shanealman / regress431 Goto Github PK

In this lab, you will write functions to implement simple linear regression, multiple linear regression, and ridge regression.

Formulas for calculating coefficients of these regressions can be found in this week’s course materials.

This repository is structured like a package (regress431), but you do not have to do any package creation or management tasks. You only need to edit the body of the functions in the .R files to perform the correct calculations.

If you did not access this file from GitHub Classroom, clone the repository to your account to begin.

• You should not alter any of the function names, inputs, or outputs that I have provided for you. This is very important - we cannot test your work if you change these things!

• You should not alter the package name, for the same reasons.

• You should not alter the unit tests that are already written, for the same reasons. You may write more unit tests if this is helpful to you, but you are not required to.

• You may write helper functions beyond those provided. If you do, you do not have to carefully document them or test them; although you might find this helpful to your coding process.

• You may alter the code I provided for you, if you wish, so long as the function inputs do not change and outputs (where specified) do not change.

• You may not rely on any existing functions designed specifically for regression; including, but not limited to lm, lm.ridge, and predict. Instead, you should do the necessary matrix calculations directly from the data. (You may, however, use these functions to check or test the output of the ones you write.)

• You may rely on existing functions and external packages designed for faster general computation, such as data.table or furrr.
  If you use an external package, don’t forget to include it in your package dependencies by running e.g. usethis::use_package("data.table"), and to either use the :: in your code or add an @import line in the documentation.

In linear_regression.R, edit the unfinished section of the simple_linear_regression function to properly compute the slope and intercept of the regression line.

Load your new code (Ctrl-Shift-L) and then run the unit tests (Ctrl-Shift-T) to confirm that your function performs as expected.

In linear_regression.R, edit the body of the multiple_linear_regression function to properly compute all the required coefficients.

Note that this function assumes that you want to include every column of the supplied data frame in your regression, except the specified response.

Load your new code and then run the unit tests to confirm that your function performs as expected.

In predict_from_coefs.R, edit the predict_from_coefs function to calculate predicted values of the response variable.

Note that the input types have been chosen for you, but that the output format is up to you.

No unit test is written for you for this function, since the output format is not known. You are not required to write one, but you may find it useful.

In ridge_regression.R, edit the body of the multiple_linear_regression function to properly compute coefficients for a specified penalty value lambda, or for a set of lambdas.

Then edit the body of the find_best_lambda function to identify a best penalty value from a provided list.

Load and test your code as you go.

By Friday, you should have finished Tasks 1-3.

Open the file Showcase.Rmd and knit it. You should see the source code for all but your ridge_regression.R functions. This file will be used for a first round of Peer Review feedback.

By Sunday, you need to have Task 4 finished as well.

When you are finished, make sure all your changes are pushed to GitHub.

This week, there is no additional Challenge. Instead, your code will be tested for speed.

I will be loading your finished packages, and running something like the following:

library(regress431)

tic()

  simple_linear_regression(mtcars, mpg, cyl)

toc()

  multiple_linear_regression(mtcars, mpg)

toc()

 lambdas <- 0:10*.1

  rand <- sample(1:32, 16)

  train_dat <- mtcars %>%
    dplyr::slice(rand)

  test_dat <- mtcars %>%
    dplyr::slice(-rand)

  best_lambda <- find_best_lambda(mpg, lambda = lambdas)
  
toc()

  ridge_regression(mtcars, mpg, lambda = best_lambda)
  
toc()

However I will run this code on different datasets than this example, of varying sizes.

Bonus Points will be given for:

+5 for fastest multiple_linear_regression +5 for fastest find_best_lambda +5 for fastest ridge_regression

+10 for top 3 in overall total speed +5 for top 4-10 in overall total speed