powerMCTests function

Power Simulation for One-Factorial All-Pairs and Many-To-One Comparison Tests

Performs power simulation for one-factorial all-pairs and Many-To-One comparison tests.

powerMCTests(
  mu,
  n = 10,
  errfn = c("Normal", "Lognormal", "Exponential", "Chisquare", "TDist", "Cauchy",
    "Weibull"),
  parms = list(mean = 0, sd = 1),
  test = c("kwManyOneConoverTest", "kwManyOneDunnTest", "kwManyOneNdwTest",
    "vanWaerdenManyOneTest", "normalScoresManyOneTest", "dunnettTest",
    "tamhaneDunnettTest", "ManyOneUTest", "chenTest", "kwAllPairsNemenyiTest",
    "kwAllPairsDunnTest", "kwAllPairsConoverTest", "normalScoresAllPairsTest",
    "vanWaerdenAllPairsTest", "dscfAllPairsTest", "gamesHowellTest", "lsdTest",
    "scheffeTest", "tamhaneT2Test", "tukeyTest", "dunnettT3Test", "pairwise.t.test",
    "pairwise.wilcox.test", "adManyOneTest", "adAllPairsTest", "bwsManyOneTest", 
    
    "bwsAllPairsTest", "welchManyOneTTest"),
  alternative = c("two.sided", "greater", "less"),
  p.adjust.method = c("single-step", p.adjust.methods),
  alpha = 0.05,
  FWER = TRUE,
  replicates = 1000
)

Arguments

• mu: numeric vector of group means.
• n: number of replicates per group. If n is a scalar, then a balanced design is assumed. Otherwise, n must be a vector of same length as mu.
• errfn: the error function. Defaults to "Normal".
• parms: a list that denotes the arguments for the error function. Defaults to list(mean=0, sd=1).
• test: the multiple comparison test for which the power analysis is to be performed. Defaults to "kwManyOneConoverTest".
• alternative: the alternative hypothesis. Defaults to "two.sided", ignored if the selected error function does not use this argument.
• p.adjust.method: method for adjusting p values (see p.adjust).
• alpha: the nominal level of Type I Error.
• FWER: logical, indicates whether the family-wise error should be computed. Defaults to TRUE.
• replicates: the number of Monte Carlo replicates or runs. Defaults to 1000.

Returns

An object with class powerPMCMR.

Details

The linear model of a one-way ANOVA can be written as:

For each Monte Carlo run, the function simulates $\epsilon_{ij}$ based on the given error function and the corresponding parameters. Then the specified all-pairs or many-to-one comparison test is performed. Finally, several effect sizes (Cohen's f ans R-squared), error rates (per comparison error rate, false discovery rate and familywise error rate) and test powers (any-pair power, average per-pair power and all-pairs power) are calculated.

Examples

## Not run:

mu <- c(0, 0, 1, 2)
n <- c(5, 4, 5, 5)
set.seed(100)
powerMCTests(mu, n, errfn="Normal",
 parms=list(mean=0, sd=1),
 test="dunnettTest", replicates=1E4)

powerMCTests(mu, n, errfn="Normal",
 parms=list(mean=0, sd=1),
 test="kwManyOneDunnTest", p.adjust.method = "bonferroni",
 replicates=1E4)
## End(Not run)