dot-h: Leverage

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

Description

Calculates leverage, that is, how far away the regressor values of an observation are from those of the other observations using

h_{ii} = x_{i}^{T} ≤ft( \mathbf{X}^{T} \mathbf{X} \right)^{-1} x_{i}

where x_{i}^{T} is the ith row of the \mathbf{X} matrix. Note that \mathbf{X} ≤ft( \mathbf{X}^{T} \mathbf{X} \right)^{-1} \mathbf{X}^{T} is the projection matrix (or hat matrix) \mathbf{P} and h_{ii} is the diagonal of \mathbf{P}.

Usage

.h(P = NULL, X = NULL)

Arguments

P	Numeric matrix The projection matrix.
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.

Details

If P = NULL, P is computed with X as a required argument. X is ignored if P is provided.

Value

Returns leverage.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Wikipedia: Leverage

See Also

Other projection matrix functions: .M(), M(), P(), h()

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