Studentized Residuals

Calculates studentized residuals using $$ t_i = \frac{\hat{\varepsilon}_{i}}{\hat{\sigma}_{\varepsilon}^{2} \sqrt{1 - h_{ii}}} $$

.tepsilonhat(
  epsilonhat = NULL,
  h = NULL,
  sigma2hatepsilonhat = NULL,
  X = NULL,
  y = NULL
)

Arguments

epsilonhat	Numeric vector of length `n` or `n` by 1 numeric matrix. $n \times 1$ vector of residuals.
h	Numeric vector of length `n` or `n` by 1 numeric matrix. $n \times 1$ vector of leverage values.
sigma2hatepsilonhat	Numeric. Estimate of error variance.
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.

If epsilonhat, h, or sigma2hatepsilonhat are NULL, they are calculated using X and y.

Ivan Jacob Agaloos Pesigan

epsilonhat	Numeric vector of length `n` or `n` by 1 numeric matrix. \(n \times 1\) vector of residuals.
h	Numeric vector of length `n` or `n` by 1 numeric matrix. \(n \times 1\) vector of leverage values.
sigma2hatepsilonhat	Numeric. Estimate of error variance.
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.