Generate data sets to apply regression methods

Generate data sets to apply regression methods

Usage

sim_xy(
  n = 500,
  beta0 = 3,
  beta1 = 0.5,
  x_dist = purrr::partial(rnorm, mean = 5, sd = 1),
  error_dist = purrr::partial(rnorm, sd = 0.5)
)

Arguments

n

Number of observations

beta0

beta0, default value: 3,

beta1

beta1, default value: 0.5

x_dist

A random number generation function. Default is a rnorm with mean 5 and sd 1.

error_dist

A random number generation function. Default is a rnorm with mean 0 and sd 0.5.

Examples

df <- sim_xy()

df
#> # A tibble: 500 × 2
#>        x     y
#>    <dbl> <dbl>
#>  1  2.45  4.75
#>  2  2.49  4.56
#>  3  2.62  4.53
#>  4  2.80  4.59
#>  5  2.83  5.06
#>  6  2.84  3.92
#>  7  2.89  3.89
#>  8  2.96  4.78
#>  9  2.98  4.43
#> 10  2.99  4.53
#> # … with 490 more rows

plot(df)


klassets:::plot.klassets_xy(setNames(cars, c("x", "y")))