rf_clf.by_datasets.summ: rf_clf.by_datasets.summ

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

rf_clf.by_datasets.summ

Description

A integrated pipeline for rf_clf.by_datasets. It runs standard random forests with oob estimation for classification of c_category in each the sub-datasets splited by the s_category. The output includes a summary of rf models in the sub datasets and all important statistics for each of features.

Usage

rf_clf.by_datasets.summ(
  df,
  metadata,
  s_category,
  c_category,
  positive_class = NA,
  rf_imp_pvalues = FALSE,
  nfolds = 3,
  verbose = FALSE,
  ntree = 500,
  p_cutoff = 0.05,
  p.adj.method = "bonferroni",
  q_cutoff = 0.05,
  outdir = NULL
)

Arguments

df	Training data: a data.frame.
metadata	A metadata with at least two categorical variables.
s_category	A string indicates the category in the sample metadata: a ‘factor’ defines the sample grouping for data spliting.
c_category	A indicates the category in the sample metadata: a 'factor' used as sample label for rf classification in each of splited datasets.
positive_class	A string indicates one class in the 'c_category' column of metadata.
rf_imp_pvalues	If compute both importance score and pvalue for each feature.
nfolds	The number of folds in the cross validation.
verbose	Show computation status and estimated runtime.
ntree	The number of trees.
p_cutoff	The cutoff of p values for features, the default value is 0.05.
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.
outdir	The output directory, the default is "./".

Value

A list includes a summary of rf models in the sub datasets, all important statistics for each of features, and plots.

See Also

ranger rf_clf.by_datasets

shihuang047/crossRanger documentation built on Nov. 8, 2024, 2:49 a.m.