rf_reg.cross_appl: rf_reg.cross_appl

In shihuang047/crossRanger: Cross-Application of Ranger Models using microbiome data

rf_reg.cross_appl

Description

Based on pre-computed rf models regressing c_category in each the sub-datasets splited by the s_category, perform cross-datasets application of the rf models. The inputs are precalculated rf regression models, x_list and y_list.

Usage

rf_reg.cross_appl(rf_list, x_list, y_list)

Arguments

rf_list	A list of rf.model objects from rf.out.of.bag.
x_list	A list of training datasets usually in the format of data.frame.
y_list	A list of responsive vector for regression in the training datasets.

Value

...

Author(s)

Shi Huang

See Also

ranger

Examples

df <- data.frame(rbind(t(rmultinom(14, 14*5, c(.21,.6,.12,.38,.099))),
            t(rmultinom(16, 16*5, c(.001,.6,.42,.58,.299))),
            t(rmultinom(30, 30*5, c(.011,.6,.22,.28,.289))),
            t(rmultinom(30, 30*5, c(.091,.6,.32,.18,.209))),
            t(rmultinom(30, 30*5, c(.001,.6,.42,.58,.299)))))
df0 <- data.frame(t(rmultinom(120, 600,c(.001,.6,.2,.3,.299))))
metadata<-data.frame(f_s=factor(c(rep("A", 60), rep("B", 60))),
                     f_s1=factor(c(rep(TRUE, 60), rep(FALSE, 60))),
                     f_c=factor(c(rep("C", 30), rep("H", 30), rep("D", 30), rep("P", 30))),
                     age=c(1:60, 2:61)
                     )

table(metadata[, 'f_c'])
reg_res<-rf_reg.by_datasets(df, metadata, s_category='f_c', c_category='age')
rf_reg.cross_appl(reg_res, x_list=reg_res$x_list, y_list=reg_res$y_list)
reg_res<-rf_reg.by_datasets(df, metadata, nfolds=5, s_category='f_c', c_category='age')
rf_reg.cross_appl(reg_res, x_list=reg_res$x_list, y_list=reg_res$y_list)

shihuang047/crossRanger documentation built on Feb. 7, 2023, 10:03 p.m.