err.regAbcrf: Calculate and plot for different numbers of tree, the out-of-bag mean squared errors associated with a REG-ABC-RF object

Description

err.regAbcrf returns out-of-bag mean squared errors and plot them.

Usage

err.regAbcrf(object, training, paral=FALSE, 
ncores= if(paral) max(detectCores()-1,1) else 1, what="mean")

Value

A matrix with 2 columns: the number of trees and the out-of-bag mean squared errors. NAs might be returned if the number of trees is too low. Errors are computed from 40 trees to the total number.

Arguments

object: a regAbcrf object.
training: the data frame containing the reference table used to train the regAbcrf object.
paral: a boolean that indicates if random forests predictions should be parallelized.
ncores: the number of CPU cores to use for the 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.
what: a string caracter "mean" or "median" indicating if the predictions are computed with mean or median of the response variable.

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 tools:::Rd_expr_doi("10.1093/bioinformatics/bty867")

Examples

Run this code

  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)
  err.regAbcrf(model.rf.r, data2)