pime.error.prediction: Error prediction

Description Usage Arguments Examples

Description

For each prevalence interval it randomizes the samples labels into arbitrary groupings using n random permutations (user defined). For each, randomized and prevalence filtered, dataset the OOB error rate is calculated to estimate whether the original differences in groups of samples occur by chance. Results are in a list containing a table and a boxplot summarizing the results.

Usage

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pime.error.prediction(physeq, variable, bootstrap, max.prev,
  parallel = TRUE)

Arguments

physeq

Input in phyloseq format, original data, pime.prevalence unfiltered

variable

Variable to run the model, to be randomized

bootstrap

Number to run randomizations

max.prev

Max prevalence reached with pime.prevalence()

parallel

Whether or not to run in parallel. Default is TRUE

Examples

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phylist=pime.split.by.variable(restroom, "Environment")
prev=pime.prevalence(phylist)
pime.best.prevalence(prev, "Environment")
set.seed(42)
result=pime.error.prediction(restroom, "Environment", bootstrap=10, max.prev=90, parallel=TRUE)
result$Plot
result$'Results table'

microEcology/pime documentation built on Nov. 13, 2019, 11:16 p.m.