computeS: Compute Smoothing Matrix

In jpkeller/spconf: Computing Scales of Spatial Smoothing for Confounding Adjustment

Compute Smoothing Matrix

Description

Calculates the smoothing (or "hat") matrix from a design matrix.

Usage

computeS(x, inds = 1:nrow(x))

Arguments

x	Matrix of spline values, assumed to have full rank. A data frame is coerced into a matrix.
inds	Column indices of smoothing matrix to return (corresponding to rows in x).

Details

Given a matrix X of spline values, this computes S=X(X'X)^(-1)X'. When x has many rows, this can be quite large. The inds argument can be used to return a subset of columns from S.

Value

An N-by-n matrix, where n is the length of inds and N is the number of rows in x.

Examples

# Simple design matrix case
X <- cbind(1, rep(c(0, 1), each=4))
S <- computeS(X)
# More complex example
xloc <- runif(n=100, min=0, max=10)
X <- splines::ns(x=xloc, df=4, intercept=TRUE)
S <- computeS(X)
S2 <- computeS(X, inds=1:4)

jpkeller/spconf documentation built on March 11, 2024, 9:32 a.m.