https://github.com/mihaiconstantin/powerly
`powerly` is an R package for conducting sample size analysis for network models and more.
https://github.com/mihaiconstantin/powerly
network-models power-analysis psychology r-package sample-size-calculation
Last synced: 6 months ago
JSON representation
`powerly` is an R package for conducting sample size analysis for network models and more.
- Host: GitHub
- URL: https://github.com/mihaiconstantin/powerly
- Owner: mihaiconstantin
- License: other
- Created: 2020-07-29T17:16:17.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2025-01-15T12:16:23.000Z (9 months ago)
- Last Synced: 2025-03-23T22:25:15.719Z (7 months ago)
- Topics: network-models, power-analysis, psychology, r-package, sample-size-calculation
- Language: R
- Homepage: https://powerly.dev
- Size: 9.5 MB
- Stars: 9
- Watchers: 2
- Forks: 6
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Sample Size Analysis for Psychological Networks
...and more
## Description
`powerly` is an `R` package that implements the method by [Constantin et al.
(2021)](https://psyarxiv.com/j5v7u) for conducting sample size analysis for
cross-sectional network models. The method implemented is implemented in the
main function `powerly`()`. The implementation takes the form of a three-step
recursive algorithm designed to find an optimal sample size value given a model
specification and an outcome measure of interest. It starts with a Monte Carlo
simulation step for computing the outcome at various sample sizes. Then, it
continues with a monotone curve-fitting step for interpolating the outcome. The
final step employs stratified bootstrapping to quantify the uncertainty around
the fitted curve.
---
---
## Installation
- to install from CRAN run `install.packages("powerly")`
- to install the latest version from GitHub run `remotes::install_github("mihaiconstantin/powerly")`
---
## Example
The code block below illustrates the main function in the package. For more
information, see the documentation `?powerly`, or check out the tutorials at
[powerly.dev](https://powerly.dev).
```r
# Suppose we want to find the sample size for observing a sensitivity of `0.6`
# with a probability of `0.8`, for a GGM true model consisting of `10` nodes
# with a density of `0.4`.
# We can run the method for an arbitrarily generated true model that matches
# those characteristics (i.e., number of nodes and density).
results <- powerly(
range_lower = 300,
range_upper = 1000,
samples = 30,
replications = 20,
measure = "sen",
statistic = "power",
measure_value = .6,
statistic_value = .8,
model = "ggm",
nodes = 10,
density = .4,
cores = 2,
verbose = TRUE
)
# Or we omit the `nodes` and `density` arguments and specify directly the edge
# weights matrix via the `model_matrix` argument.
# To get a matrix of edge weights we can use the `generate_model()` function.
true_model <- generate_model(type = "ggm", nodes = 10, density = .4)
# Then, supply the true model to the algorithm directly.
results <- powerly(
range_lower = 300,
range_upper = 1000,
samples = 30,
replications = 20,
measure = "sen",
statistic = "power",
measure_value = .6,
statistic_value = .8,
model = "ggm",
model_matrix = true_model, # Note the change.
cores = 2,
verbose = TRUE
)
```
To validate the results of the analysis, we can use the `validate()` method. For
more information, see the documentation `?validate`.
```r
# Validate the recommendation obtained during the analysis.
validation <- validate(results)
```
To visualize the results, we can use the `plot` function and indicate the step
that should be plotted.
```r
# Step 1.
plot(results, step = 1)
```
```r
# Step 2.
plot(results, step = 2)
```
```r
# Step 3.
plot(results, step = 3)
```
```r
# Validation.
plot(validation)
```
---
## Contributing
- *To support* a new model, performance measure, or statistic, please open a
[pull request](https://github.com/mihaiconstantin/powerly/pulls) on GitHub.
- *To request* a new model, performance measure, or statistic, please open an
[issue](https://github.com/mihaiconstantin/powerly/issues) on GitHub. If
possible, also include references discussing the topics you are requesting.
---
## Poster
---
## License
The code in this repository is licensed under the [MIT license](https://opensource.org/licenses/MIT).
To use `powerly` please cite:
- Constantin, M. A., Schuurman, N. K., & Vermunt, J. (2021). A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models. PsyArXiv. [https://doi.org/10.31234/osf.io/j5v7u](https://doi.org/10.31234/osf.io/j5v7u)