Description Usage Arguments Details Value Examples
View source: R/MC.ZT.statistics.R
This Monte-Carlo simulation procedure provides the power and size of the Multinomial vs. Dirichlet-Multinomial goodness of fit test, using the C(α)-optimal test statistics of Kim and Margolin (1992) (t statistics) and the C(α)-optimal test statistics of (Paul et al., 1989).
1 | MC.ZT.statistics(Nrs, numMC = 10, fit, type = "ha", siglev = 0.05)
|
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. |
type |
If |
siglev |
Significance level for size of the test / power calculation. The default is 0.05. |
Note: Though the test statistic supports an unequal number of reads across samples, the performance has not yet been fully tested.
A vector containing both the size of the test statistics (under "hnull"
) or power (under "ha"
) of the test for both the z and t statistics.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | data(saliva)
### Get a list of dirichlet-multinomial parameters for the data
fit.saliva <- DM.MoM(saliva)
### 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.ZT.statistics(nrs, numMC, fit.saliva, "hnull")
pval1
### Computing Power of the test statistics (Type II error)
pval2 <- MC.ZT.statistics(nrs, numMC, fit.saliva)
pval2
|
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