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

Description Usage Arguments Value Author(s) See Also Examples

Calculates studentized residuals using

t_i = \frac{\hat{\varepsilon}_{i}}{\hat{σ}_{\varepsilon}^{2} √{1 - h_{ii}}}

1	tepsilonhat(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.

Returns studentized residuals.

Ivan Jacob Agaloos Pesigan

Other residuals functions: .My(), .tepsilonhat(), .yminusyhat(), My(), epsilonhat(), yminusyhat()

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

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