regAbcrf: Create a reg-ABC-RF object: a regression random forest from a...

In abcrf: Approximate Bayesian Computation via Random Forests

Create a reg-ABC-RF object: a regression random forest from a reference table aimed out predicting posterior expectation, variance and quantiles for a parameter

Description

regAbcrf constructs a regression random forest from a reference table towards predicting posterior expectations, variances and quantiles of a parameter.

Usage

## S3 method for class 'formula'
regAbcrf(formula, data, ntree=500,
mtry=max(floor((dim(data)[2]-1)/3), 1), sampsize=min(1e5, nrow(data)),
paral=FALSE, ncores=if(paral) max(detectCores()-1,1) else 1, ...)

Arguments

formula	a formula: left of ~, variable representing the response variable; right of ~, summary statistics of the reference table.
data	a data frame containing the reference table, composed of response variable (parameter) and summary statistics.
ntree	number of trees to grow in the forest, by default 500 trees.
mtry	Number of variables to possibly split at in each node. Default is the minimum between 1 and the number of variables divided by 3.
sampsize	size of the sample from the reference table used to grow a tree of the regression forest, by default the minimum between the number of elements of the reference table and 100,000.
paral	a boolean that indicates if the calculations of the regression random forest should be parallelized.
ncores	the number of CPU cores to use. 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.
...	additional arguments to be passed on to ranger used to construct the regression random forest that predicts the response variable.

Value

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

call	the original call to regAbcrf,
formula	the formula used to construct the regression random forest,
model.rf	an object of class ranger containing the trained forest with the reference table.

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 doi: 10.1093/bioinformatics/bty867

See Also

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

Examples

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)
model.rf.r

abcrf documentation built on Aug. 9, 2022, 5:07 p.m.