linreg_r2: Linear Regression (R-square)
In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Description Usage Arguments Value Author(s)

Calculates the coefficient of determination (R^2 = 1 - \frac{\textrm{Residual sum of squares}}{\textrm{Total sum of squares}} or R^2 = \frac{\textrm{Explained sum of squares}}{\textrm{Total sum of squares}}).

1	linreg_r2(beta_hat = NULL, X, y, m = FALSE, rss = TRUE)

`beta_hat`	Vector of k estimated regression parameters. If `NULL`, regression coefficients are estimated using ≤ft( \mathbf{X}^{\prime} \mathbf{X} \right)^{-1} ≤ft( \mathbf{X}^{\prime} \mathbf{y} \right) .
`X`	The data matrix, that is an n \times k matrix of n observations of k regressors, which includes a regressor whose value is 1 for each observation.
`y`	n \times 1 vector of observations on the regressand variable.
`m`	Logical. If `TRUE`, the function uses an alternative formula e = \mathbf{M} \mathbf{y} . See `linreg_m` for \mathbf{M}.
`rss`	Logical. If `TRUE`, the function uses the residual sum of squares in the calculation. If `FALSE`, the function uses the estimated sum of squares in the calculation.