library(knitr) options(knitr.kable.NA = "") knitr::opts_chunk$set(comment = ">", dpi = 300) options(digits = 2) if (!requireNamespace("ggplot2", quietly = TRUE) || !requireNamespace("poorman", quietly = TRUE) || !requireNamespace("see", quietly = TRUE) || !requireNamespace("gganimate", quietly = TRUE) || !requireNamespace("rstanarm", quietly = TRUE)) { knitr::opts_chunk$set(eval = FALSE) } set.seed(333)
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).
library(modelbased) model <- glm(Sepal.Length ~ Sepal.Width, data = iris) visualization_data <- estimate_relation(model) head(visualization_data)
library(ggplot2) library(see) library(poorman) visualization_data %>% ggplot(aes(x = Sepal.Width, y = Predicted)) + geom_ribbon(aes(ymin = CI_low, ymax = CI_high), alpha = 0.2) + geom_line() + see::theme_modern()
Note that non-linear relationships can be also described by linear approximations using describe_nonlinear.
glm(Sepal.Length ~ poly(Sepal.Width, 2), data = iris) %>% modelbased::estimate_relation(length = 50) %>% ggplot(aes(x = Sepal.Width, y = Predicted)) + geom_ribbon(aes(ymin = CI_low, ymax = CI_high), alpha = 0.2) + geom_line() + see::theme_modern()
library(mgcv) mgcv::gam(Sepal.Length ~ s(Sepal.Width), data = iris) %>% modelbased::estimate_relation(length = 50) %>% ggplot(aes(x = Sepal.Width, y = Predicted)) + geom_ribbon(aes(ymin = CI_low, ymax = CI_high), alpha = 0.2) + geom_line() + see::theme_modern()
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