getDPfit: MCMC fitting for single sample

In CHAT: Clonal Heterogeneity Analysis Tool

Description

This function implements MCMC with Dirichlet process prior on a numeric vector.

Usage

getDPfit(y, alpha = 0.05, low.thr = 0.05,prior,mcmc)

Arguments

y	input numeric vector, can be either sAGP or CCF from one sample.
alpha	significance level.
low.thr	values below this threshold in y will be omitted.
prior	a list of prior parameters required for DPdensity. An example is data(prior).
mcmc	a list of parameters required to run MCMC for DPdensity. An example is data(mcmc).

Details

Three models are evaluated in this function. 0) There is not enough events (n<5) to evaluate which model is true. 1) Normal-Uniform mixture and 2) Normal mixture with unknown number of Guassian peaks. The first model is evaluated by SampleNMM(), and the second by MCMC fitting. The two models are compared by BIC scores and a P-value is obtained from likelihood ratio test.

Value

A list is returned. In case of model 0, the list contains:

model

always 0

In case of model 1, the list contains:

PA0	peak information, always equals -1.
A	proportion of Uniform component.
mu	mean of Normal component.
sigma	standard deviation of Normal component.
P	P-value
model	always 1

In case of model 2, the list contains:

PA0	peak information
x,y	density function fitted by MCMC.
P	P value
model	always 2.

Author(s)

Bo Li

Examples

data(mcmc)
data(prior)
## model 1
y1=c(runif(50),rnorm(100,0.5,0.1))
getDPfit(y1,prior=prior,mcmc=mcmc)$model
## model 2
y2=c(rnorm(100,0.3,0.05),rnorm(100,0.7,0.05))
getDPfit(y2,prior=prior,mcmc=mcmc)$model

CHAT documentation built on May 29, 2017, 10:32 p.m.