rf_clf.cross_appl: rf_clf.cross_appl

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

rf_clf.cross_appl

Description

Based on pre-computed rf models classifying '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 models, and the outputs include accuracy, auc and Kappa statistics.

Usage

rf_clf.cross_appl(rf_model_list, x_list, y_list, positive_class = NA)

Arguments

rf_model_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.
positive_class	A string indicates one common class in each of elements in the y_list.

Value

A object of class rf_clf.cross_appl including a list of performance summary and predicted values of all predictions

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", 30), rep("B", 30), rep("C", 30), rep("D", 30))),
                     f_c=factor(c(rep("C", 14), rep("H", 16), rep("C", 14), rep("H", 16),
                                  rep("C", 14), rep("H", 16), rep("C", 14), rep("H", 16))),
                     f_d=factor(rep(c(rep("a", 10), rep("b", 10), rep("c", 10)), 4)))
res_list<-rf_clf.by_datasets(df, metadata, s_category='f_s', nfolds=5,
                             c_category='f_c', positive_class="C")
rf_model_list<-res_list$rf_model_list

rf_clf.cross_appl(rf_model_list, res_list$x_list, res_list$y_list, positive_class="C")
#--------------------
comp_group="A"
comps_res<-rf_clf.comps(df, f=metadata[, 'f_s'], comp_group, verbose=FALSE,
                        ntree=500, p.adj.method = "bonferroni", q_cutoff=0.05)
comps_res
rf_clf.cross_appl(comps_res$rf_model_list,
                  x_list=comps_res$x_list,
                  y_list=comps_res$y_list,
                  positive_class=comp_group)

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