Plotting contrasts for categorical model with interaction
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 48/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- r
- Domain
- data-visualization
Research direction
Start by running the supplied Arabidopsis example with estimate_means(), estimate_contrasts(), and plot(contrasts, means), including facet_wrap(vars(status)). Inspect how the plotting entry point combines contrasts and means; the work is done when the categorical interaction contrasts render in separate status facets without changing their calculated values.
Written by the indexing model from the issue text.
Description
When plotting contrasts for a model with an interaction term between two categorical variables, is it possible to split the lighthouse plots into facets?
It seems to me that the contrast are correctly calculated, but the output plot is not what I would expect.
library(ggplot2)
library(modelbased)
library(see)
data("Arabidopsis", package = "lme4")
model = lm(total.fruits ~ amd*status, Arabidopsis)
means = estimate_means(model)
#> We selected `by = c("amd", "status")`.
contrasts = estimate_contrasts(model, contrast = "amd", by = "status")
contrasts
#> Marginal Contrasts Analysis
#>
#> Level1 | Level2 | status | Difference | 95% CI | SE | t(619) | p
#> ----------------------------------------------------------------------------------------
#> clipped | unclipped | Normal | -5.73 | [-14.02, 2.56] | 4.22 | -1.36 | 0.175
#> clipped | unclipped | Petri.Plate | -4.62 | [-21.61, 12.38] | 8.65 | -0.53 | 0.594
#> clipped | unclipped | Transplant | 0.37 | [-12.48, 13.21] | 6.54 | 0.06 | 0.955
#>
#> Marginal contrasts estimated at amd
#> p-value adjustment method: Holm (1979)
plot(contrasts, means) +
facet_wrap(vars(status))
Created on 2024-10-28 with reprex v2.1.0
- Dominant language
- R
- Stars
- 263
- Forks
- 22
- Avg merge
- 1h 22m
- Merged PRs (30d)
- 11
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