predict.abcrf: Predict and evaluate the posterior probability of the MAP for new data using an ABC-RF object

Description

Based on an ABC-RF object this function predicts the best model for new data and evaluate the posterior probability of the MAP.

Usage

# S3 method for abcrf
predict(object, obs, training, ntree = 1000, 
sampsize = min(1e5,  object$model.rf$num.samples ), paral = FALSE,
ncores = if(paral) max(detectCores()-1,1) else 1, paral.predict = FALSE, 
ncores.predict = if(paral.predict) max(detectCores()-1,1) else 1, ...)

Value

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

allocation: indices of the selected models for each observed data set,
vote: votes for each observed dataset,
post.prob: ABC-RF approximations of the posterior probability of the selected model for each observed dataset.

Arguments

object: an abcrf object.
obs: a data frame containing the summary statistics of the observed data sets.
training: the data frame containing the reference table used to train the abcrf object.
ntree: number of trees to grow in the regression forest, by default 1,000 trees.
sampsize: size of the sample from the reference table used to grow a tree of the 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 (forest that returns the posterior probability of the selected model) should be parallelized.
ncores: the number of CPU cores to use for the regression random forest construction. 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.
paral.predict: a boolean that indicates if random forests predictions should be parallelized.
ncores.predict: the number of CPU cores to use for random forest predictions (classification and regression). If paral.predict=TRUE, it is used the number of CPU cores minus 1. If ncores.predict 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 estimates the posterior probability of the selected model.

References

Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert, C. P. (2016) Reliable ABC model choice via random forests Bioinformatics tools:::Rd_expr_doi("10.1093/bioinformatics/btv684")

Examples

Run this code

data(snp)
modindex <- snp$modindex[1:500]
sumsta <- snp$sumsta[1:500,]
data1 <- data.frame(modindex, sumsta)
model.rf <- abcrf(modindex~., data1, ntree=100)
data(snp.obs)
predict(model.rf, snp.obs, data1, ntree=100)