if (FALSE) {
## Examples from RFSRC package...
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## ------------- iris data
## You can build a randomForest
rfsrc_iris <- rfsrc(Species ~ ., data = iris, tree.err = 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,
tree.err = 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 <- rfsrc(Ozone ~ ., data = airquality,
na.action = "na.impute", tree.err = 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 <- 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 <- rfsrc(mpg ~ ., data = mtcars, 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 <- 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)
}
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