covRegAbcrf: Predict posterior covariance between two parameters for new...

In abcrf: Approximate Bayesian Computation via Random Forests

Predict posterior covariance between two parameters for new data using two reg-ABC-RF objects

Description

Using two reg-ABC-RF objects constructed on the same reference table for two different response variables, this function predicts the posterior covariance between those two response variables, given a new dataset of summaries.

Usage

## S3 method for class 'regAbcrf'
covRegAbcrf(regForest1, regForest2, obs, training1, training2,
ntree=500, mtry=max(floor((dim(training1)[2]-1)/3), 1),
sampsize=min(1e5, dim(training1)[1]), 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, ... )

Arguments

regForest1, regForest2	regAbcrf objects.
obs	a data frame containing the summary statistics of the observed data sets.
training1, training2	data frames containing the reference table respectively used to train the regAbcrf objects regForest1 and regForest2.
ntree	number of trees to grow in the forest, by default equal to 500 trees.
mtry	Number of variables to possibly split at in each node for the regression random forest. 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 whether or not the calculations of the regression random forest (forest used to predict a response from the observed dataset) 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.
paral.predict	a boolean that indicates if random forests predictions should be parallelized.
ncores.predict	the number of CPU cores to use for the regression random forest predictions. 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 predicts posterior covariance.

Value

covRegAbcrf returns predicted posterior covariances between response variables of two reg-ABC-RF objects, for a new data set.

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

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

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)
N1 <- snp$param$N1[modindex == "3"]
N1 <- N1[1:500]
data3 <- data.frame(N1, sumsta) 
model.rf.N1 <- regAbcrf(N1~., data3, ntree=100)
data(snp.obs)
covRegAbcrf(model.rf.r, model.rf.N1, snp.obs, 
            data2, data3, ntree=100)

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