https://github.com/stufield/power
Informal suite of functions to calculate simple power calculations via p-value simulation.
https://github.com/stufield/power
power pvalue r typeii
Last synced: about 1 year ago
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Informal suite of functions to calculate simple power calculations via p-value simulation.
- Host: GitHub
- URL: https://github.com/stufield/power
- Owner: stufield
- License: other
- Created: 2022-04-25T18:35:03.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-12-09T23:14:44.000Z (over 1 year ago)
- Last Synced: 2025-01-25T06:13:07.006Z (over 1 year ago)
- Topics: power, pvalue, r, typeii
- Language: R
- Homepage:
- Size: 1.02 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
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README
---
output: github_document
---
```{r setup, include = FALSE}
options(width = 100)
Sys.setlocale("LC_COLLATE", "en_US.UTF-8") # ensure common sorting envir
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
ver <- desc::desc_get_version(".")
ver <- paste0("https://img.shields.io/badge/Version-", ver,
"-success.svg?style=flat&logo=github")
```
# The power package
[](https://cran.r-project.org/package=power)
[](https://github.com/stufield/power/actions)
[](https://cran.r-project.org/package=power)
[](https://app.codecov.io/gh/stufield/power?branch=main)
[](https://choosealicense.com/licenses/mit/)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
The `power` package contains some simple functions to empirically
simulate and estimate statistical power under various statistical
test conditions. In general, simulations are performed with *known*
effect sizes or differences, and the proportion of detected significant
*p*-values represents the empirical power, i.e. $1 - \beta$ or
`1 - TypeII` error.
The goal is typically get an idea of the required sample size
given an experimental design, statistical test, effect size, and
desired power.
---------------
## Installation
The `power` package is not currently on [CRAN](https://CRAN.R-project.org)
but you can install the latest version from
[github](https://github.com/stufield/power) via:
```{r install-github, eval = FALSE}
remotes::install_github("stufield/power")
```
## Loading `power`
Loading the `power` package is as simple as:
```{r load}
library(power)
```
## Plot power curves
The simplest way to generally plot power curves is via `plot_power_curves()`
which uses `power.t.test()` under the hood:
```{r plot-power-curves, fig.height = 6, fig.width = 11}
plot_power_curves(
delta_vec = seq(0.5, 2, 0.1),
power_vec = seq(0.5, 0.9, 0.1)
)
```
## Power curves and KS-distance
There is a loose "rule-of-thumb" relationship between Sensitivity/Specificity
and KS-distance, and of course, KS is related to effect size. So we
can visualize this relationship also via a standard power curve:
```{r ks-curves, fig.height = 6, fig.width = 11}
ks_tables <- ks_power_table()
ks_tables
# `power` as y-axis
plot(ks_tables)
# `n` as y-axis
plot(ks_tables, plot_power = FALSE)
```
-------------
## Two-Groups
### Empirical Power via Simulation
A more robust (?) empirical calculation of power can be
generated via simulation:
```{r power-curve-n, fig.height = 6, fig.width = 11}
# constant effect size (delta)
size_tbl <- withr::with_seed(1,
t_power_curve(seq(10, 50, 2), delta = 0.75, nsim = 25L)
)
size_tbl
plot(size_tbl)
```
```{r power-curve-d, fig.height = 6, fig.width = 11}
# constant sample size (n)
delta_tbl <- withr::with_seed(2,
t_power_curve(seq(0.5, 2.5, 0.1), n = 10, nsim = 25L)
)
delta_tbl
plot(delta_tbl)
```
### Solve for Sample Size
To solve for the sample size given a corresponding power value
you must have simulate power keeping "delta" (effect size) constant
and varying `n`. For example, the `size_tbl` object created above:
```{r solve-n}
solve_n(size_tbl, 0.8)
```
## Fisher's Exact for Count Data
For count data, Fisher's Exact tests assume a 2x2 contingency
matrix and equal proportions across the margins (rows x cols):
```{r fisher-power}
fisher_power(0.85, 0.75, 200, 200, nsim = 200L)
```
### Fisher's Power Curve
```{r fisher-curve}
f_tbl <- fisher_power_curve(seq(50, 400, 5), p = 0.85, p_diff = -0.1,
nsim = 200L)
f_tbl
```
### Plot the Power Curve
```{r plot-fisher-curve, fig.height = 6, fig.width = 11}
gg_pwr <- plot(f_tbl)
gg_pwr
```
### Solve for `n`
```{r solve-n-fisher}
pwr_n <- solve_n(f_tbl, 0.8)
pwr_n
```
Visually check the curve and add the solution to the `ggplot`.
```{r plot-fisher-curve2, fig.height = 6, fig.width = 11}
gg_pwr +
ggplot2::annotate("segment",
x = c(pwr_n[["n"]], min(f_tbl$n)),
xend = c(pwr_n[["n"]],pwr_n[["n"]]),
y = c(min(f_tbl$power), pwr_n[["power"]]),
yend = c(pwr_n[["power"]], pwr_n[["power"]]),
linetype = "dashed", colour = "#00A499")
```