Simultaneous confidence intervals for relative contrast effects The procedure controls the FWER in the strong sense.
nparcomp.wrapper(model, data, hypotheses, alpha, alternative,
asy.method)A list containing:
A numeric vector containing the adjusted pValues
A logical vector indicating which hypotheses are rejected
A matrix containing the estimates and the lower and upper confidence bound
A Mutoss S4 class of type errorControl, containing the type of error controlled by the function.
FrankKonietschke
A two-sided formula specifying a numeric response variable and a factor with more than two levels.
A dataframe containing the variables specified the model
Character string defining the type of contrast. It should be one of "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus".
the significance level
Character string defining the alternative hypothesis, one of "two.sided", "less" or "greater"
A character string defining the asymptotic approximation method, one of "logit", for using the logit transformation function, "probit", for using the probit transformation function, "normal", for using the multivariate normal
With this function, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes adjusted p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers