dot-model: Model Assessment

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

Description

Model Assessment

Usage

.model(RSS = NULL, TSS = NULL, n, k, X, y)

Arguments

RSS	Numeric. Residual sum of squares.
TSS	Numeric. Total sum of squares.
n	Integer. Sample size.
k	Integer. Number of regressors including a regressor whose value is 1 for each observation.
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 RSS = NULL, RSS is computed using RSS() with X and y as required arguments. If RSS is provided, X, and y are not needed. If TSS = NULL, TSS is computed using TSS() with y as r equired argument. If TSS is provided, y is not needed.

Value

Returns a vector with the following elements

RSS

Residual sum of squares.

MSE

Mean squared error.

RMSE

Root mean squared error.

R2

R-squared ≤ft( R^2 \right).

Rbar2

Adjusted R-squared ≤ft( \bar{R}^2 \right) .

Author(s)

Ivan Jacob Agaloos Pesigan

References

Wikipedia: Residual Sum of Squares

Wikipedia: Explained Sum of Squares

Wikipedia: Total Sum of Squares

Wikipedia: Coefficient of Determination

See Also

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

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