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Simulate outcome

Source: R/simulate.R
simulate_response.Rd

Simulates outcome vector.

Usage

.simulate_response(family, x = NULL, beta = NULL, n = NULL, seed = 1L)

Arguments

family

character string "gaussian", "binomial", "poisson", or "cox"

x

predictors: numeric matrix with \(n\) rows (observations) and \(p\) columns (predictors)

beta

effects: numeric vector of length \(p\)

n

sample size: positive integer scalar or NULL (minimum 1, maximum \(100\,000\))

seed

random seed for reproducibility: integer scalar (unrestricted)

Value

Returns an \(n\)-dimensional response vector.

See also

This function is called by simulate_data().

Examples

# simulate independent response
.simulate_response(family = "gaussian", n = 10L)
#>  [1] -0.6264538  0.1836433 -0.8356286  1.5952808  0.3295078 -0.8204684
#>  [7]  0.4874291  0.7383247  0.5757814 -0.3053884

# simulate dependent response
set.seed(1L)
n <- 10L
p <- 20L
x <- matrix(rnorm(n * p), n, p)
beta <- rnorm(p)
.simulate_response(family = "gaussian", x = x, beta = beta)
#>  [1]   0.5803419   3.2072851  -4.3545695 -10.0067385   5.8595180   4.2391688
#>  [7]   0.4219104  -6.4163150  -1.3329548  -1.5614558