R2: Coefficient of Determination R^2
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Author(s) References See Also Examples

Calculates the coefficient of determination using

R^2 = 1 - \frac{\textrm{Residual sum of squares}} {\textrm{Total sum of squares}}

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

1	R2(X, y, fromRSS = TRUE)

`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.
`fromRSS`	Logical. If `TRUE`, calculates the coefficient of determination from `RSS`. If `FALSE`, calculates the coefficient of determination from `ESS`.

Ivan Jacob Agaloos Pesigan

Wikipedia: Residual Sum of Squares

Wikipedia: Explained Sum of Squares

Wikipedia: Total Sum of Squares

Wikipedia: Coefficient of Determination

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

# Simple regression------------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
X <- X[, c(1, ncol(X))]
y <- jeksterslabRdatarepo::wages.matrix[["y"]]
R2(X = X, y = y)

# Multiple regression----------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
R2(X = X, y = y)