vcovhatbetahatbiased: Variance-Covariance Matrix of Estimates of Regression...
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Author(s) References See Also Examples

Variance-Covariance Matrix of Estimates of Regression Coefficients (from \hat{σ}_{\varepsilon \ \textrm{biased}}^{2})

1	vcovhatbetahatbiased(X, y)

`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.

Ivan Jacob Agaloos Pesigan

Wikipedia: Linear Regression

Wikipedia: Ordinary Least Squares

Other variance-covariance of estimates of regression coefficients functions: .vcovhatbetahatbiased(), .vcovhatbetahat(), vcovhatbetahat()

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

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