dot-RMSE: Root Mean Squared Error (from \mathrm{RSS})

In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description

Calculates the root mean squared error ≤ft( \mathrm{RMSE} \right) using

\mathrm{RMSE} = √{\frac{1}{n} ∑_{i = 1}^{n} ≤ft( \mathbf{y} - \mathbf{X} \boldsymbol{\hat{β}} \right)^{2}} \\ = √{\frac{1}{n} ∑_{i = 1}^{n} ≤ft( \mathbf{y} - \mathbf{\hat{y}} \right)^{2}} \\ = √{\frac{\mathrm{RSS}}{n}} .

Usage

.RMSE(MSE = NULL, X, y)

Arguments

MSE	Numeric. Mean square error.
X	n by k numeric matrix. The data matrix \mathbf{X} (also known as design matrix, model matrix or regressor matrix) is an n \times k matrix of n observations of k regressors, which includes a regressor whose value is 1 for each observation on the first column.
y	Numeric vector of length n or n by 1 matrix. The vector \mathbf{y} is an n \times 1 vector of observations on the regressand variable.

Details

If MSE = NULL, MSE is computed using MSE() with X and y as required arguments. If MSE is provided, X, and y are not needed.

Value

Returns the root mean squared error.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Wikipedia: Root-mean-square deviation

See Also

Other assessment of model quality functions: .MSE(), .R2fromESS(), .R2fromRSS(), .Rbar2(), .model(), MSE(), R2(), RMSE(), Rbar2(), model()

jeksterslabds/jeksterslabRlinreg documentation built on Jan. 7, 2021, 8:30 a.m.