# NOT RUN {
## ------------------------------------------------------------
## classification example
## showcase different vimp
## ------------------------------------------------------------
iris.obj <- rfsrc(Species ~ ., data = iris)
# Permutation vimp (default)
print(vimp(iris.obj)$importance)
# VIMP using brier prediction error
print(vimp(iris.obj, perf.type = "brier")$importance)
# Random daughter vimp
print(vimp(iris.obj, importance = "random")$importance)
# Joint permutation vimp
print(vimp(iris.obj, joint = TRUE)$importance)
# Paired vimp
print(vimp(iris.obj, c("Petal.Length", "Petal.Width"), joint = TRUE)$importance)
print(vimp(iris.obj, c("Sepal.Length", "Petal.Width"), joint = TRUE)$importance)
## ------------------------------------------------------------
## imbalanced classification example
## see the imbalanced function for more details
## ------------------------------------------------------------
data(breast, package = "randomForestSRC")
breast <- na.omit(breast)
f <- as.formula(status ~ .)
o <- rfsrc(f, breast, ntree = 2000)
## Breiman importance
print(100 * vimp(o)$importance)
## G-mean importance
print(100 * vimp(o, perf.type = "g.mean")$importance[, 1])
## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------
airq.obj <- rfsrc(Ozone ~ ., airquality)
print(vimp(airq.obj))
## ------------------------------------------------------------
## regression example where vimp is calculated on test data
## ------------------------------------------------------------
set.seed(100080)
train <- sample(1:nrow(airquality), size = 80)
airq.obj <- rfsrc(Ozone~., airquality[train, ])
#training data vimp
print(airq.obj$importance)
print(vimp(airq.obj)$importance)
#test data vimp
print(vimp(airq.obj, newdata = airquality[-train, ])$importance)
## ------------------------------------------------------------
## case-specific vimp
## returns VIMP for each case
## ------------------------------------------------------------
o <- rfsrc(mpg~., mtcars)
v <- vimp(o, csv = TRUE)
csvimp <- get.mv.csvimp(v, standardize=TRUE)
print(csvimp)
## ------------------------------------------------------------
## case-specific joint vimp
## returns joint VIMP for each case
## ------------------------------------------------------------
o <- rfsrc(mpg~., mtcars)
v <- vimp(o, joint = TRUE, csv = TRUE)
csvimp <- get.mv.csvimp(v, standardize=TRUE)
print(csvimp)
## ------------------------------------------------------------
## case-specific joint vimp for multivariate regression
## returns joint VIMP for each case, for each outcome
## ------------------------------------------------------------
o <- rfsrc(Multivar(mpg, cyl) ~., data = mtcars)
v <- vimp(o, joint = TRUE, csv = TRUE)
csvimp <- get.mv.csvimp(v, standardize=TRUE)
print(csvimp)
# }
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