## ------------------------------------------------------------
## typical train/testing scenario
## ------------------------------------------------------------
data(veteran, package = "randomForestSRC")
train <- sample(1:nrow(veteran), round(nrow(veteran) * 0.80))
veteran.grow <- rfsrc(Surv(time, status) ~ ., veteran[train, ], ntree = 100)
veteran.pred <- predict(veteran.grow, veteran[-train , ])
print(veteran.grow)
print(veteran.pred)
## ------------------------------------------------------------
## predicted probability and predicted class labels are returned
## in the predict object for classification analyses
## ------------------------------------------------------------
data(breast, package = "randomForestSRC")
breast.obj <- rfsrc(status ~ ., data = breast[(1:100), ], nsplit = 10)
breast.pred <- predict(breast.obj, breast[-(1:100), ])
print(head(breast.pred$predicted))
print(breast.pred$class)
## ------------------------------------------------------------
## example illustrating restore mode
## if predict is called without specifying the test data
## the original training data is used and the forest is restored
## ------------------------------------------------------------
# first we make the grow call
airq.obj <- rfsrc(Ozone ~ ., data = airquality)
# now we restore it and compare it to the original call
# they are identical
predict(airq.obj)
print(airq.obj)
# we can retrieve various outputs that were not asked for in
# in the original call
#here we extract the proximity matrix
prox <- predict(airq.obj, proximity = TRUE)$proximity
print(prox[1:10,1:10])
#here we extract the number of times a variable was used to grow
#the grow forest
var.used <- predict(airq.obj, var.used = "by.tree")$var.used
print(head(var.used))
## ------------------------------------------------------------
## unique feature of randomForestSRC
## cross-validation can be used when factor labels differ over
## training and test data
## ------------------------------------------------------------
# first we convert all x-variables to factors
data(veteran, package = "randomForestSRC")
veteran.factor <- data.frame(lapply(veteran, factor))
veteran.factor$time <- veteran$time
veteran.factor$status <- veteran$status
# split the data into unbalanced train/test data (5/95)
# the train/test data have the same levels, but different labels
train <- sample(1:nrow(veteran), round(nrow(veteran) * .05))
summary(veteran.factor[train,])
summary(veteran.factor[-train,])
# grow the forest on the training data and predict on the test data
veteran.f.grow <- rfsrc(Surv(time, status) ~ ., veteran.factor[train, ])
veteran.f.pred <- predict(veteran.f.grow, veteran.factor[-train , ])
print(veteran.f.grow)
print(veteran.f.pred)
## ------------------------------------------------------------
## example illustrating the flexibility of outcome = "test"
## illustrates restoration of forest via outcome = "test"
## ------------------------------------------------------------
# first we make the grow call
data(pbc, package = "randomForestSRC")
pbc.grow <- rfsrc(Surv(days, status) ~ ., pbc, nsplit = 10)
# now use predict with outcome = TEST
pbc.pred <- predict(pbc.grow, pbc, outcome = "test")
# notice that error rates are the same!!
print(pbc.grow)
print(pbc.pred)
# note this is equivalent to restoring the forest
pbc.pred2 <- predict(pbc.grow)
print(pbc.grow)
print(pbc.pred)
print(pbc.pred2)
# similar example, but with na.action = "na.impute"
airq.obj <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
print(airq.obj)
print(predict(airq.obj))
# ... also equivalent to outcome="test" but na.action = "na.impute" required
print(predict(airq.obj, airquality, outcome = "test", na.action = "na.impute"))
# classification example
iris.obj <- rfsrc(Species ~., data = iris)
print(iris.obj)
print(predict.rfsrc(iris.obj, iris, outcome = "test"))
## ------------------------------------------------------------
## another example illustrating outcome = "test"
## unique way to check reproducibility of the forest
## ------------------------------------------------------------
# primary call
set.seed(542899)
data(pbc, package = "randomForestSRC")
train <- sample(1:nrow(pbc), round(nrow(pbc) * 0.50))
pbc.out <- rfsrc(Surv(days, status) ~ ., data=pbc[train, ],
nsplit = 10)
# standard predict call
pbc.train <- predict(pbc.out, pbc[-train, ], outcome = "train")
#non-standard predict call: overlays the test data on the grow forest
pbc.test <- predict(pbc.out, pbc[-train, ], outcome = "test")
# check forest reproducibilility by comparing "test" predicted survival
# curves to "train" predicted survival curves for the first 3 individuals
Time <- pbc.out$time.interest
matplot(Time, t(exp(-pbc.train$chf)[1:3,]), ylab = "Survival", col = 1, type = "l")
matlines(Time, t(exp(-pbc.test$chf)[1:3,]), col = 2)Run the code above in your browser using DataLab