MC.Xsc.statistics function

Size and Power for the One Sample RAD Probability-Mean Test Comparison

This Monte-Carlo simulation procedure provides the power and size of the one sample RAD probability-mean test, using the Generalized Wald-type statistic.

MC.Xsc.statistics(Nrs, numMC = 10, fit, pi0 = NULL, type = "ha", siglev = 0.05)

Arguments

• Nrs: A vector specifying the number of reads/sequence depth for each sample.

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

• fit: A list (in the format of the output of dirmult function) containing the data parameters for evaluating either the size or power of the test.

• pi0: The RAD-probability mean vector. If the type is set to "hnull" then pi0 is set by the sample in fit.

• 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.

Returns

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

Details

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

Examples

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
        nrs <- rep(15000, 25)
        
        ### Computing size of the test statistics (Type I error)
        pval1 <- MC.Xsc.statistics(nrs, numMC, fit.tonsils, fit.saliva$pi, "hnull")
        pval1
        
        ### Computing Power of the test statistics (Type II error)
        pval2 <- MC.Xsc.statistics(nrs, numMC, fit.throat, fit.tonsils$pi)
        pval2