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Generate quasi Anscombe data sets Type 3: Extreme values (a.k.a Outliers)

Source: R/quasianscombe.R
sim_quasianscombe_set_3.Rd

Data sets Type 3 get some outliers but conserving the $x$ mean and the same coefficients -but different significance- of the adjusted linear model.

Usage

sim_quasianscombe_set_3(
  df,
  prop = 0.05,
  beta1_factor = 0.5,
  residual_factor = 0.25
)

Arguments

df

A data frame from sim_quasianscombe_set_1 (or similar).

prop

The proportion of value to modify as outliers.

beta1_factor

Numeric value to modify the beta1 value.

residual_factor

Numeric value to multiply residual to modify their variance.

Details

This function will:

  • Calculate the linear regression model and will calculate new trend using 0.5 times beta1

  • Take prop% values from the greater 2*prop x values and modify the related y value to get the original estimation of beta1

  • Apply residual_factor factor to residual to get minor variance and better visual impression of the outliers effect.

Examples


df <- sim_quasianscombe_set_1()

dataset3 <- sim_quasianscombe_set_3(df)

dataset3
#> # A tibble: 500 × 2
#>        x     y
#>    <dbl> <dbl>
#>  1  2.17  4.36
#>  2  2.26  4.70
#>  3  2.37  4.81
#>  4  2.39  4.86
#>  5  2.41  4.81
#>  6  2.46  4.62
#>  7  2.59  4.75
#>  8  2.66  4.78
#>  9  2.68  4.64
#> 10  2.83  4.67
#> # … with 490 more rows

plot(dataset3)


plot(sim_quasianscombe_set_3(df, prop = 0.1, residual_factor = 0))