power_r: Conduct a priori power analysis, and plan the sample size for...

In Keng: Knock Errors Off Nice Guesses

Conduct a priori power analysis, and plan the sample size for r.

Description

Conduct a priori power analysis, and plan the sample size for r.

Usage

power_r(r = 0.2, sig_level = 0.05, power = 0.8, n_ul = 1450000000L)

Arguments

r	Pearson's correlation. Cohen(1988) suggested >=0.1, >=0.3, and >=0.5 as cut-off values of Pearson's correlation r for small, medium, and large effect sizes, respectively.
sig_level	Expected significance level.
power	Expected statistical power.
n_ul	The upper limit of the sample size below which the minimum required sample size is searched for. Non-integer n_ul would be converted to be an integer using as.integer(). n_ul should be at least 3L.

Details

Power_r() follows Aberson (2019) approach to conduct power analysis. n_ul determines the upper limit of the interval below which power_r() searches for the minimum required sample size, hence the number of rows of the returned power table priori and the right limit of the horizontal axis of the returned power plot. When r is very small and power is larger than 0.8, a huge increase of sample size only brings about a trivial increase in power, which is cost-ineffective. To make power_r() omit unnecessary searching, you could set n_ul to be a value less than 1.45e+09 (e.g., 10000).

Value

A Keng_power class, also a list. If n is not given, the following results would be returned: ⁠[[1]]⁠ r, the given r; ⁠[[2]]⁠ d, Cohen's d derived from r; Cohen (1988) suggested >=0.2, >=0.5, and >=0.8 as cut-off values of d for small, medium, and large effect sizes, respectively; ⁠[[3]]⁠ sig_level, the expected significance level; ⁠[[4]]⁠ power, the expected power; ⁠[[5]]⁠ n_ul, the upper limit of sample size; ⁠[[6]]⁠ root, the exact decimal n found by internal uniroot() to reach the expected power; ⁠[[7]]⁠ minimum, the minimum required sample size. ⁠[[8]]⁠ priori, a priori power table with increasing sample sizes (n_i), and corresponding df_i (the df of t-test at the sample size n_i, df_i = n_i - 2), SE_i (the SE of r at the sample size n_i), t_i (the t-test of r), p_i (the p-value of t_i), delta_i (the non-centrality parameter of the t-distribution for the alternative hypothesis, given r and n_i), power_i (the actual power of r at the sample size n_i);.

If sample size n is given, the following results would also be returned: Integer n, the t_test of r at the sample size n with df, SE of r, p (the p-value of t-test), and the post-hoc power analysis with delta_post (the non-centrality parameter of the t-distribution for the alternative hypothesis), and power_post (the post-hoc power of r at the sample size n).

By default, print() prints the primary but not all contents of the Keng_power class. To inspect more contents, use print.AsIs() or list extracting.

References

Aberson, C. L. (2019). Applied power analysis for the behavioral sciences. Routledge.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Examples

power_r(0.2)
print(power_r(0.04))
plot(power_r(0.04))

Keng documentation built on Sept. 1, 2026, 5:07 p.m.