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gettingtothebottom (version 3.2)

Learning Optimization and Machine Learning for Statistics

Description

Getting to the Bottom accompanies the "Getting to the Bottom" optimization methods series at Statisticsviews.com. It contains data and code to reproduce the examples in the articles.

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Install

install.packages('gettingtothebottom')

Monthly Downloads

61

Version

3.2

License

MIT + file LICENSE

Maintainer

Last Published

December 5th, 2014

Functions in gettingtothebottom (3.2)

Engel's Law - Engel Food Expenditures Data from the quantreg package for R

MM Algorithm - Normed Difference

Gradient Descent Algorithm - Plots Depicting Gradient Descent Results in Example 1 Using Different Choices for the Step Size

MM Algorithm - Initial lambda

MM Algorithm - Plotting the NNMLS regression coefficients

MM Algorithm - Softhreshold Function

MM Algorithm - Make Y

Gradient Descent Algorithm

Gradient Descent Algorithm - Plotting the Gradient Function

The Diet Problem: "Nutritive Values of Common Foods per Dollar of Expenditure, August 15, 1944", from George Stigler's 1945 paper on "The Cost of Subsistence"

Gradient Descent Algorithm - Plotting the Loss Function

MM Algorithm - Plot NNM

MM Algorithm - Matrix Completion

plot_nnm_reconstruction

MM Algorithm - Plotting the Reconstruction

MM Algorithm - Plotting the True Signal

MM Algorithm - Generate Test Matrix

Movie ratings and budget database derived from data from IMDB.com

example.quadratic.approx

Gradient Descent Algorithm - Plots Depicting How Different Choices of Alpha Result in Differing Quadratic Approximations

plot_solpaths_error

MM Algorithm - Function for plotting the imputed values against the truth for minimum error solution

plot_solutionpaths

MM Algorithm - Plot results of solutionpaths function

Nonnegative Least Squares via MM

Gradient Descent Algorithm - Plotting the Iterates

MM Algorithm - Plot NNM Objective

MM Algorithm - Find the best fit lambda for a given problem based on an initial guess for lambda

Movie ratings database derived from data from IMDB.com

MM Algorithm - Make Z

MM Algorithm - Plotting the Spectroscopic Signal

plot_softhreshold

MM Algorithm - Plot the Softhreshold Function

MM Algorithm - Function for making sequence of lambdas for solution paths

MM Algorithm - Generate Omega

Generate random nonnegative mixture components

gettingtothebottom

gettingtothebottom

The Diet Problem: "Daily Allowances of Nutrients for a Moderately Active Man (weighing 154 pounds)" from George Stigler's 1945 paper on "The Cost of Subsistence"

Baltimore Youth Indicators - 2010 and 2011