scpcR¶

scpcR provides the SCPC inference stage of the SPUR workflow for cross-sectional regressions with spatial dependence. For the diagnostic and transformation stage in R, see spuR.

Installation¶

scpcR can be installed as standalone package with

# install.packages("remotes")
remotes::install_github("spatial-spur/scpcR@v0.1.3")

If you want to use scpcR through the full spuR workflow, install both tagged releases explicitly:

remotes::install_github("spatial-spur/scpcR@v0.1.3")
remotes::install_github("spatial-spur/spuR@v0.1.2")

Example: Chetty Dataset¶

In this example, we walk you through the SCPC inference workflow step-by-step. SCPC is the inference stage of the SPUR workflow, so we start from a fitted regression model. We also provide a one-stop pipeline wrapper if you want to run the full SPUR workflow in one step.

Data preparation¶

For illustration, we load the Chetty dataset from spuR. Of course, the analysis in principle follows the same logic on any other dataset. In this specific case, we first omit the non-contiguous US states. We also drop rows with missing values.

library(spuR)

data(spur_example)

df <- subset(
  spur_example,
  !state %in% c("AK", "HI"),
  select = c(am, gini, fracblack, lat, lon)
)

df <- stats::na.omit(df)

Fitting the regression¶

SCPC needs a fitted model, the data used to fit that model, and spatial coordinates supplied either as lon / lat or as Euclidean coordinates.

library(scpcR)

fit <- stats::lm(am ~ gini + fracblack, data = df)

Running SCPC inference¶

We suggest applying SCPC inference after estimating the regression:

out <- scpc(
  fit,
  data = df,
  lon = "lon",
  lat = "lat"
)

summary(out)

Interpreting the output¶

summary(out) prints the main SCPC inference table. You can also use coef(out) to extract coefficient estimates and confint(out) to extract confidence intervals.

If your coordinates are Euclidean rather than geographic, use coords_euclidean = c(...) instead of lon and lat.

Pipeline wrapper¶

As a shortcut to implementing the full SPUR workflow manually, use the spuR pipeline wrapper. It runs the diagnostics, transformation, and SCPC inference steps in one call.

result <- spuR::spur(
  am ~ gini + fracblack,
  data = df,
  lon = "lon",
  lat = "lat"
)

The nested SCPC results can be printed directly:

summary(result$fits$levels$scpc)
summary(result$fits$transformed$scpc)

Next Step¶

See Reference for the full public API, parameter meanings, and return objects.