Model diagnostics

Last reviewed May 28, 2026 Content v20260528

Track mode: server_script
Means: Server runner
Reading: ~1 min
Level: advanced

This lesson

This lesson teaches Model diagnostics: the syntax, patterns, and safety habits you need before advancing in R.

Teams still ship Model diagnostics in R codebases—skipping it leaves gaps in debugging and code reviews.

You will apply Model diagnostics in contexts like: Research pipelines, Shiny dashboards, and statistical reporting.

Write R in the editor and click Run on server—the dev runner executes with Rscript; use print() or cat() and base R in playground snippets (tidyverse locally; LEARNING_RUNNER_ENABLED=true).

When pointers, structs, and basic control flow from intermediate lessons are familiar.

Validate regression assumptions with residuals, fitted vs observed plots, and influence measures—plot(fit) generates diagnostic panels locally.

Residuals

fit <- lm(y ~ x, data = df)
print(residuals(fit))
print(fitted(fit))

Diagnostic plots (local)

# plot(fit)  # four-panel diagnostics when run locally

Look for non-linearity, heteroscedasticity, and influential points—do not ship models without checks.

Important interview questions and answers

Q: What are residuals?
A: Observed minus fitted values—should look randomly scattered for linear models.
Q: plot(fit) panels?
A: Standard R diagnostic plots: residuals vs fitted, Q-Q, scale-location, leverage.

Self-check

How compute residuals from fit?
Why inspect Q-Q plot?

Tip: Run plot(fit) locally before trusting coefficients in a report.

Interview prep

Residual plots?: Check patterns—curves suggest non-linearity; funnel shapes suggest heteroscedasticity.

Interview tip Lesson completion confidence

Can you explain this lesson in 30 seconds without reading notes?

Self-reflection (saved on this device)

Not saved yet.

Playground

Runs on the configured server runner (dev: npm run runner with LEARNING_RUNNER_ENABLED=true). Output appears below the editor.

Code runner not available

Server runner is disabled. Set LEARNING_RUNNER_ENABLED=true and LEARNING_RUNNER_URL in .env (see .env.example).

Check yourself

Multiple choice — immediate feedback.

Discussion

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Residual plots?
confint use?

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