CTP (version 3.0.1)

Adjust_raw: Adjusting raw p-values of a CTP

Description

Function that adjusts the raw p-values of the elementary hypotheses of a closed testing procedure. The raw p-values are adjusted according to the closure principle. The adjusted p-value is calculated as the maximum of the raw p-value from the current hypothesis in question and the raw p-values from all subsequent hypotheses that contain the current hypothesis.

Usage

Adjust_raw(
  ctp.struc,
  p.value,
  dataset.name = NULL,
  factor.name = NULL,
  factor.levels = NULL,
  model = NULL,
  test.name = NULL
)

Arguments

ctp.struc

Object generated by IntersectHypotheses

p.value

Vector of raw p-values in the order of the hypotheses created by summary.ctp.str

dataset.name

Character string naming the analysis dataset (optional - only for documentation purposes).

factor.name

Character string naming the factor whose levels are compared (optional - only for documentation purposes).

factor.levels

Vector of type "character" containing the levels of the treatment factor (optional - only for documentation purposes).

model

Model used in the analysis (optional - only for documentation purposes).

test.name

Character string naming the statistical test applied.

Value

An object of oldClass = "ctp" to be used for summarizing and plotting the results.

See Also

IntersectHypotheses, AnalyseCTP, Display, summary.ctp

Examples

# NOT RUN {
Pairwise <- IntersectHypotheses(list(c(1,2), c(1,3), c(1,4), c(2,3), c(2,4), c(3,4)))
Display(Pairwise)
summary(Pairwise)

# the vector of p-values calculated by another software

  p.val <- c(
            0.4374,
            0.6485,
            0.4103,
            0.2203,
            0.1302,
            0.6725,
            0.4704,
            0.3173,
            0.6762,
            0.7112,
            0.2866,
            0.3362,
            0.2871,
            0.4633)

 result <- Adjust_raw(ctp.struc=Pairwise, p.value=p.val)

 ## details may be documented

 result <- Adjust_raw(Pairwise, p.value=p.val
          ,dataset.name="my Data", factor.name="Factor"
          ,factor.levels=c("A","B","C","D"), model=y~Factor
          ,test.name="my Test")

 summary(result)
 Display(result)


# }