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

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

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}.

h(X)

`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.

Returns leverage.

Ivan Jacob Agaloos Pesigan

Wikipedia: Leverage

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

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

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