moose: Mean Squared Out-of-Sample Error Projection

Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>. It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.

Version: 0.0.1
Published: 2022-09-09
Author: Chris Rohlfs ORCID iD [aut, cre]
Maintainer: Chris Rohlfs <car2228 at>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: moose results


Reference manual: moose.pdf


Package source: moose_0.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): moose_0.0.1.tgz, r-oldrel (arm64): moose_0.0.1.tgz, r-release (x86_64): moose_0.0.1.tgz, r-oldrel (x86_64): moose_0.0.1.tgz


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