Adjusting raw p-values of a CTP

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.

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.

Examples


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)
#> 
#> Hypotheses to be tested 
#> =======================
#> 
#>  hyp.no level hypothesis.name
#>       1     1            [12]
#>       2     1            [13]
#>       3     1            [14]
#>       4     1            [23]
#>       5     1            [24]
#>       6     1            [34]
#>       1     2           [123]
#>       2     2           [124]
#>       3     2        [12][34]
#>       4     2           [134]
#>       5     2        [13][24]
#>       6     2        [14][23]
#>       7     2           [234]
#>       1     3          [1234]
#> 
#> Connection structure of the hypotheses 
#> ======================================
#> 
#>  Level         Connection
#>      1      [12] -> [123]
#>      1      [12] -> [124]
#>      1   [12] -> [12][34]
#>      1      [13] -> [123]
#>      1      [13] -> [134]
#>      1   [13] -> [13][24]
#>      1      [14] -> [124]
#>      1      [14] -> [134]
#>      1   [14] -> [14][23]
#>      1      [23] -> [123]
#>      1   [23] -> [14][23]
#>      1      [23] -> [234]
#>      1      [24] -> [124]
#>      1   [24] -> [13][24]
#>      1      [24] -> [234]
#>      1   [34] -> [12][34]
#>      1      [34] -> [134]
#>      1      [34] -> [234]
#>      2 [12][34] -> [1234]
#>      2    [123] -> [1234]
#>      2    [124] -> [1234]
#>      2 [13][24] -> [1234]
#>      2    [134] -> [1234]
#>      2 [14][23] -> [1234]
#>      2    [234] -> [1234]

# 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)
#> 
#> Summary of Closed Testing Procedure
#> ===================================
#> 
#> Model : y ~ Factor , test : my Test 
#> 
#> Factor levels: 1=A, 2=B, 3=C, 4=D 
#> 
#> Elementary Hypotheses and p-values
#> ----------------------------------
#> 
#>  Hypothesis raw p-value adj. p-value
#>        [12]      0.4374       0.6762
#>        [13]      0.6485       0.7112
#>        [14]      0.4103       0.7112
#>        [23]      0.2203       0.4704
#>        [24]      0.1302       0.4633
#>        [34]      0.6725       0.7112
 Display(result)

Arguments

Value

See also

Examples