predictOOB: Predict out-of-bag posterior expectation, median, variance,...

Description Usage Arguments Value References See Also Examples

View source: R/predictOOB.R

Description

Based on a reg-ABC-RF object this function predicts the out-of-bag posterior expectation, median, variance, quantiles, mean squared errors, normalized mean absolute errors, credible interval and coverage, for the corresponding parameter using the out-of-bag observations of the training data set.

Mean squared errors and normalized mean absolute errors are computed both with mean and median of the response variable.

Memory allocation issues might be encountered when the size of the training data set is large.

Usage

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## S3 method for class 'regAbcrf'
predictOOB(object, training, quantiles=c(0.025,0.975), paral = FALSE,
ncores = if(paral) max(detectCores()-1,1) else 1,...)

Arguments

object

a regAbcrf object.

training

the data frame containing the reference table used to train the regAbcrf object.

quantiles

numeric vector of probabilities with values in [0,1]. The default value is equal to c(0.025, 0.975).

paral

a boolean that indicates if training data predictions should be parallelized or not.

ncores

the number of CPU cores to use for the regression random forest predictions. If paral=TRUE, it is used the number of CPU cores minus 1. If ncores is not specified and detectCores does not detect the number of CPU cores with success then 1 core is used.

...

optional arguments to be passed on to the function predict.ranger.

Value

An object of class regAbcrfOOBpredict, which is a list with the following components:

expectation

predicted posterior expectation for each oberved data set,

med

predicted posterior median for each oberved data set,

variance

predicted posterior variance for each observed data set, computed by reusing weights,

variance.cdf

predicted posterior variance for each observed data set, computed by approximation of the cumulative distribution function,

quantiles

predicted posterior quantiles for each observed data set,

MSE

mean squared error computed with prediction based on mean of response variable,

NMAE

normalized mean absolute error computed with predictions based on mean of response variable,

MSE.med

mean squared error computed with predictions based on median of response variable,

NMAE.med

normalized mean absolute error with predictions based on median of response variable,

coverage

credible inteval coverage if only two quantiles are of interest, NULL otherwise.

References

Raynal L., Marin J.-M. Pudlo P., Ribatet M., Robert C. P. and Estoup, A. (2019) ABC random forests for Bayesian parameter inference Bioinformatics https://doi.org/10.1093/bioinformatics/bty867

See Also

regAbcrf, predict.regAbcrf, plot.regAbcrf, err.regAbcrf, covRegAbcrf, ranger, densityPlot

Examples

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data(snp)
modindex <- snp$modindex
sumsta <- snp$sumsta[modindex == "3",]
r <- snp$param$r[modindex == "3"]
r <- r[1:500]
sumsta <- sumsta[1:500,]
data2 <- data.frame(r, sumsta)
model.rf.r <- regAbcrf(r~., data2, ntree=100)
res <- predictOOB(model.rf.r, data2) 

abcrf documentation built on Nov. 5, 2019, 5:06 p.m.