Draw a data frame from the specified population.

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 -5.62   2.78 
#> 2  4.52   3.64 
#> 3 13.0    0.699
#> 4  0.0510 5.89 
#> 5 13.5    0.841

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 -5.62   2.78  -12.8  
#> 2  4.52   3.64   11.3  
#> 3 13.0    0.699  30.9  
#> 4  0.0510 5.89   -0.540
#> 5 13.5    0.841  31.3