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JimGrange/simulate_linear_model.r

Last active November 3, 2021 05:03
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simulate_linear_model.r
# sample size
n <- 500
# simulate data for each predictor
# (note predictor b is categorical)
predictor_a <- rnorm(n, 0, 1)
predictor_b <- rbinom(n, 1, .5)
# population-level beta values
b_predictor_a <- -0.20
b_predictor_b <- 0
b_int <- 0.20
# simulate outcome variable
y <- b_predictor_a * predictor_a + b_predictor_b * predictor_b +
(predictor_a * predictor_b * b_int) + rnorm(n, 0, 1)
# collate into data frame
data <- data.frame(
y = y,
predictor_a = predictor_a,
predictor_b = predictor_b
)
# fit linear model
summary(lm(y ~ predictor_a * predictor_b, data = data))
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