| 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 |
|
data |
New test data of class |
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 |
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 |
seed |
Random seed. Default is |
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
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.
See Also
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)