## S3 method for class 'rfsrc'
predict(object,
  newdata,
  m.target = NULL,
  importance = c(FALSE, TRUE, "none", "permute", "random", "anti"),
  get.tree = NULL,
  block.size = if (any(is.element(as.character(importance),
                     c("none", "FALSE")))) NULL else 10,
  ensemble = NULL,
  na.action = c("na.omit", "na.impute"),
  outcome = c("train", "test"),
  proximity = FALSE,
  forest.wt = FALSE,
  ptn.count = 0,
  distance = FALSE,
  var.used = c(FALSE, "all.trees", "by.tree"),
  split.depth = c(FALSE, "all.trees", "by.tree"), seed = NULL,
  do.trace = FALSE, membership = FALSE, statistics = FALSE,
   
  ...)

## ------------------------------------------------------------
## 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)


## ------------------------------------------------------------
## 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))

## ------------------------------------------------------------
## vimp for each tree
## illustrates get.tree and how to extract information
## from trees, even if that information was not requested
## in the original call
## ------------------------------------------------------------

## regression analysis but no VIMP
o <- rfsrc(mpg~., mtcars)

## now extract VIMP for each tree using get.tree
vimp.tree <- do.call(rbind, lapply(1:o$ntree, function(b) {
     predict(o, get.tree = b, importance = TRUE)$importance
}))

## boxplot of tree VIMP
boxplot(vimp.tree, outline = FALSE, col = "cyan")
abline(h = 0, lty = 2, col = "red")

## summary information of tree VIMP
print(summary(vimp.tree))

## extract tree-averaged VIMP using importance=TRUE
## remember to set block.size to 1
print(predict(o, importance = TRUE, block.size = 1)$importance)

## use direct call to vimp() for tree-averaged VIMP
print(vimp(o, block.size = 1)$importance)

## ------------------------------------------------------------
## case-specific vimp
## returns VIMP for each case
## ------------------------------------------------------------

o <- rfsrc(mpg~., mtcars)
op <- predict(o, importance = TRUE, csv = TRUE)
csvimp <- get.mv.csvimp(op, standardize=TRUE)
print(csvimp)

## ------------------------------------------------------------
## case-specific error rate
## returns tree-averaged error rate for each case
## ------------------------------------------------------------

o <- rfsrc(mpg~., mtcars)
op <- predict(o, importance = TRUE, cse = TRUE)
cserror <- get.mv.cserror(op, standardize=TRUE)
print(cserror)


## ------------------------------------------------------------
## 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), ])
breast.pred <- predict(breast.obj, breast[-(1:100), ])
print(head(breast.pred$predicted))
print(breast.pred$class)


## ------------------------------------------------------------
## 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)

## 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, ])

## 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$time.interest
matplot(Time, t(pbc.train$survival[1:3,]), ylab = "Survival", col = 1, type = "l")
matlines(Time, t(pbc.test$survival[1:3,]), col = 2)

## ------------------------------------------------------------
## ... just for _fun_ ...
## survival analysis using mixed multivariate outcome analysis 
## compare the predicted value to RSF
## ------------------------------------------------------------

## fit the pbc data using RSF
data(pbc, package = "randomForestSRC")
rsf.obj <- rfsrc(Surv(days, status) ~ ., pbc)
yvar <- rsf.obj$yvar

## fit a mixed outcome forest using days and status as y-variables
pbc.mod <- pbc
pbc.mod$status <- factor(pbc.mod$status)
mix.obj <- rfsrc(Multivar(days, status) ~., pbc.mod)

## compare oob predicted values
rsf.pred <- rsf.obj$predicted.oob
mix.pred <- mix.obj$regrOutput$days$predicted.oob
plot(rsf.pred, mix.pred)

## compare C-index error rate
rsf.err <- get.cindex(yvar$days, yvar$status, rsf.pred)
mix.err <- 1 - get.cindex(yvar$days, yvar$status, mix.pred)
cat("RSF                :", rsf.err, "\n")
cat("multivariate forest:", mix.err, "\n")