sim_quasianscombe_set_1: Generate _quasi_ Anscombe data sets Type 1

In jbkunst/klassets: Tools to simulate data set to teach Statistical Models and ML Algorithms

Generate quasi Anscombe data sets Type 1

Description

This function generate a data set Type 1 creating first a x a random vector then apply a linear transformation using beta0 and beta1 and finally adding a normal distributed noise using error_sd creating y values.

Usage

sim_quasianscombe_set_1(
  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.

Details

This is the typical first example when regression analysis is taught.

Internally this is the same procedure of sim_xy.

Examples

df <- sim_quasianscombe_set_1()

df

plot(df)

plot(df, add_lm = FALSE)

plot(sim_quasianscombe_set_1(n = 1000))

plot(sim_quasianscombe_set_1(n = 1000, beta0 = 0, beta1 = 1, x_dist = runif))

jbkunst/klassets documentation built on Dec. 7, 2022, 9:18 p.m.