Model Assessment

Model Assessment

model(X, y)

Arguments

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 a vector with the following elements

RSS

Residual sum of squares.

MSE

Mean squared error.

RMSE

Root mean squared error.

R2

R-squared \(\left( R^2 \right)\).

Rbar2

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

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

Author

Ivan Jacob Agaloos Pesigan

Examples

# Simple regression------------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
X <- X[, c(1, ncol(X))]
y <- jeksterslabRdatarepo::wages.matrix[["y"]]
model(X = X, y = y)
#>          RSS          MSE         RMSE           R2        Rbar2 
#> 7.367313e+04 5.715526e+01 7.560110e+00 8.263864e-02 8.192585e-02 

# Multiple regression----------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
model(X = X, y = y)
#>          RSS          MSE         RMSE           R2        Rbar2 
#> 5.434254e+04 4.215868e+01 6.492972e+00 3.233388e-01 3.207018e-01