search for: rfsrc

...ript (and data set if necessary) that isolates the error. Additional documentation can be found at: https://kogalur.github.io/randomForestSRC/ ------------------------------------------ Details are as follows: Ensembles in regression now support Greenwald-Khanna approximate quantile queries via rfsrc(), predict.rfsrc() and the new wrapper quantileReg.rfsrc(). Related to this, a new split rule "quantile.regr" has been added. Another new wrapper, imbalanced.rfsrc(), implements various solutions to the two-class imbalanced problem, including the newly proposed quantile-classifier approa...

...ript (and data set if necessary) that isolates the error. Additional documentation can be found at: https://kogalur.github.io/randomForestSRC/ ------------------------------------------ Details are as follows: Ensembles in regression now support Greenwald-Khanna approximate quantile queries via rfsrc(), predict.rfsrc() and the new wrapper quantileReg.rfsrc(). Related to this, a new split rule "quantile.regr" has been added. Another new wrapper, imbalanced.rfsrc(), implements various solutions to the two-class imbalanced problem, including the newly proposed quantile-classifier approa...

...relative abundance) as response matrix, and acoustic indices as predictors. I?d then like to know: - total variance explained & error - variable importance (stable rank at least) - proximity matrix The interface for package randomForestSRC looks hopeful. You can specify a MRF like this: > rfsrc(Multivar(y1, y2, ..., yd) ~ . , my.data, ?) So for a data matrix containing 1984 observations of 26 Acoustic indices and 65 species (UN. CG etc) - uk_ai_sp - It seems you can build a multivariate regression model like this: > uk_sp_ai.mrf <-rfsrc(Multivar(UN,CG,ZL,ML,MH,H.,PH,IP,RL,WK,CO,BZ...