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 two different procedures, described by Keller and Szpiro (2020) and Rainey and Keller (2024). This package also contains a wrapper function to create a thin-plate regression spline basis using the mgcv package (Wood, 2011).

Installation

The spconf package can be installed by running

remotes::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. Using smoothedCurve = TRUE, the effective range is computed using the procedure introduced by Keller and Szpiro (2020) and requires the span input; and using smoothedCurve = FALSE, the effective range is computed using the procedure introduced by Rainey and Keller (2024) which no longer requires the span input.

# Example using metric of Rainey and Keller (2024)
M <- 16
tprs_df <- 10
si <- seq(0, 1, length=M+1)[-(M+1)]
gridcoords <- expand.grid(x=si, y=si)
tprsX <- computeTPRS(coords = gridcoords, maxdf = tprs_df+1)
compute_effective_range(X=tprsX, coords=gridcoords, namestem = "tprs",
                        df=3:10, smoothedCurve = FALSE)
#>         3         4         5         6         7         8         9        10 
#> 0.3801727 0.3365728 0.3125000 0.3125000 0.3125000 0.3125000 0.3125000 0.3186887

# Example using metric of Keller and Szpiro (2020)
xloc <- runif(n=100, min=0, max=10)
X <- splines::ns(x=xloc, df=4, intercept=TRUE)
colnames(X) <- paste0("s", 1:ncol(X))
xplot <- 0:10
compute_effective_range(X=X, coords=as.matrix(xloc), df=2:4, 
                        smoothedCurve = TRUE, newd=xplot, namestem="s")
#>        2        3        4 
#> 4.512474 4.472711 4.215862

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.

Rainey and Keller (2024). spconfShiny: An R Shiny application for calculating the spatial scale of smoothing splines for point data. PLOS ONE https://doi.org/10.1371/journal.pone.0311440

Wood (2011). Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models. Journal of the Royal Statistical Society Series B: Statistical Methodology https://doi.org/10.1111/j.1467-9868.2010.00749.x