Compute power of test or determine parameters to obtain target power. Inspired by the pwr.f2.test function in the pwr package, but allows for varying noncentrality parameter estimates for a more liberal (default in pwr.f2.test) or conservative (default in this function) estimates (see Aberson, Chapter 5, pg 72).
power.ftest(
num_df = NULL,
den_df = NULL,
cohen_f = NULL,
alpha_level = Superpower_options("alpha_level"),
beta_level = NULL,
liberal_lambda = Superpower_options("liberal_lambda")
)degrees of freedom for numerator
degrees of freedom for denominator
Cohen's f effect size. Note: this is the sqrt(f2) if you are used to using pwr.f2.test
Alpha level used to determine statistical significance.
Type II error probability (power/100-1)
Logical indicator of whether to use the liberal (cohen_f^2\*(num_df+den_df)) or conservative (cohen_f^2\*den_df) calculation of the noncentrality (lambda) parameter estimate. Default is FALSE.
num_df = degrees of freedom for numerator, den_df = degrees of freedom for denominator, cohen_f = Cohen's f effect size, alpha_level = Type 1 error probability, beta_level = Type 2 error probability, power = Power of test (1-beta_level\*100
lambda = Noncentrality parameter estimate (default = cohen_f^2\*den_df, liberal = cohen_f^2\*(num_df+den_df))
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum. Aberson, C. (2019). Applied Power Analysis for the Behavioral Sciences (2nd ed.). New York,NY: Routledge.
design_result <- ANOVA_design(design = "2b",
n = 65,
mu = c(0,.5),
sd = 1,
plot = FALSE)
x1 = ANOVA_exact2(design_result, verbose = FALSE)
ex = power.ftest(num_df = x1$anova_table$num_df,
den_df = x1$anova_table$den_df,
cohen_f = x1$main_result$cohen_f,
alpha_level = 0.05,
liberal_lambda = FALSE)