bootMSE: MSE for bootstrap samples of linear regression coefficients
In ndelrocco/lmBootCompare: Compares Six Methods of Bootstrapping Linear Model Coefficients

MSE of a bootstrap sample of simple linear regression coefficients

1	bootMSE(B, lmodObs, x, ncovs)

`B`	The number of bootstrap replicates. Usually this will be a single positive integer.
`lmodObs`	The observed linear model estimated by least squares. A fitted model object of class inheriting from 'lm'.
`x`	A B x (k+1) data frame containing B samples from the distributions of each of the (k+1) model parameters, where k is the number of predictors in the model.
`ncovs`	The number of covariates in the observed model. A positive integer.

This function calculates the mean square error for bootstrap samples of linear regression coefficients.

A vector of size (k+1) containing the mean square error of the bootstrap sample of each regression coefficient.

Natalie DelRocco

ndelrocco/lmBootCompare documentation built on Dec. 10, 2019, 12:38 p.m.

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