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

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

Calculates the residual maker matrix ≤ft( \mathbf{M} \right) using

\mathbf{M} = \mathbf{I} - \mathbf{P}

where

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

The residual maker matrix ≤ft( \mathbf{M} \right) transforms the \mathbf{y} vector to the vector of residuals ≤ft( \mathbf{e} = \mathbf{My} \right) .

M(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 the residual maker matrix ≤ft( \mathbf{M} \right) .

Ivan Jacob Agaloos Pesigan

Wikipedia: Projection Matrix

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

# Simple regression------------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
X <- X[, c(1, ncol(X))]
M <- M(X = X)
str(M, list.len = 6)

# Multiple regression----------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
M <- M(X = X)
str(M, list.len = 6)