# Predict posterior covariance between two parameters for new data using two reg-ABC-RF objects 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. ```r ## 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. ## Returns `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 tools:::Rd_expr_doi("10.1093/bioinformatics/bty867") ## See Also `regAbcrf`, `predict.regAbcrf`, `err.regAbcrf`, `plot.regAbcrf`, `ranger`, `densityPlot` ## Examples ```r 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) ```

covRegAbcrf function