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.