pime.best.prevalence: Best Prevalence
In microEcology/pime: Prevalence Interval for Microbiome Evaluation

Description Usage Arguments Value See Also Examples

This function is the core of PIME. It uses randomForest to build random forests trees for samples classification and variable importance computation. It performs classifications for each prevalence interval returned by pime.prevalence. Variable importance is calculated, returning the Mean Decrease Accuracy (MDA), Mean Decrease Impurity (MDI), overall and by sample group, and taxonomy for each feature/OTU/ASV. PIME keeps the top 30 variables with highest MDA each prevalence level.

1	pime.best.prevalence(prev.list, variable)

`prev.list`	List phyloseq objects with the calculated prevalences for each interval. The output of `pime.prevalence`
`variable`	Any variable in the metadata to be analyzed

The function returns a list with a 'OOB error' table with results from random forests classification, numbers of sequence and OTUs/ASVs for each prevalence level. It also returns a list 'Importance', with a table for each prevalence interval, containing the OTUs/ASVs with importance (MDA and MDI) above the overall mean, with individual importance values and the taxonomy classification.

pime.prevalence randomForest

#Spliting data by "Environment"
phylist=pime.split.by.variable(restroom, "Environment")

#Computes prevalences for each treatment "Environment" separately
prev=pime.prevalence(phylist)

#Finds best prevalence interval
set.seed(42)
pime.best.prevalence(prev, "Environment")