| Title: | Sample Size Estimation for Experimental Designs |
|---|---|
| Description: | Power analysis is used in the estimation of sample sizes for experimental designs. Most programs and R packages will only output the highest recommended sample size to the user. Often the user input can be complicated and computing multiple power analyses for different treatment comparisons can be time consuming. This package simplifies the user input and allows the user to view all of the sample size recommendations or just the ones they want to see. The calculations used to calculate the recommended sample sizes are from the 'pwr' package. |
| Authors: | Aaron McGarvey |
| Maintainer: | Aaron McGarvey <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.2 |
| Built: | 2024-11-06 02:39:29 UTC |
| Source: | https://github.com/cran/easypower |
Power analysis is used in the estimation of sample sizes for experimental designs. Most programs and R packages will only output
the highest recommended sample size to the user. Often the user input can be complicated and computing multiple power analyses
for different treatment comparisons can be time consuming. This package simplifies the user input and allows the user to
view all of the sample size recommendations or just the ones they want to see. Currently, one-way ANOVAs n.oneway
and factorial ANOVAs n.multiway are supported. The effect size utilized by the functions is eta-squared which
is equivalent to percentage variance. It is used in the input for all of the functions so that the user may use one standard effect
size for all of their calculations. The calculations used to calculate the recommended sample sizes are from the 'pwr'
package. Future updates are planned to add more experimental designs.
| Package: | easypower |
| Type: | Package |
| Version: | 1.0.1 |
| Date: | 2015-11-04 |
| License: | GPL (>=3) |
| Author: | Aaron McGarvey [email protected] |
| Maintainer: | Aaron McGarvey [email protected] |
Sample size calculations for factorial ANOVAs
n.multiway(iv1 = NULL, iv2 = NULL, iv3 = NULL, iv4 = NULL, interaction.eta2 = "small", sig.level = 0.05, power = 0.8, result = "all", ...)n.multiway(iv1 = NULL, iv2 = NULL, iv3 = NULL, iv4 = NULL, interaction.eta2 = "small", sig.level = 0.05, power = 0.8, result = "all", ...)
iv1 |
The list of data for treatment 1. |
iv2 |
The list of data for treatment 2. |
iv3 |
(optional) The list of data for treatment 3. |
iv4 |
(optional) The list of data for treatment 4. |
interaction.eta2 |
(optional) Either a character string or numeric value of the desired eta squared. Default is set to "small". |
sig.level |
(optional) Desired significance level. Default value is 0.05. |
power |
(optional) Desired level of power. Default value is 0.80. |
result |
The amount of data that will be output to the user (default = "all"). The following are the three output options the user may specify:
|
... |
Extra interactions to pass in. In order to change the effect size of a specific interaction an interaction effect may be added to the function. It must take the form: int# = int.eff.#. |
Acceptable effect size character string values and their numeric equivalents are: "small" (0.01), "med" (0.06), and "large" (0.14).
Sample size recommendations are rounded up to the nearest integer. More detailed examples on n.multiway can be viewed in the vignette.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, N.J.: Lawrence Erlbaum Associates.
# Exercise 8.15, p.400 from Cohen (1988) # Defining the treatments main.eff.1 <- list(name = "R", levels = 2, eta.sq = 0.123) main.eff.2 <- list(name = "C", levels = 4, eta.sq = 0.215) # Running n.multiway n.multiway(iv1=main.eff.1, iv2=main.eff.2, interaction.eta2 = 0.079) # To just view highest n.multiway(iv1=main.eff.1, iv2=main.eff.2, interaction.eta2 = 0.079, result = "highest") # Exercise 8.14, p.397 from Cohen (1988) # Defining the treatments and interaction main.eff.1 <- list(name = "Sex", levels = 2, eta.sq = 0.0099) main.eff.2 <- list(name = "Age", levels = 3, eta.sq = 0.0588) main.eff.3 <- list(name = "Conditions", levels = 4, eta.sq = 0.1506) # Running n.multiway n.multiway(iv1=main.eff.1, iv2=main.eff.2, iv3=main.eff.3, interaction.eta2 = 0.0588)# Exercise 8.15, p.400 from Cohen (1988) # Defining the treatments main.eff.1 <- list(name = "R", levels = 2, eta.sq = 0.123) main.eff.2 <- list(name = "C", levels = 4, eta.sq = 0.215) # Running n.multiway n.multiway(iv1=main.eff.1, iv2=main.eff.2, interaction.eta2 = 0.079) # To just view highest n.multiway(iv1=main.eff.1, iv2=main.eff.2, interaction.eta2 = 0.079, result = "highest") # Exercise 8.14, p.397 from Cohen (1988) # Defining the treatments and interaction main.eff.1 <- list(name = "Sex", levels = 2, eta.sq = 0.0099) main.eff.2 <- list(name = "Age", levels = 3, eta.sq = 0.0588) main.eff.3 <- list(name = "Conditions", levels = 4, eta.sq = 0.1506) # Running n.multiway n.multiway(iv1=main.eff.1, iv2=main.eff.2, iv3=main.eff.3, interaction.eta2 = 0.0588)
Calculates the required sample size for a one-way ANOVA.
n.oneway(iv = iv, sig.level = 0.05, power = 0.8)n.oneway(iv = iv, sig.level = 0.05, power = 0.8)
iv |
List of data for the treatment to be tested. |
sig.level |
Desired significance level (default is 0.05). |
power |
Desired level of power (default is 0.80). |
Returns the recommended sample size given the conditions to achieve the desired power.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, N.J.: Lawrence Erlbaum Associates.
# Exercise 8.10, p.391 from Cohen (1988) main.eff <- list(name = "Teaching", levels = 4, eta.sq = 0.0588) # Running the function with default settings n.oneway(iv = main.eff)# Exercise 8.10, p.391 from Cohen (1988) main.eff <- list(name = "Teaching", levels = 4, eta.sq = 0.0588) # Running the function with default settings n.oneway(iv = main.eff)