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R Linear Models

Fit a simple numeric model and inspect its coefficients and predictions.

How it works

lm fits a linear model described by a formula. The response appears on the left and predictors on the right. Intercept and slope describe the fitted association in the chosen units, not a causal effect. This exact artificial line keeps the mechanics inspectable; later examples use non-zero residuals and held-out evaluation.

Run this R example

These examples run locally with R, not PHP, Python or JavaScript. The reviewed baseline is R 4.5.2 on Windows x64/UCRT, using only packages shipped with R. This is a tested version, not a newest-release claim. Install your own R from the official R project; an IDE is optional. Save the complete source as linear-models.R in a separate learning folder and run Rscript --vanilla linear-models.R. Keep code and fictional fixtures separate from a real project.

Bhylu has no R browser runner. There is no Try in playground link because the existing Python and JavaScript editors do not execute R. No package installation, account, network dataset, Apache, MySQL or PHP setting is needed to run these base-R sources.

The complete download uses a local scope, restores its temporary options and treats unexpected warnings as errors. Examples that handle an expected warning do so narrowly. Random examples change only their R process’s RNG state. File examples own their temporary output; figure examples leave their named PNG in the current learning folder and refuse to overwrite it. Use a new empty folder to rerun a figure.

Download the complete linear-models.R · Download all source files with hash checks

For the optional collection downloader, save download-examples.mjs in an empty folder and run node download-examples.mjs. Local preview before publishing uses node download-examples.mjs http://127.0.0.1:8000/examples/r/. It refuses an existing bhylu-r-examples folder and does not install or execute R.

Example

linear-models.R

local({
  previous_options <- options(warn = 2, digits = 8, stringsAsFactors = FALSE)
  on.exit(options(previous_options), add = TRUE)
  sessions <- data.frame(visits = 1:5, minutes = c(7, 9, 11, 13, 15))
  fit <- lm(minutes ~ visits, data = sessions)
  cat("coefficients:", paste(round(unname(coef(fit)), 3), collapse = ", "), "\n")
  predicted <- predict(fit, newdata = data.frame(visits = c(2.5, 4.5)))
  cat("predictions:", paste(round(unname(predicted), 3), collapse = ", "), "\n")
  invisible(NULL)
})
Focus on the lesson operations

This excerpt omits the complete script’s option/resource setup. Use the full download to execute it.

Operation excerpt

sessions <- data.frame(visits = 1:5, minutes = c(7, 9, 11, 13, 15))
fit <- lm(minutes ~ visits, data = sessions)
cat("coefficients:", paste(round(unname(coef(fit)), 3), collapse = ", "), "\n")
predicted <- predict(fit, newdata = data.frame(visits = c(2.5, 4.5)))
cat("predictions:", paste(round(unname(predicted), 3), collapse = ", "), "\n")

Expected result

coefficients: 5, 2
predictions: 10, 14

The intercept is five and the slope is two in this constructed relation. The new observations lie inside the training input range. A correct fit to an exact line verifies the example, not real-world forecasting quality or the assumptions needed for statistical inference.

For API behaviour, consult the official Introduction to R and your installed help pages. These small examples do not establish real-world statistical conclusions or production performance.

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