dot-R2fromESS: Coefficient of Determination R^2 (from ESS)

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

Description

Calculates the coefficient of determination using

R^2 = \frac{\textrm{Explained sum of squares}} {\textrm{Total sum of squares}} .

Usage

.R2fromESS(ESS = NULL, TSS = NULL, X, y)

Arguments

ESS	Numeric. Explained sum of squares.
TSS	Numeric. Total sum of squares.
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 ESS = NULL, ESS is computed using ESS() with X and y as required arguments. If TSS = NULL, TSS is computed using TSS() with y as a required argument. If ESS and TSS are provided, yhat, ybar, betahat, X, and y are not needed.

Value

Returns the coefficient of determination R^2 .

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(), .R2fromRSS(), .RMSE(), .Rbar2(), .model(), MSE(), R2(), RMSE(), Rbar2(), model()

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