dot-My: Residuals <=ft( \boldsymbol{\hat{\varepsilon}} = \mathbf{My}...
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Details Value Author(s) References See Also

Calculates residuals using

\boldsymbol{\hat{\varepsilon}} = \mathbf{My} .

where

\mathbf{M} = \mathbf{I} - \mathbf{P} \\ = \mathbf{I} - \mathbf{X} ≤ft( \mathbf{X}^{T} \mathbf{X} \right)^{-1} \mathbf{X}^{T} .

1	.My(y, M = NULL, X = NULL, P = NULL)

`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.
`M`	`n` by `n` numeric matrix. The n \times n residual maker matrix ≤ft( \mathbf{M} \right).
`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.
`P`	`n` by `n` numeric matrix. The n \times n projection matrix ≤ft( \mathbf{P} \right).

If M = NULL, the M matrix is computed using M() with X as a required argument and P as an optional argument. If M is provided, X and P are not needed.

Returns an n \times 1 matrix of residuals ≤ft( \boldsymbol{\hat{\varepsilon}} \right), that is, the difference between the observed ≤ft( \mathbf{y} \right) and predicted ≤ft( \mathbf{\hat{y}} \right) values of the regressand variable ≤ft( \boldsymbol{\hat{\varepsilon}} = \mathbf{y} - \mathbf{\hat{y}} \right).

Ivan Jacob Agaloos Pesigan

Wikipedia: Errors and Residuals

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