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moose
View on CRAN: Click
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Download and install moose package within the R console
Install from CRAN:
install.packages("moose")
Install from Github:
library("remotes")
install_github("cran/moose") Install by package version:
library("remotes")
install_version("moose", "0.0.1") Attach the package and use:
library("moose")
Maintained by
Chris Rohlfs
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First Published: 2022-09-09
Latest Update: 2022-09-09
Description:
Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) . 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.
How to cite:
Chris Rohlfs (2022). moose: Mean Squared Out-of-Sample Error Projection. R package version 0.0.1, https://cran.r-project.org/web/packages/moose. Accessed 06 Oct. 2026.
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Some associated R codes: moose.R . Full moose package functions and examples
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