find_MixtureThreshold: Single bootstrap iteration of application of mixture model...

In MarcelaCespedes/FSPmix: Exploratory tool for fast unsupervised features selection and clustering using mixture models

Description

Bootstrap two component univariate mixture models on feature of interest and find real intersection (valley between the two mixture model means).

Usage

find_MixtureThreshold(dat, boot.size, method=c('diff', 'intersect'),
                          apply.all.dat = FALSE)

Arguments

dat	Vector of selected features
method	Either 'diff' or 'intersection'. Former argument estimates the difference in the posterior probability. Later argument calls on function findInt() to find the real value intersection between two Guassian mixture means.
apply.all.dat	boolean: FALSE implies take sample with replacement on dat and is the default for bootstrapping; TRUE denotes otherwise and no sampling of dat is performed

Value

mix.threshold

Indicator variable denoting if

return(list(mix.threshold=mix.threshold$rr, boot.samp = samp.dat, mix.means = mix.means1, sw = sw))

Author(s)

Marcela Cespedes and Amy Chan

Examples

library(mixtools)
library(reshape2)
source("SimulationStudy2.r")

# Simulation study for 20 Genes
dat<- SimulationStudy2()
head(dat)

op<- find_MixtureThreshold(dat = dat[,1],
                        boot.size = 800, method='intersection')

str(op)

MarcelaCespedes/FSPmix documentation built on May 12, 2020, 5:49 p.m.