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https://inbo.github.io/effectclass/

Interpret effects and visualise uncertainty
https://inbo.github.io/effectclass/

effect-size fan-chart r r-package r-stats

Last synced: about 2 months ago
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Interpret effects and visualise uncertainty

Host: GitHub
URL: https://inbo.github.io/effectclass/
Owner: inbo
License: gpl-3.0
Created: 2019-05-22T08:44:06.000Z (over 5 years ago)
Default Branch: main
Last Pushed: 2024-02-28T15:06:10.000Z (7 months ago)
Last Synced: 2024-05-01T12:39:51.233Z (5 months ago)
Topics: effect-size, fan-chart, r, r-package, r-stats
Language: R
Homepage: https://effectclass.netlify.com
Size: 5.21 MB
Stars: 3
Watchers: 7
Forks: 1
Open Issues: 1
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.cff

Awesome Lists containing this project

awesome-ggplot2 - effectclass

README

        # effectclass

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The `effectclass` package helps interpreting effects and visualising uncertainty. 

It classifies the effects by comparing a coverage interval with a reference, lower and upper threshold. The result is a 10 scale classification of the effect. You can reduced it to a 4 scale classification. `effectclass` provides `stat_effect()` and `scale_effect()` to visualise the effects as points with different shapes.

The Bank of England visualises uncertainty by using a fan plot^[Britton, E.; Fisher, P. & J. Whitley (1998). [The Inflation Report Projections: Understanding the Fan Chart](https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/1998/the-inflation-report-projections-understanding-the-fan-chart). Bank of England Quarterly Bulletin. Retrieved 2019-05-22.]. Instead of displaying a single coverage interval, they recommend to display a bunch of coverage intervals with different levels of transparency.

## Installation

You can install the released version of `effectclass` from [GitHub](https://github.com/inbo/effectclass) with:

``` r

# installation requires the "remotes" package

# install.package("remotes")

remotes::install_github("inbo/effectclass")

```

## Example

Classifying effect for usage in a table

``` r

library(effectclass)

z <- data.frame(

  effect = c("unknown\neffect", "potential\npositive\neffect",

             "potential\nnegative\neffect", "no effect", "positive\neffect",

             "negative\neffect", "moderate\npositive\neffect",

             "moderate\nnegative\neffect", "strong\npositive\neffect",

             "strong\nnegative\neffect"),

  estimate = c( 0,  0,    0,   0,   1,   -1,   0.5, -0.5, 1.5, -1.5),

  lcl =      c(-2, -0.9, -2,  -0.9, 0.1, -2,   0.1, -0.9, 1.1, -2),

  ucl =      c( 2,  2,    0.9, 0.9, 2,   -0.1, 0.9, -0.1, 2,   -1.1)

)

classification(z$lcl, z$ucl, threshold = c(-1, 1), reference = 0)

```

Adding a classification to a plot

``` r

library(ggplot2)

ggplot(z, aes(x = effect, y = estimate, ymin = lcl, ymax = ucl)) +

  stat_effect(threshold = c(-1, 1), reference = 0, size = 3)

```

Creating a fan plot

``` r

z <- data.frame(year = 1990:2019, dx = rnorm(30), s = rnorm(30, 1, 0.05))

z$index <- cumsum(z$dx)

library(ggplot2)

ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan() + geom_line()

```