simulate_prediction: Simulate a prediction design with continuous and categorical...

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Simulate a prediction design with continuous and categorical predictors

Description

Draws the continuous predictors independently from normal distributions and each categorical predictor by sampling its levels with replacement, then forms the outcome as the intercept plus the continuous linear predictor plus the level effects plus normal residual noise. With no categorical predictor the call is simply simulate_regression().

Usage

simulate_prediction(
  n,
  coefficients,
  categorical_levels = NULL,
  categorical_effects = NULL,
  category_probabilities = NULL,
  predictor_means = 0,
  predictor_sds = 1,
  error_sd = 1,
  outcome = "outcome",
  seed = NULL,
  batch = NULL
)

Arguments

n	Sample size. A single whole number of at least 2.
coefficients	Named numeric vector of continuous-predictor coefficients, with an optional (Intercept) entry that defaults to 0 when absent. The remaining names become the continuous predictor columns.
categorical_levels	Named list of levels for categorical predictors, or a tidy data frame with columns variable and level. Optional effect and probability columns in that table supply categorical_effects and category_probabilities, so one table replaces all three arguments. Defaults to NULL, no categorical predictor.
categorical_effects	Named list of level effects matching categorical_levels by name and length, one number per level, added to the outcome for the level a unit is assigned. Required whenever categorical_levels is given.
category_probabilities	Optional named list of sampling probabilities, one non-negative value per level summing to one within each predictor. Defaults to NULL, equal probabilities across a predictor's levels.
predictor_means, predictor_sds	Mean and standard deviation of each normally distributed continuous predictor, a scalar recycled across them (the defaults, 0 and 1) or one value per continuous coefficient.
error_sd	Residual standard deviation. A single positive number, defaulting to 1.
outcome	Outcome-column name. A single non-empty string, defaulting to "outcome".
seed	Optional random seed. A single number, or NULL (the default).
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.

Value

When categorical_levels is NULL, whatever simulate_regression() returns. Otherwise a simulab_sim base data.frame with one row per unit and columns id, one column per continuous coefficient name, one character column per categorical predictor, and the outcome named by outcome. Components: coefficients (one row per fixed term, columns term and coefficient, always including (Intercept)), categorical_effects (one row per predictor level, columns variable, level, effect and probability), and effects (one row, columns signal_variance, residual_variance and r_squared). signal_variance is the realized variance of the simulated linear predictor, so r_squared is the realized proportion of outcome variance it explains, not a population value.

Examples

result <- simulate_prediction(
  n = 200,
  coefficients = c("(Intercept)" = 0.5, x1 = 1, x2 = -1),
  seed = 1
)
head(result)
as.data.frame(result, what = "coefficients")

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