Description Usage Arguments Details Value Examples
View source: R/MC.Xsc.statistics.R
This Monte-Carlo simulation procedure provides the power and size of the one sample RAD probability-mean test, using the Generalized Wald-type statistic.
1 | MC.Xsc.statistics(Nrs, numMC = 10, fit, pi0 = NULL, 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. |
pi0 |
The RAD-probability mean vector. If the type is set to |
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.
Size of the test statistics (under "hnull") or power (under "ha") of the test.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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
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