CHAT (version 1.1)

getDPfit: MCMC fitting for single sample

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).

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.

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="" with="" unknown="" number="" of="" guassian="" peaks.="" the="" first="" 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.

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