HMP (version 2.0.1)

MC.Xdc.statistics: Size and Power for the Several-Sample DM Parameter Test Comparison

Description

This Monte-Carlo simulation procedure provides the power and size of the several sample Dirichlet-Multinomial parameter test comparison, using the likelihood-ratio-test statistics.

Usage

MC.Xdc.statistics(group.Nrs, numMC = 10, alphap, type = "ha", 
		siglev = 0.05, est = "mom")

Arguments

group.Nrs

A list specifying the number of reads/sequence depth for each sample in a group with one group per list entry.

numMC

Number of Monte-Carlo experiments. In practice this should be at least 1,000.

alphap

If "hnull": A matrix where rows are vectors of alpha parameters for the reference group. If "ha": A matrix consisting of vectors of alpha parameters for each taxa in each group.

type

If "hnull": Computes the size of the test. If "ha": Computes the power of the test. (default)

siglev

Significance level for size of the test / power calculation. The default is 0.05.

est

The type of parameter estimator to be used with the Likelihood-ratio-test statistics, 'mle' or 'mom'. Default value is 'mom'. (See Note 2 in details)

Value

Size of the test statistics (under "hnull") or power (under "ha") of the test.

Details

1. Note 1: Though the test statistic supports an unequal number of reads across samples, the performance has not yet been fully tested.

2. Note 2: 'mle' will take significantly longer time and may not be optimal for small sample sizes; 'mom' will provide a more conservative result in such a case.

3. Note 3: All components of alphap should be non-zero or it may result in errors and/or invalid results.

Examples

# NOT RUN {
	data(saliva)
	data(throat)
	data(tonsils)
	
	### Get a list of dirichlet-multinomial parameters for the data
	fit.saliva <- DM.MoM(saliva) 
	fit.throat <- DM.MoM(throat)
	fit.tonsils <- DM.MoM(tonsils)
	
	### Set up the number of Monte-Carlo experiments
	### We use 1 for speed, should be at least 1,000
	numMC <- 1
	
	### Generate the number of reads per sample
	### The first number is the number of reads and the second is the number of subjects
	nrsGrp1 <- rep(12000, 9)
	nrsGrp2 <- rep(12000, 11)
	nrsGrp3 <- rep(12000, 12)
	group.Nrs <- list(nrsGrp1, nrsGrp2, nrsGrp3)
	
	### Computing size of the test statistics (Type I error)
	alphap <- fit.saliva$gamma
	pval1 <- MC.Xdc.statistics(group.Nrs, numMC, alphap, "hnull")
	pval1
	
	### Computing Power of the test statistics (Type II error)
	alphap <- rbind(fit.saliva$gamma, fit.throat$gamma, fit.tonsils$gamma)
	pval2 <- MC.Xdc.statistics(group.Nrs, numMC, alphap)
	pval2
# }