predict.blockForest {blockForest}R Documentation

Prediction using Random Forest variants for block-structured covariate data

Description

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.

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.

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.

Author(s)

Marvin N. Wright

References

See Also

blockForest

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


# Obtain prediction for the test data:

(predres <- predict(blockforobj$forest, data = Xtest, block.method = "SplitWeights"))
predres$predictions



# 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


# Obtain prediction for the test data:

(predres <- predict(blockforobj$forest, data = Xtest, block.method = "SplitWeights"))
rowSums(predres$chf)


[Package blockForest version 0.2.4 Index]