% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.gg_error.R
\name{plot.gg_error}
\alias{plot.gg_error}
\title{Plot a \code{\link{gg_error}} object}
\usage{
\method{plot}{gg_error}(x, ...)
}
\arguments{
\item{x}{A \code{\link{gg_error}} object created from either a
\code{\link[randomForestSRC]{rfsrc}} or a
\code{\link[randomForest]{randomForest}} object.  A raw forest object
may also be supplied and will be passed through \code{\link{gg_error}}
automatically before plotting.}

\item{...}{Extra arguments forwarded to the underlying \code{ggplot2}
geometry calls (e.g. \code{size}, \code{linetype}).}
}
\value{
A \code{ggplot} object with \code{ntree} on the x-axis and
OOB error rate on the y-axis.  Single-outcome forests (regression,
survival) produce a single line; multi-outcome forests (classification)
produce one coloured line per class.
}
\description{
A plot of the cumulative OOB error rates of the random forest as a
function of number of trees.
}
\details{
The gg_error plot is used to track the convergence of the
randomForest. This figure is a reproduction of the error plot
from the \code{\link[randomForestSRC]{plot.rfsrc}} function.
}
\examples{
## Examples from RFSRC package...
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## ------------- iris data
## You can build a randomForest
rfsrc_iris <- randomForestSRC::rfsrc(Species ~ ., data = iris,
  forest = TRUE,
  importance = TRUE,
  tree.err = TRUE,
  save.memory = TRUE)

# Get a data.frame containing error rates
gg_dta <- gg_error(rfsrc_iris)

# Plot the gg_error object
plot(gg_dta)

## RandomForest example
rf_iris <- randomForest::randomForest(Species ~ .,
  data = iris,
  forest = TRUE,
  importance = TRUE,
  tree.err = TRUE,
  save.memory = TRUE
)
gg_dta <- gg_error(rf_iris)
plot(gg_dta)

gg_dta <- gg_error(rf_iris, training = TRUE)
plot(gg_dta)
## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
## ------------- airq data
rfsrc_airq <- randomForestSRC::rfsrc(Ozone ~ .,
  data = airquality,
  na.action = "na.impute",
  forest = TRUE,
  importance = TRUE,
  tree.err = TRUE,
  save.memory = TRUE
)

# Get a data.frame containing error rates
gg_dta <- gg_error(rfsrc_airq)

# Plot the gg_error object
plot(gg_dta)


## ------------- Boston data
data(Boston, package = "MASS")
Boston$chas <- as.logical(Boston$chas)
rfsrc_boston <- randomForestSRC::rfsrc(medv ~ .,
  data = Boston,
  forest = TRUE,
  importance = TRUE,
  tree.err = TRUE,
  save.memory = TRUE
)

# Get a data.frame containing error rates
gg_dta <- gg_error(rfsrc_boston)

# Plot the gg_error object
plot(gg_dta)

## ------------- mtcars data
rfsrc_mtcars <- randomForestSRC::rfsrc(mpg ~ ., data = mtcars,
  importance = TRUE,
  save.memory = TRUE,
  forest = TRUE,
  tree.err = TRUE)

# Get a data.frame containing error rates
gg_dta<- gg_error(rfsrc_mtcars)

# Plot the gg_error object
plot(gg_dta)


## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
## ------------- veteran data
## randomized trial of two treatment regimens for lung cancer
data(veteran, package = "randomForestSRC")
rfsrc_veteran <- randomForestSRC::rfsrc(Surv(time, status) ~ ., data = veteran,
                       tree.err = TRUE)

gg_dta <- gg_error(rfsrc_veteran)
plot(gg_dta)

## ------------- pbc data
# Load a cached randomForestSRC object
# We need to create this dataset
data(pbc, package = "randomForestSRC",)
# For whatever reason, the age variable is in days... makes no sense to me
for (ind in seq_len(dim(pbc)[2])) {
 if (!is.factor(pbc[, ind])) {
   if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
     if (sum(range(pbc[, ind], na.rm = TRUE) == c(0, 1)) == 2) {
       pbc[, ind] <- as.logical(pbc[, ind])
     }
   }
 } else {
   if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
     if (sum(sort(unique(pbc[, ind])) == c(0, 1)) == 2) {
       pbc[, ind] <- as.logical(pbc[, ind])
     }
     if (sum(sort(unique(pbc[, ind])) == c(FALSE, TRUE)) == 2) {
       pbc[, ind] <- as.logical(pbc[, ind])
     }
   }
 }
 if (!is.logical(pbc[, ind]) &
     length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 5) {
   pbc[, ind] <- factor(pbc[, ind])
 }
}
#Convert age to years
pbc$age <- pbc$age / 364.24

pbc$years <- pbc$days / 364.24
pbc <- pbc[, -which(colnames(pbc) == "days")]
pbc$treatment <- as.numeric(pbc$treatment)
pbc$treatment[which(pbc$treatment == 1)] <- "DPCA"
pbc$treatment[which(pbc$treatment == 2)] <- "placebo"
pbc$treatment <- factor(pbc$treatment)
dta_train <- pbc[-which(is.na(pbc$treatment)), ]
# Create a test set from the remaining patients
pbc_test <- pbc[which(is.na(pbc$treatment)), ]

#========
# build the forest:
rfsrc_pbc <- randomForestSRC::rfsrc(
  Surv(years, status) ~ .,
 dta_train,
 nsplit = 10,
 na.action = "na.impute",
 tree.err = TRUE,
 forest = TRUE,
 importance = TRUE,
 save.memory = TRUE
)


gg_dta <- gg_error(rfsrc_pbc)
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_error}} \code{\link[randomForestSRC]{rfsrc}}
\code{\link[randomForestSRC]{plot.rfsrc}}
}
