README.md

In haisx/basiclm: Linear_model

basiclm

Motivation

Biostat 625 HW4

The goal of making this package is to get familiar with R package, and at the same time, trying to simplify the process of accessing the numeric key statistics in lm(). The linear_model() with detailed = TRUE can return a list of important statistics directly. In that way, we does not need to use summary() on lm() to show the results and then use complex indies to access the numeric values.

Installation

# If not have devtools installed, it is recommended
install.packages('devtools')

devtools::install_github("haisx/basiclm", build_vignettes = T)
library("basiclm")

There is only one function linear_model() in basiclm package.

Features

The basiclm package contains linear_model() which achieves the basic functionality of lm(). The function need to take the argument formula with all numeric variables and optional argument data, digit, and detailed. Without using detailed = TRUE, the function will return the function call and coefficients just like what lm() does. If we use a detailed = TRUE, we will get a list of numeric key values returned, which are the same values we can see in summary(lm()) and confint().

Tests

The linear_model() function is tested compared with lm() function so that we can prove that it output correct and stable results when handle the numeric data. Each output statistics are tested in the tests. You can find them at testthat directory under tests directory. The tests mainly use iris data as sample data and random generated data by runif() as other simulated data to prove the correctness of the functionality. In the Vignette, we also compared the performance and efficiency of linear_model() function with lm() function.

Examples

set.seed(222)
x = runif(500000, 1, 10000)
y = runif(500000, 1, 10000)
linear_model(y~x, detailed = TRUE)
linear_model(y~x + log(x), digit = 3, detailed = FALSE)

linear_model(iris$Petal.Length~iris$Sepal.Width)

linear_model(Petal.Length~Sepal.Width, data=iris, detailed = T)

linear_model(Petal.Length~Sepal.Width, data=iris, digit = 8, detailed = F)

To access the values, we can use $ directly like following,

linear_model(Petal.Length~Sepal.Width, data=iris, detailed = T)$Rsq

linear_model(Petal.Length~Sepal.Width, data=iris, detailed = T)$P

More specific examples can be viewed at browseVignettes("basiclm")

haisx/basiclm documentation built on Dec. 20, 2021, 2:45 p.m.