Package: cowbell 0.1.0
Christoph Luerig
cowbell: Performs Segmented Linear Regression on Two Independent Variables
Implements a specific form of segmented linear regression with two independent variables. The visualization of that function looks like a quarter segment of a cowbell giving the package its name. The package has been specifically constructed for the case where minimum and maximum value of the dependent and two independent variables are known a prior, which is usually the case when those values are derived from Likert scales.
Authors:
cowbell_0.1.0.tar.gz
cowbell_0.1.0.tar.gz(r-4.5-noble)cowbell_0.1.0.tar.gz(r-4.4-noble)
cowbell_0.1.0.tgz(r-4.4-emscripten)cowbell_0.1.0.tgz(r-4.3-emscripten)
cowbell.pdf |cowbell.html✨
cowbell/json (API)
| # Install 'cowbell' in R: |
| install.packages('cowbell', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 years agofrom:35f663f391. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 29 2024 |
| R-4.5-linux | NOTE | Oct 29 2024 |
Exports:generateCowbellgenerateCowbellConcept
Dependencies:base64encbslibcachemclicolorspacedigestevaluatefansifarverfastmapfontawesomefsggplot2gluegtablehighrhtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemisc3dmunsellnlmepillarpkgconfigR6rappdirsRColorBrewerrglrlangrmarkdownsassscalestibbletinytexutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| allFun: Data set for Fluency, Absorption, Fit and Fun. | allFun |
| Computes a response surface as segmented linear regression that resembles a cowbell. | cowbell-package cowbell |
| Implementation of the 'fitted' generic. | fitted.cowbell |
| Performs the segmented linear regression analysis generating the cowbell function. | generateCowbell |
| Expresses the fitting formula and the value range of the variables. | generateCowbellConcept |
| Plots the obtained cowbell function. | plot.cowbell |
| Performs a prediction on the cowbell model that has been generated. | predict.cowbell |
| Summarizes the cowbell regression analysis | print.cowbell |
| Summarizes the cowbell concept with the formula and value ranges. | print.cowbellConcept |
| Prints the summary obtained by 'summary.cowbell'. | print.summary.cowbell |
| Implementation of the 'residuals' generic. | residuals.cowbell |
| Generates the core information of the cowbell analysis. | summary.cowbell |
| testA: Artificial data set for testing. | testA |