This vignette will present how to visualize the effects and interactions using estimate_relation().
Note that the statistically correct name of estimate_relation is estimate_expectation (which can be used as an alias), as it refers to expected predictions (read more).
Simple regression
Linear relationship
library(modelbased) model <- lm(Sepal.Length ~ Sepal.Width, data = iris) visualization_data <- estimate_relation(model) head(visualization_data)> Model-based Predictions > > Sepal.Width | Predicted | SE | 95% CI > --------------------------------------------- > 2.00 | 6.08 | 0.18 | [5.73, 6.43] > 2.27 | 6.02 | 0.14 | [5.74, 6.30] > 2.53 | 5.96 | 0.11 | [5.75, 6.17] > 2.80 | 5.90 | 0.08 | [5.75, 6.06] > 3.07 | 5.84 | 0.07 | [5.71, 5.97] > 3.33 | 5.78 | 0.08 | [5.62, 5.94] > > Variable predicted: Sepal.Length > Predictors modulated: Sepal.Width library(ggplot2) plot(visualization_data, line = list(color = "red")) + theme_minimal()
More complex regressions
Polynomial
lm(Sepal.Length ~ poly(Sepal.Width, 2), data = iris) |> modelbased::estimate_relation(length = 50) |> plot()
Additive Models
library(mgcv)> Loading required package: nlme> This is mgcv 1.9-3. For overview type 'help("mgcv-package")'. mgcv::gam(Sepal.Length ~ s(Sepal.Width), data = iris) |> modelbased::estimate_relation(length = 50) |> plot()