README.md

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

spconf

R Package for Computing Scales of Spatial Smoothing for Confounding Adjustment

This package is designed to calculate the effective bandwidth of a spatial smoothing matrix, following the procedure described by Keller and Szpiro (2020).

Installation

The spconf package can be installed by running

devtools::install_github("kpkeller/spconf")

Computing Effective Range

The primary function is compute_effective_range(). The function takes a matrix of spline values X, with the assumption that the splines are nested to that adding additional columns increases the flexibility of forms that can be fit. For each choice of degrees of freedom, the function computes the effective range of the smoothing matrix.

M <- 16
tprs_df <- 8
si <- seq(0, 1, length=M+1)[-(M+1)]
gridcoords <- expand.grid(x=si, y=si)
tprsSC <- mgcv::smoothCon(mgcv::s(x, y, fx=TRUE, k=tprs_df + 1), data= gridcoords)
tprsX <- mgcv::PredictMat(tprsSC[[1]], data= gridcoords)
# Re-order the TPRS to put linear terms first
tprsX <- tprsX[, c(ncol(tprsX) + -1:0, 1:(ncol(tprsX)-2))]
colnames(tprsX) <- 1:ncol(tprsX)
compute_effective_range(X=tprsX,
                        coords=gridcoords,
                        newd=seq(0, 1, length=7),
                        df=3:8,
                        span=0.15)
#> Df =  3 
#> Df =  4 
#> Df =  5 
#> Df =  6 
#> Df =  7 
#> Df =  8
#>        3        4        5        6        7        8 
#> 3.886970 3.694037 3.543898 3.442408 3.346475 3.176590

References

Keller and Szpiro (2020). Selecting a scale for spatial confounding adjustment. Journal of the Royal Statistical Society, Series A https://doi.org/10.1111/rssa.12556.

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