Mean Squared Error (from \(\mathrm{RSS}\))

Source: R/MSE.R

Calculates the mean squared error \(\left( \mathrm{MSE} \right)\) using $$ \mathrm{MSE} = \frac{1}{n} \sum_{i = 1}^{n} \left( \mathbf{y} - \mathbf{X} \boldsymbol{\hat{\beta}} \right)^{2} \\ = \frac{1}{n} \sum_{i = 1}^{n} \left( \mathbf{y} - \mathbf{\hat{y}} \right)^{2} \\ = \frac{\mathrm{RSS}}{n} . $$

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

Arguments

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.

Value

Returns the mean squared error.

Details

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.

References

Wikipedia: Mean squared error

See also

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

Author

Ivan Jacob Agaloos Pesigan