D3M (version 0.41.1)

d3m.parallel: Parallel Version of Two Sample Test with Distribution-Valued Data

Description

Parallel Version of Two Sample Test with Distribution-Valued Data

Usage

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))

Arguments

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.

Value

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.

References

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

Examples

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)