Description
Tools for Assessing Estimability of Linear Predictions.
Description
Provides tools for determining estimability of linear functions of regression coefficients, and 'epredict' methods that handle non-estimable cases correctly. Estimability theory is discussed in many linear-models textbooks including Chapter 3 of Monahan, JF (2008), "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4).
README.md
R package estimability: Support for determining estimability of linear functions
Features
- A
nonest.basis()function is provided that determines a basis for the null space of a matrix. This may be used in conjunction withis.estble()to determine the estimability (within a tolerance) of a given linear function of the regression coefficients in a linear model. - A set of
epredict()methods are provided forlm,glm, andmlmobjects. These work just likepredict(), except anNAis returned for any cases that are not estimable. This is a useful alternative to the generic warning that "predictions from rank-deficient models are unreliable." - A function
estble.subspace()that projects a set of linear functions onto an
estimable subspace (possibly of smaller dimension). This can be useful in creating a set of estimable contrasts for joint testing. - Package developers may wish to import this package and incorporate estimability checks for their
predictmethods.
Installation
- To install latest version from CRAN, run
install.packages("estimability")
Release notes for the latest CRAN version are found at https://cran.r-project.org/package=estimability/NEWS -- or do news(package = "estimability") for notes on the version you have installed.
- To install the latest development version from Github, have the newest devtools package installed, then run
devtools::install_github("rvlenth/estimability", dependencies = TRUE)
For latest release notes on this development version, see the NEWS file.