dot-tepsilonhat: Studentized Residuals

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

Description

Calculates studentized residuals using

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

Usage

.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.

Details

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

Value

Returns studentized residuals.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Wikipedia: Leverage

See Also

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

jeksterslabds/jeksterslabRlinreg documentation built on Jan. 7, 2021, 8:30 a.m.