Prediction using Random Forest variants for block-structured covariate data

This function is to be applied to the entry 'forest' of the output of blockfor. See the example section for illustration.

Usage

# S3 method for class 'blockForest'
predict(
  object,
  data = NULL,
  predict.all = FALSE,
  num.trees = object$num.trees,
  type = "response",
  se.method = "infjack",
  quantiles = c(0.1, 0.5, 0.9),
  seed = NULL,
  num.threads = NULL,
  verbose = TRUE,
  ...
)

Arguments

object: blockForest object.
data: New test data of class data.frame or gwaa.data (GenABEL).
predict.all: Return individual predictions for each tree instead of aggregated predictions for all trees. Return a matrix (sample x tree) for classification and regression, a 3d array for probability estimation (sample x class x tree) and survival (sample x time x tree).
num.trees: Number of trees used for prediction. The first num.trees in the forest are used.
type: Type of prediction. One of 'response', 'se', 'terminalNodes', 'quantiles' with default 'response'. See below for details.
se.method: Method to compute standard errors. One of 'jack', 'infjack' with default 'infjack'. Only applicable if type = 'se'. See below for details.
quantiles: Vector of quantiles for quantile prediction. Set type = 'quantiles' to use.
seed: Random seed. Default is NULL, which generates the seed from R. Set to 0 to ignore the R seed. The seed is used in case of ties in classification mode.
num.threads: Number of threads. Default is number of CPUs available.
verbose: Verbose output on or off.
...: further arguments passed to or from other methods.

Value

Object of class blockForest.prediction with elements

`predictions`	Predicted classes/values (only for classification and regression)
`unique.death.times`	Unique death times (only for survival).
`chf`	Estimated cumulative hazard function for each sample (only for survival).
`survival`	Estimated survival function for each sample (only for survival).
`num.trees`	Number of trees.
`num.independent.variables`	Number of independent variables.
`treetype`	Type of forest/tree. Classification, regression or survival.
`num.samples`	Number of samples.

Details

For type = 'response' (the default), the predicted classes (classification), predicted numeric values (regression), predicted probabilities (probability estimation) or survival probabilities (survival) are returned. For type = 'se', the standard error of the predictions are returned (regression only). The jackknife-after-bootstrap or infinitesimal jackknife for bagging is used to estimate the standard errors based on out-of-bag predictions. See Wager et al. (2014) for details. For type = 'terminalNodes', the IDs of the terminal node in each tree for each observation in the given dataset are returned. For type = 'quantiles', the selected quantiles for each observation are estimated. See Meinshausen (2006) for details.

If type = 'se' is selected, the method to estimate the variances can be chosen with se.method. Set se.method = 'jack' for jackknife-after-bootstrap and se.method = 'infjack' for the infinitesimal jackknife for bagging.

For classification and predict.all = TRUE, a factor levels are returned as numerics. To retrieve the corresponding factor levels, use rf$forest$levels, if rf is the ranger object.

References

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. doi:10.18637/jss.v077.i01 .
Wager, S., Hastie T., & Efron, B. (2014). Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife. J Mach Learn Res 15:1625-1651. https://jmlr.org/papers/v15/wager14a.html.
Meinshausen (2006). Quantile Regression Forests. J Mach Learn Res 7:983-999. https://www.jmlr.org/papers/v7/meinshausen06a.html.

Author

Marvin N. Wright

Examples

# NOTE: There is no association between covariates and response for the
# simulated data below.
# Moreover, the input parameters of blockfor() are highly unrealistic
# (e.g., nsets = 10 is specified much too small).
# The purpose of the shown examples is merely to illustrate the
# application of predict.blockForest().


# Generate data:
################

set.seed(1234)

# Covariate matrix:
X <- cbind(matrix(nrow=40, ncol=5, data=rnorm(40*5)), 
           matrix(nrow=40, ncol=30, data=rnorm(40*30, mean=1, sd=2)),
           matrix(nrow=40, ncol=100, data=rnorm(40*100, mean=2, sd=3)))
colnames(X) <- paste("X", 1:ncol(X), sep="")

# Block variable (list):
block <- rep(1:3, times=c(5, 30, 100))
block <- lapply(1:3, function(x) which(block==x))

# Binary outcome:
ybin <- factor(sample(c(0,1), size=40, replace=TRUE), levels=c(0,1))

# Survival outcome:
ysurv <- cbind(rnorm(40), sample(c(0,1), size=40, replace=TRUE))



# Divide in training and test data:

Xtrain <- X[1:30,]
Xtest <- X[31:40,]

ybintrain <- ybin[1:30]
ybintest <- ybin[31:40]

ysurvtrain <- ysurv[1:30,]
ysurvtest <- ysurv[31:40,]




# Binary outcome: Apply algorithm to training data and obtain predictions
# for the test data:
#########################################################################

# Apply a variant to the training data:

blockforobj <- blockfor(Xtrain, ybintrain, num.trees = 100, replace = TRUE, block=block,
                        nsets = 10, num.trees.pre = 50, splitrule="extratrees", 
                        block.method = "SplitWeights")
blockforobj$paramvalues
#> [1] 1.00000000 0.06193513 0.57704991


# Obtain prediction for the test data:

(predres <- predict(blockforobj$forest, data = Xtest, block.method = "SplitWeights"))
#> blockForest prediction
#> 
#> Type:                             Classification 
#> Sample size:                      10 
#> Number of independent variables:  135 
predres$predictions
#>  [1] 0 0 0 0 0 0 0 0 0 0
#> Levels: 0 1



# Survival outcome: Apply algorithm to training data and obtain predictions
# for the test data:
###########################################################################

# Apply a variant to the training data:

blockforobj <- blockfor(Xtrain, ysurvtrain, num.trees = 100, replace = TRUE, block=block,
                        nsets = 10, num.trees.pre = 50, splitrule="extratrees", 
                        block.method = "SplitWeights")
blockforobj$paramvalues
#> [1] 1.00000000 0.07620874 0.86167920


# Obtain prediction for the test data:

(predres <- predict(blockforobj$forest, data = Xtest, block.method = "SplitWeights"))
#> blockForest prediction
#> 
#> Type:                             Survival 
#> Sample size:                      10 
#> Number of independent variables:  135 
#> Number of unique death times:     30 
rowSums(predres$chf)
#>  [1] 12.156000 14.736000 13.539500 13.278667  9.685667 12.356167 12.604167
#>  [8] 14.348333 12.657833 14.365833