Sampling is split into two steps, for predictors and for response variables, to allow users to choose which to simulate. sample_x() will only sample predictor variables, and sample_y() will augment a data frame of predictors with columns for response variables, overwriting any already present. Hence one can use sample_y() as part of a simulation with fixed predictors, for instance.

## Usage

sample_x(population, n)

sample_y(xs)

## Arguments

population

Population, as defined by population().

n

Number of observations to draw from the population.

xs

Data frame of predictor values drawn from the population, as obtained from sample_x().

## Value

Data frame (tibble) of n rows, with columns matching the variables specified in the population.

## Examples

# A population with a simple linear relationship
pop <- population(
x1 = predictor("rnorm", mean = 4, sd = 10),
x2 = predictor("runif", min = 0, max = 10),
y = response(0.7 + 2.2 * x1 - 0.2 * x2, error_scale = 1.0)
)

xs <- pop |>
sample_x(5)

xs
#> Sample of 5 observations from
#> Population with variables:
#> x1: rnorm(list(mean = 4, sd = 10))
#> x2: runif(list(min = 0, max = 10))
#> y: gaussian(0.7 + 2.2 * x1 - 0.2 * x2, error_scale = 1)
#>
#> # A tibble: 5 × 2
#>       x1    x2
#> *  <dbl> <dbl>
#> 1 16.1   8.25
#> 2 -0.732 3.56
#> 3 17.8   4.68
#> 4  0.160 1.87
#> 5 14.5   0.793

xs |>
sample_y()
#> Sample of 5 observations from
#> Population with variables:
#> x1: rnorm(list(mean = 4, sd = 10))
#> x2: runif(list(min = 0, max = 10))
#> y: gaussian(0.7 + 2.2 * x1 - 0.2 * x2, error_scale = 1)
#>
#> # A tibble: 5 × 3
#>       x1    x2      y
#>    <dbl> <dbl>  <dbl>
#> 1 16.1   8.25  35.1
#> 2 -0.732 3.56  -1.75
#> 3 17.8   4.68  37.9
#> 4  0.160 1.87   0.401
#> 5 14.5   0.793 33.6