Data

larc - Least Angle Regression Companion

Queensland University of Technology
Fitzpatrick, Benjamin
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/09/5964187f3b994&rft.title=larc - Least Angle Regression Companion&rft.identifier= https://doi.org/10.4225/09/5964187f3b994&rft.publisher=Queensland University of Technology&rft.description=This repository contains the data and code necessary to replicate the analysis described in the PLOS ONE article:by , David W. Lamb (DWL) and Kerrie Mengersen (KM). Code and repository authorship was the sole responsibility of Benjamin R. Fitzpatrick. The code file example_analysis.R illustrates how the functions included in this repository may be used to replicate the analysis described in the article. The article discusses the relevant theory and demonstrates the application of these methods to a geostatistical case study. This repository contains a set of functions written in the . The analysis this repository enables makes heavy use of the Least Angle Regression (LAR) algorithm for finding Least Absolute Shrinkage Selection Operator (LASSO) regularised solutions to multiple linear regression problems. An R package for conducting Least Absolute Shrinkage Selection Operator (LASSO) variable selection with the LAR algorithm already exists and is hosted on the Comprehensive R Archive Network under the name ''. This repository makes heavy use of functions from the 'lars' package. This repository contains functions that: randomly generate unique divisions of a sequence of numbers into two groups of user specified sizes (the intent being that these two groups of numbers are used as row indices to create training and validation sets from a full dataframe) use the LAR algorithm within a cross validation scheme in a manner that permits greater control of the particulars than is provided by the cv.lars( ) function from the 'lars' package use chord diagrams to visualise the covariate selection frequencies that result from conducting LAR within a cross validation scheme model average the predictions from the models selected for each of the training sets in the cross validation scheme interpolate a geostatistical response variable to a full cover predicted raster via such model averaged predictions. The functions provided here depend on the R packages: &rft.creator=Fitzpatrick, Benjamin &rft.date=2017&rft.edition=1&rft.relation=https://eprints.qut.edu.au/109005/ &rft.coverage=153.079103,-27.475784&rft_rights=© 2016 Benjamin R. Fitzpatrick, David W. Lamb and Kerrie Mengersen.&rft_rights=GNU General Public License (GPL) http://www.gnu.org/licenses/gpl.html&rft_subject=Machine learning&rft_subject=Carbon Sequestration&rft_subject=Interpolation&rft_subject=Algorithms&rft_subject=Physical geography&rft_subject=Linear regression analysis&rft_subject=Polynomials&rft_subject=Agricultural soil science&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
Gpl

GNU General Public License (GPL)
http://www.gnu.org/licenses/gpl.html

© 2016 Benjamin R. Fitzpatrick, David W. Lamb and Kerrie Mengersen.

Access:

Other view details

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program, in the form a text file titled 'LICENSE. If not, see http://www.gnu.org/licenses/.
Contact Information
b1.fitzpatrick@qut.edu.au

Mr Benjamin R. Fitzpatrick

Contact Information

Postal Address:
Mr Benjamin Fitzpatrick

b1.fitzpatrick@qut.edu.au

Full description

This repository contains the data and code necessary to replicate the analysis described in the PLOS ONE article:by , David W. Lamb (DWL) and Kerrie Mengersen (KM).

Code and repository authorship was the sole responsibility of Benjamin R. Fitzpatrick.

The code file example_analysis.R illustrates how the functions included in this repository may be used to replicate the analysis described in the article. The article discusses the relevant theory and demonstrates the application of these methods to a geostatistical case study. This repository contains a set of functions written in the . The analysis this repository enables makes heavy use of the Least Angle Regression (LAR) algorithm for finding Least Absolute Shrinkage Selection Operator (LASSO) regularised solutions to multiple linear regression problems. An R package for conducting Least Absolute Shrinkage Selection Operator (LASSO) variable selection with the LAR algorithm already exists and is hosted on the Comprehensive R Archive Network under the name ''. This repository makes heavy use of functions from the 'lars' package.

This repository contains functions that:

  • randomly generate unique divisions of a sequence of numbers into two groups of user specified sizes (the intent being that these two groups of numbers are used as row indices to create training and validation sets from a full dataframe)
  • use the LAR algorithm within a cross validation scheme in a manner that permits greater control of the particulars than is provided by the cv.lars( ) function from the 'lars' package
  • use chord diagrams to visualise the covariate selection frequencies that result from conducting LAR within a cross validation scheme
  • model average the predictions from the models selected for each of the training sets in the cross validation scheme
  • interpolate a geostatistical response variable to a full cover predicted raster via such model averaged predictions.

The functions provided here depend on the R packages:

Data time period: 2016 to 2016

This dataset is part of a larger collection

Click to explore relationships graph

153.0791,-27.47578

153.079103,-27.475784

Subjects

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers