Simultaneous confidence intervals for sequentially rejective multiple test procedures

Calculates simultaneous confidence intervals for sequentially rejective multiple test procedures.

Usage

simConfint(
  object,
  pvalues,
  confint,
  alternative = c("less", "greater"),
  estimates,
  df,
  alpha = 0.05,
  mu = 0
)

Arguments

object: A graph of class graphMCP.
pvalues: A numeric vector specifying the p-values for the sequentially rejective MTP.
confint: One of the following: A character string "normal", "t" or a function that calculates the confidence intervals. If confint=="t" the parameter df must be specified. If confint is a function it must be of signature ("character", "numeric"), where the first parameter is the hypothesis name and the second the marginal confidence level (see examples).
alternative: A character string specifying the alternative hypothesis, must be "greater" or "less".
estimates: Point estimates for the parameters of interest.
df: Degree of freedom as numeric.
alpha: The overall alpha level as numeric scalar.
mu: The numerical parameter vector under null hypothesis.

Value

A matrix with columns giving lower confidence limits, point estimates and upper confidence limits for each parameter. These will be labeled as "lower bound", "estimate" and "upper bound".

(1-level)/2 in % (by default 2.5% and 97.5%).

Details

For details see the given references.

References

Frank Bretz, Willi Maurer, Werner Brannath, Martin Posch: A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine 2009 vol. 28 issue 4 page 586-604. http://www.meduniwien.ac.at/fwf_adaptive/papers/bretz_2009_22.pdf

Author

Kornelius Rohmeyer rohmeyer@small-projects.de

Examples

est <- c("H1"=0.860382, "H2"=0.9161474, "H3"=0.9732953)
# Sample standard deviations:
ssd <- c("H1"=0.8759528, "H2"=1.291310, "H3"=0.8570892)

pval <- c(0.01260, 0.05154, 0.02124)/2

simConfint(BonferroniHolm(3), pvalues=pval,
    confint=function(node, alpha) {
      c(est[node]-qt(1-alpha,df=9)*ssd[node]/sqrt(10), Inf)
    }, estimates=est, alpha=0.025, mu=0, alternative="greater")
#>     lower bound  estimate upper bound
#> H1  0.000000000 0.8603820         Inf
#> H2 -0.007600126 0.9161474         Inf
#> H3  0.000000000 0.9732953         Inf

# Note that the sample standard deviations in the following call
# will be calculated from the pvalues and estimates.
ci <- simConfint(BonferroniHolm(3), pvalues=pval,
    confint="t", df=9, estimates=est, alpha=0.025, alternative="greater")
ci
#>       lower bound  estimate upper bound
#> [1,]  0.000000000 0.8603820         Inf
#> [2,] -0.007580967 0.9161474         Inf
#> [3,]  0.000000000 0.9732953         Inf

# plotSimCI(ci)