d3m.parallel: Parallel Version of Two Sample Test with Distribution-Valued...
In D3M: Two Sample Test with Wasserstein Metric

Description Usage Arguments Value Author(s) References Examples

View source: R/d3m_parallel.R

Parallel Version of Two Sample Test with Distribution-Valued Data

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d3m.parallel(cases, control, rm.mean = F, rm.var = F, paranum = 101,
  q = 2, bsn = 5000, n.core = 4, type = "SOCK", seed = c(100, 200,
  300, 400))

`cases`	name of case group data (matrix)
`control`	names of control group data (matrix)
`rm.mean`	standarize each rows of cases and control to mean=0.
`rm.var`	standarize each rows of cases and control to var=1.
`paranum`	the number of quatile discretization + 1. Default is discretized by 1 %.
`q`	power of Wasserstein metric. Default is q = 2.
`bsn`	the number of resampling. Default is bsn = 5000.
`n.core`	the number of cores to be used.
`type`	choose the cluster type. "SOCK","MPI","PVM"or "NWS". Default is "SOCK". More detail information, see ?clusterMap in "parllel" and/or "snow" package.
`seed`	seed for random number generator assigned for each cluster. Default is c(100,200,300,400) assuming n.core=4.

result of d3m in the list format, including p-value, test statistics, data of cases, data of control. Each element of list correponds to result from each core.

Yusuke Matsui & Teppei Shimamura

Yusuke Matsui, Masahiro Mizuta, Satoru Miyano and Teppei Shimamura.(2015) D3M:Detection of differential distributions of methylation patterns (submitted). BIORXIV/2015/023879.

Antonio Irpino and Rossanna Verde.(2015) Basic Statistics for distributional symbolic variables: a new metric-based approach. Adv.Data.Anal.Classif(9) 143–175

library(D3M)
nrep <- 12
cases <- Map(rbeta,rep(30,nrep),rep(1,nrep),rep(5,nrep))
cases <- do.call("rbind",cases)
control <- Map(rbeta,rep(30,nrep),rep(1,nrep),rep(5,nrep))
control <- do.call("rbind",control)
## do not run.
#d3m.parallel(cases,control,rm.mean = FALSE, rm.var = FALSE, bsn = 1000)