Power of contrasts

Computes the statistical power of t-tests for comparisons among means.

Usage

pwr.contrast(
  object,
  which,
  by = NULL,
  contrast = c("pairwise", "poly", "trt.vs.ctrl"),
  sig.level = 0.05,
  p.adj = FALSE,
  alternative = c("two.sided", "one.sided"),
  strict = TRUE
)

Arguments

object

a design object created in pwr4exp

which

the factor of interest. Multiple factors can be combined using : or *, e.g., "facA*facB", which represents a single factor that combines the levels of both factors.

by

the variable to condition on

contrast

A character string specifying the contrast method, one of "pairwise", "poly", or "trt.vs.ctrl". Alternatively, a numeric vector defining a single contrast or a (named) list of vectors specifying multiple custom contrasts. If a list is provided, each element must be a vector whose length matches the number of levels of the factor in each by group. In multi-factor scenarios, factor levels are combined and treated as a single factor.

sig.level

significance level, default 0.05

p.adj

whether the sig.level should be adjusted using the Bonferroni method, default FALSE

alternative

one- or two-sided test. Can be abbreviated.

strict

use strict interpretation in two-sided case

Value

For each by condition, returns a data frame containing the contrast value (effect), degrees of freedom (df), type I error rate (sig.level), power, and the test direction (by alternative). When multiple by conditions are present, the results are returned as a list.

Examples

rcbd <- designRCBD(
  treatments = c(2, 2),
  label = list(facA = c("1", "2"), facB = c("1", "2")),
  blocks = 12,
  formula = ~ facA*facB + (1|block),
  means = c(32, 35, 30, 37),
  vcomp = 4,
  sigma2 = 6
)

# If contrast is not specified, pairwise comparisons are conducted
pwr.contrast(rcbd, which = "facA") # Marginal effect of facA
#>               effect df sig.level     power alternative
#> facA1 - facA2     -5 33      0.05 0.9999995   two.sided
pwr.contrast(rcbd, which = "facA", by = "facB") # Conditional effect of facA within levels of facB
#> $`facB = 1`
#>               effect df sig.level     power alternative
#> facA1 - facA2     -3 33      0.05 0.8293757   two.sided
#> 
#> $`facB = 2`
#>               effect df sig.level     power alternative
#> facA1 - facA2     -7 33      0.05 0.9999993   two.sided
#> 

# Custom contrast vector, identical to pairwise comparison
pwr.contrast(rcbd, which = "facA", contrast = c(1, -1))
#>         effect df sig.level     power alternative
#> c(1,-1)     -5 33      0.05 0.9999995   two.sided
pwr.contrast(rcbd, which = "facA", by = "facB", contrast = c(1, -1))
#> $`facB = 1`
#>         effect df sig.level     power alternative
#> c(1,-1)     -3 33      0.05 0.8293757   two.sided
#> 
#> $`facB = 2`
#>         effect df sig.level     power alternative
#> c(1,-1)     -7 33      0.05 0.9999993   two.sided
#> 

# A single factor combining two factors
pwr.contrast(
  rcbd,
  which = "facA*facB",
  contrast = list(
    A1B1vs.A2B1 = c(1, -1, 0, 0),
    A1B1vs.A2B2 = c(1, 0, 0, -1)
  )
)
#>             effect df sig.level     power alternative
#> A1B1vs.A2B1     -3 33      0.05 0.8293757   two.sided
#> A1B1vs.A2B2     -5 33      0.05 0.9980739   two.sided