Predicted response plot from a gg_rfsrc object. — plot.gg

Plot the predicted response from a gg_rfsrc object, the rfsrc prediction, using the OOB prediction from the forest. The plot type adapts automatically to the forest family: jitter + boxplot for regression and classification, step curves for survival.

Usage

# S3 method for class 'gg_rfsrc'
plot(x, notch = TRUE, ...)

Arguments

x

A gg_rfsrc object, or a raw rfsrc object (which will be passed through gg_rfsrc automatically before plotting).

notch

Logical; whether to draw notched boxplots for regression and classification forests (default TRUE). Set notch = FALSE to suppress notches when sample sizes are too small for reliable confidence intervals on the median.

...

Additional arguments forwarded to the underlying ggplot2 geometry calls. Commonly useful arguments include:

alpha: Numeric in \([0,1]\); point/ribbon transparency. For survival plots with confidence bands the ribbon alpha is automatically halved relative to the value supplied here.
size: Point or line size passed to geom_jitter, geom_step, etc.

Arguments that control gg_rfsrc (e.g. conf.int, surv_type, by) should be applied when constructing the gg_rfsrc object before calling plot().

Value

A ggplot object. The plot appearance depends on the forest family stored in x:

Regression ("regr"): Jitter + notched boxplot of OOB predicted values. If a group column is present the x-axis shows each group label; otherwise observations are collapsed to a single x-position.
Classification ("class"): Binary: jitter + notched boxplot of the predicted class probability. Multi-class: jitter plot with one panel per class (class probabilities in long form).
Survival ("surv"): Step curves of the ensemble survival function. When gg_rfsrc was called with conf.int, a shaded ribbon is added. When called with by, curves are coloured by group.

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. https://cran.r-project.org/package=randomForestSRC

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
# Build a small classification forest (ntree=50 keeps example fast)
set.seed(42)
rfsrc_iris <- rfsrc(Species ~ ., data = iris, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_iris)

plot(gg_dta)


## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
## -------- air quality data
# na.action = "na.impute" handles missing Ozone / Solar.R values
set.seed(42)
rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality,
                    na.action = "na.impute", ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_airq)

plot(gg_dta)


## -------- mtcars data
set.seed(42)
rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_mtcars)

plot(gg_dta)


## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
## -------- veteran data
## randomized trial of two treatment regimens for lung cancer
data(veteran, package = "randomForestSRC")
set.seed(42)
rfsrc_veteran <- rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_veteran)
plot(gg_dta)


# With 95% pointwise bootstrap confidence bands
gg_dta <- gg_rfsrc(rfsrc_veteran, conf.int = .95)
plot(gg_dta)


# Stratified by treatment arm
gg_dta <- gg_rfsrc(rfsrc_veteran, by = "trt")
plot(gg_dta)


## -------- pbc data (larger dataset -- skipped on CRAN)
# \donttest{
data(pbc, package = "randomForestSRC")
# For whatever reason, the age variable is in days; convert to years
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 from days 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)
# Remove test-set patients (those with no assigned treatment)
dta_train <- pbc[-which(is.na(pbc$treatment)), ]

set.seed(42)
rfsrc_pbc <- randomForestSRC::rfsrc(
  Surv(years, status) ~ .,
  dta_train,
  nsplit = 10,
  na.action = "na.impute",
  forest = TRUE,
  importance = TRUE,
  save.memory = TRUE
)

gg_dta <- gg_rfsrc(rfsrc_pbc)
plot(gg_dta)


gg_dta <- gg_rfsrc(rfsrc_pbc, conf.int = .95)
plot(gg_dta)


gg_dta <- gg_rfsrc(rfsrc_pbc, by = "treatment")
plot(gg_dta)

# }

Predicted response plot from a `gg_rfsrc` object.

Usage

Arguments

Value

References

See also

Examples