In EvanWie/regNselect:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(regNselect)

First we will examine the lin_model function of the regNselect package. This will be compared to the 'lm' function in R and the 'ols_regress' function of the olsrr package.

install.packages('olsrr')
library(olsrr)

We will start by generating random data:

x = rnorm(100)
x2 = rnorm(100)
x3 = rnorm(100)
y = rnorm(100)
df = data.frame(y, x, x2, x3)

We will now run each model technique with and compare the output of the three models. We expect all return values to be true.

options(digits = 6)
mod_regNselect = lin_model(y~x*x2+x3, data = df)
mod_lm = lm(y~x*x2+x3, data = df)
mod_olsrr = ols_regress(y~x*x2+x3, data = df)

# We will first compare the beta estimates
all.equal(mod_regNselect$coefficients, unname(mod_lm$coefficients))
all.equal(mod_regNselect$coefficients, unname(mod_olsrr$betas))

# We will also compare the standard errors of the betas and the test statistics output by olsrr
all.equal(mod_regNselect$St.Error, unname(mod_olsrr$std_errors))
all.equal(mod_regNselect$test_statistic, unname(mod_olsrr$tvalues))

EvanWie/regNselect documentation built on Nov. 20, 2019, 12:02 a.m.