rf_clf.comps: rf_clf.comps

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

rf_clf.comps

Description

It runs standard random forests with oob estimation for classification of one level VS all other levels of one category in the datasets. The output includes a list of rf models for the sub datasets and all important statistics for each of features.

Usage

rf_clf.comps(
  df,
  f,
  comp_group,
  verbose = FALSE,
  clr_transform = TRUE,
  rf_imp_values = FALSE,
  ntree = 500,
  p.adj.method = "bonferroni",
  q_cutoff = 0.05
)

Arguments

df	Training data: a data.frame.
f	A factor in the metadata with at least two levels (groups).
comp_group	A string indicates the group in the f
verbose	A boolean value indicating if show computation status and estimated runtime.
clr_transform	A boolean value indicating if the clr-transformation applied.
rf_imp_values	A boolean value indicating if compute both importance score and pvalue for each feature.
ntree	The number of trees.
p.adj.method	The p-value correction method, default is "bonferroni".
q_cutoff	The cutoff of q values for features, the default value is 0.05.

Value

...

Author(s)

Shi Huang

See Also

ranger

Examples

df0 <- data.frame(t(rmultinom(60, 300,c(.001,.6,.2,.3,.299))))
df <- data.frame(rbind(t(rmultinom(7, 75, c(.21,.6,.12,.38,.099))),
            t(rmultinom(8, 75, c(.001,.6,.42,.58,.299))),
            t(rmultinom(15, 75, c(.011,.6,.22,.28,.289))),
            t(rmultinom(15, 75, c(.091,.6,.32,.18,.209))),
            t(rmultinom(15, 75, c(.001,.6,.42,.58,.299)))))
f=factor(c(rep("A", 15), rep("B", 15), rep("C", 15), rep("D", 15)))
comp_group="A"
comps_res<-rf_clf.comps(df, f, comp_group, verbose=FALSE, ntree=500,
                        p.adj.method = "bonferroni", q_cutoff=0.05)
comps_res

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