ccrepe_analysis: Conduct ccrepe analysis

In AlmaasLab/micInt: Find microbial interactions

Conduct ccrepe analysis

Description

A wrapper around the ccrepe function, provides parallel analysis

Usage

ccrepe_analysis(
  ccrepe_job,
  commonargs,
  parallel = FALSE,
  ncpus = getOption("micInt.ncpus", 1L),
  cl = NULL,
  verbose = TRUE
)

Arguments

ccrepe_job	A list of jobs to be passed to ccrepe. The lists themselves are named lists with the arguments being passed to ccrepe
commonargs	ccrepe arguments common for all jobs
parallel	Should the analysis be run in parallel?
ncpus	If parallel = TRUE, how many cores should be used? Defaults to one.
cl	Custom cluster to use if parallel = TRUE.
verbose	Should the function display how much time it spent?

Value

A list of the results of the various jobs. Each element of this list containings the ccrepe results

See Also

ccrepe

Examples

library(micInt)
data("seawater")
sim.scores <- similarity_measures(subset= c("spearman","pearson"))
sim_funs <- lapply(sim.scores,sim_measure_function)
refined_table <- refine_data(seawater)
ccrepe_commonargs <- list(x = refined_table, iterations = 100,
memory.optimize = TRUE
, min.subj = 5)
ccrepe_job <- list(spearman=list(sim.score = sim_funs[["spearman"]]),
pearson = list(sim.score = sim_funs[["pearson"]]))
ccrepe_analysis(ccrepe_job,ccrepe_commonargs, parallel = TRUE, ncpus = 2)

AlmaasLab/micInt documentation built on April 1, 2022, 10:37 a.m.