Simulate data with measurment error from a simple linear regression

Usage

sim_slr(
  n_sim = 50,
  min_x = 0,
  max_x = 2,
  alpha = 0,
  beta = 1,
  sigma = 0.1,
  x_err = 0.1,
  y_err = 0.1
)

Arguments

n_sim: Number of data points to simulate
min_x: Minimum x value
max_x: Maximum x value
alpha: Regression intercept
beta: Regression slope
sigma: Nugget variation
x_err: x measurement error
y_err: y measurement error

Value

Simulated dataset with columns x, x_err, y, y_err

Examples

sim_slr(n_sim = 50)
#> # A tibble: 50 × 6
#>          x x_err       y y_err true_y true_x
#>      <dbl> <dbl>   <dbl> <dbl>  <dbl>  <dbl>
#>  1 0.00801   0.1  0.122    0.1 0.0276 0.0276
#>  2 0.190     0.1 -0.0713   0.1 0.180  0.180 
#>  3 1.89      0.1  1.95     0.1 1.99   1.99  
#>  4 0.663     0.1  0.874    0.1 0.906  0.906 
#>  5 1.15      0.1  0.938    0.1 1.04   1.04  
#>  6 1.65      0.1  1.79     0.1 1.68   1.68  
#>  7 0.866     0.1  0.823    0.1 0.856  0.856 
#>  8 0.644     0.1  0.866    0.1 0.741  0.741 
#>  9 1.71      0.1  1.75     0.1 1.83   1.83  
#> 10 1.23      0.1  1.63     0.1 1.44   1.44  
#> # … with 40 more rows