find_MixtureThreshold_Simulation: Single bootstrap sample of mixture model and threshold on...

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

Description

Function used to apply a single bootstrap sample of two component univariate mixture models on feature of interest and find real intersection (valley between the two mixture model means). This function is intended for simulation studies, where the underlying classification of each feature is known; to be called from inside a for-loop for no.bootstrap iterations.

Usage

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

Arguments

dat	vector of selected features.
gRoup	Known classification of features (groups A or B).
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

Details

Default arguments are method = 'intersection', apply.all.dat = FALSE in order to implement FSPmix algorithm.

Value

mix.threshold	Real value of mixture intersection
boot.samp	Bootstrap sample of data set/ vector
mix.mean	Vector of mixture model means
sw	boolean: Indicator variable denoting if mix.threshold was found (sw == 1); if threhsold was not found sw == 0

Author(s)

Marcela Cespedes and Amy Chan

Examples

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

# Simulation study for 20 Genes
dat<- SimulationStudy1()
head(dat)

op<- find_MixtureThreshold_Simulation(dat = dat[,1], gRoup =as.character(dat$group),
                        boot.size = 800, method='intersection')

str(op)

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