coloured by the \code{positive} flag: a bar at or below zero
(non-positive VIMP) is colour-coded differently to flag predictors that
\emph{hurt} OOB accuracy when their signal is removed -- usually a sign of
collinearity or a very noisy variable.  In a well-behaved forest most bars
are positive; the colour distinction matters when a handful are not.
}
\examples{
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
rfsrc_iris <- randomForestSRC::rfsrc(Species ~ ., data = iris)
gg_dta <- gg_vimp(rfsrc_iris)
plot(gg_dta)

## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------
## -------- air quality data
rfsrc_airq <- randomForestSRC::rfsrc(Ozone ~ ., airquality)
gg_dta <- gg_vimp(rfsrc_airq)
plot(gg_dta)


}
\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. randomForestSRC: Random Forests for Survival,
Regression and Classification. R package version >= 3.4.0.
\url{https://cran.r-project.org/package=randomForestSRC}
}
\seealso{
\code{\link{gg_vimp}}
}
