loocv: Leave one out cross-validation

In qunfengdong/DMBC: Dirichlet-Multinomial Bayes Classifier (DMBC) for Microbiome Classification

Description

Implementation of leave-one-out cross-validataion. It takes in an entire dataset, with first column the sample IDs, second column the group variable (classificatoin variable/disease type), using "Type" as its colum names, and third column the beginning of count table.

Usage

loocv(
  data = data,
  type_col = 2,
  col_start = 3,
  Cutoff_mean = 0.001,
  Cutoff_ratio = 0.1,
  totalReadsCutoff = 500,
  Cutoff_pvalue = 1
)

Arguments

data	A data frame validation set with first column the sample IDs, second column the group variable (classificatoin variable/disease type), and third column the beginning of count table.
type_col	An index indicating at which column is group/type variable. The default is the 3rd column.
col_start	An index indicating at which column is the beginning of bacteria (features) data. Default is the 2nd column.
Cutoff_mean	The minimum average relative abundance allowed in filtering step. Default is 0.0005.
Cutoff_ratio	The non-zero ratio cutoff in filtering features.Default value is 0.1.
totalReadsCutoff	The minimum allowed total reads per sample. Any sample has less than this number of total reads will be removed. Default is 500.
Cutoff_pvalue	The maximal P value allowed for a given feature to be remained in the list of selected features.

Value

A list of CalPrb() results. This output will be further used for Cal_AUC() in order to estimate the final model.

Examples

data(training)
loocv(training)

qunfengdong/DMBC documentation built on April 22, 2020, 7:27 p.m.