simulate_regression: Simulate a linear regression design

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Simulate a linear regression design

Description

Simulate a linear regression design

Usage

simulate_regression(
  n = 100L,
  coefficients = NULL,
  predictor_means = 0,
  predictor_sds = 1,
  correlation = NULL,
  error_sd = 1,
  outcome = "outcome",
  seed = NULL,
  batch = NULL,
  n_predictors = 2L
)

Arguments

n	Sample size, defaulting to 100.
coefficients	Named coefficients including optional (Intercept). NULL (the default) draws an intercept from the standard normal distribution and n_predictors slopes, named x1, x2, ..., from the uniform distribution on [-1, 1], each rounded to two decimals, inside the same seeded draw as the data. The drawn values are reported in the coefficients table.
predictor_means, predictor_sds	Predictor means and standard deviations.
correlation	Optional predictor correlation matrix or tidy table.
error_sd	Positive residual standard deviation.
outcome	Name of the outcome variable.
seed	Optional random seed.
batch	Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.
n_predictors	Number of predictors when coefficients is NULL, a single positive whole number defaulting to 2. Ignored otherwise.

Value

A simulab_sim base data.frame with one row per observation and columns id, one column per predictor, and outcome (renamed by outcome). Three tidy tables come from as.data.frame(): what = "coefficients" has one row per term with columns term and coefficient, the intercept first; what = "effects" has a single row with columns signal_variance (the quadratic form of the slopes in the predictor covariance matrix), residual_variance (error_sd^2) and the population r_squared signal_variance / (signal_variance + residual_variance); what = "predictor_correlation" has one row per correlation-matrix cell with columns row, column and correlation.

Examples

# `coefficients` must be named; "(Intercept)" is optional.
result <- simulate_regression(
  n = 200,
  coefficients = c("(Intercept)" = 1, x1 = 0.5, x2 = -0.3),
  seed = 1
)
head(result)
as.data.frame(result, what = "coefficients")
as.data.frame(result, what = "effects")

# With no coefficients, they are drawn and reported as the truth.
drawn <- simulate_regression(seed = 1)
as.data.frame(drawn, what = "coefficients")

simulab documentation built on Oct. 15, 2026, 5:08 p.m.