dot-MSE: Mean Squared Error (from \mathrm{RSS})
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Details Value Author(s) References See Also

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

\mathrm{MSE} = \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} .

1	.MSE(RSS = NULL, n, X, y)

`RSS`	Numeric. Residual sum of squares.
`n`	Integer. Sample size.
`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.

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

Returns the mean squared error.

Ivan Jacob Agaloos Pesigan

Wikipedia: Mean squared error

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