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Power and Sample Size for Two-Way ANOVA

Source: R/getDesignMeans.R
getDesignTwoWayANOVA.Rd

Obtains the power and sample size for two-way analysis of variance.

Usage

getDesignTwoWayANOVA(
  beta = NA_real_,
  n = NA_real_,
  nlevelsA = 2,
  nlevelsB = 2,
  means = NA_real_,
  stDev = 1,
  rounding = TRUE,
  alpha = 0.05
)

Arguments

beta

The type II error.

n

The total sample size.

nlevelsA

The number of groups for Factor A.

nlevelsB

The number of levels for Factor B.

means

The matrix of treatment means for Factors A and B combination.

stDev

The common standard deviation.

rounding

Whether to round up sample size. Defaults to 1 for sample size rounding.

alpha

The two-sided significance level. Defaults to 0.05.

Value

An S3 class designTwoWayANOVA object with the following components:

  • alpha: The two-sided significance level.

  • nlevelsA: The number of levels for Factor A.

  • nlevelsB: The number of levels for Factor B.

  • means: The matrix of treatment group means.

  • stDev: The common standard deviation.

  • effectsizeA: The effect size for Factor A.

  • effectsizeB: The effect size for Factor B.

  • effectsizeAB: The effect size for Factor A and Factor B interaction.

  • rounding: Whether to round up sample size.

  • powerdf: The data frame containing the power and sample size results. It has the following variables:

    • n: The sample size.

    • powerA: The power to reject the null hypothesis that there is no difference among Factor A levels.

    • powerB: The power to reject the null hypothesis that there is no difference among Factor B levels.

    • powerAB: The power to reject the null hypothesis that there is no interaction between Factor A and Factor B.

Author

Kaifeng Lu, kaifenglu@gmail.com

Examples


(design1 <- getDesignTwoWayANOVA(
  beta = 0.1, nlevelsA = 2, nlevelsB = 2,
  means = matrix(c(0.5, 4.7, 0.4, 6.9), 2, 2, byrow = TRUE),
  stDev = 2, alpha = 0.05))
#>     n    powerA    powerB   powerAB alpha nlevelsA nlevelsB stDev effectsizeA
#> 1 156 0.9028350 1.0000000 0.9460967  0.05        2        2     2  0.06890625
#> 2  12 0.1267756 0.9807046 0.1424807  0.05        2        2     2  0.06890625
#> 3 132 0.8492519 1.0000000 0.9062945  0.05        2        2     2  0.06890625
#>   effectsizeB effectsizeAB
#> 1    1.788906   0.08265625
#> 2    1.788906   0.08265625
#> 3    1.788906   0.08265625