Multilevel Modeling in R with ggplot2 Diagnostics

R remains a powerhouse for statistical modeling. This walkthrough fits a varying-intercept model with lme4, then layers diagnostic charts created with ggplot2 to verify assumptions.

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April 3, 2024
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Online at https://diogoribeiro7.github.io/analytics-blog-jekyll/2024/04/03/r-statistical-analysis-visualizations/

R remains a powerhouse for statistical modeling. This walkthrough fits a varying-intercept model with lme4, then layers diagnostic charts created with ggplot2 to verify assumptions.

Load packages and data

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library(tidyverse)
library(lme4)

metrics <- read_csv("data/store-conversion.csv")
metrics <- metrics |> mutate(period = as.Date(period))

Fit a multilevel model

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model <- lmer(conversion_rate ~ campaign_spend + (1 | region), data = metrics)
summary(model)

The random intercept term captures regional heterogeneity while partial pooling shrinks noisy estimates toward the grand mean.

Visualize partial pooling

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library(broom.mixed)
coefs <- broom.mixed::ranef(model, condVar = TRUE)$region |> tibble::rownames_to_column("region")

posterior <- coefs |> mutate(
  estimate = `(Intercept)`[, "condval"],
  se = sqrt(`(Intercept)`[, "condvar"])
)

posterior_plot <- posterior |> 
  ggplot(aes(x = estimate, y = fct_reorder(region, estimate))) +
  geom_point(color = "#1b9e77", size = 2.6) +
  geom_errorbarh(aes(xmin = estimate - 2 * se, xmax = estimate + 2 * se), height = 0.15) +
  labs(
    title = "Regional baseline conversion rates",
    x = "Log-odds",
    y = NULL
  ) +
  theme_minimal(base_size = 14)

ggplot2::ggsave("assets/images/posts/conversion-region-effects.png", posterior_plot, width = 8, height = 6, dpi = 144)

Partial pooling chart showing regional intercepts

Exporting figures directly from the R session ensures the rendered asset is tracked in version control. Pair the static PNG with an Observable embed for interactive exploration:

Share reproducibility artifacts

  • Commit the R script or Quarto document to _notebooks/.
  • Publish the model summary as a downloadable CSV in _datasets/.
  • Capture session info with sessionInfo() for audit trails.

The article mixes syntax highlighting, figure embeds, and Observable visualizations so readers can scrutinize both the statistical rigor and presentation quality.

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    © 2024 Diogo Ribeiro. Text and figures under CC BY 4.0.

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    Diogo Ribeiro (2024). Multilevel Modeling in R with ggplot2 Diagnostics. DataLog | Data Science & Research Theme. https://diogoribeiro7.github.io/analytics-blog-jekyll/2024/04/03/r-statistical-analysis-visualizations/.

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