sptotal implements finite population block kriging (Ver
Hoef (2008)), a geostatistical approach to predicting means and totals
of count data for finite populations.
See sptotal’s Website for more information.
sptotal can be installed from CRAN
sptotal package can be used for spatial prediction
in settings where there are a finite number of sites and some of these
sites were not sampled. Note that, to keep this example simple, we are
simulating response values that are spatially independent. In a real
example, we assume that there is some spatial dependence in the
set.seed(102910) spatial_coords <- expand.grid(1:10, 1:10) toy_df <- data.frame(xco = spatial_coords[ ,1], yco = spatial_coords[ ,2], counts = sample(c(rpois(50, 15), rep(NA, 50)), size = 100, replace = TRUE)) mod <- slmfit(formula = counts ~ 1, xcoordcol = "xco", ycoordcol = "yco", data = toy_df) summary(mod) pred <- predict(mod)
We can look at the predictions with
pred$Pred_df[1:6, c("xco", "yco", "counts", "counts_pred_count")]
sptotal Main Functions:
slmfit() fits a spatial linear model to the response on
the observed/sampled sites. can be used to construct an empirical
variogram of the residuals of the spatial linear model.
predict.slmfit() uses the spatial linear model fitted
slmfit() and finite population block kriging to
predict counts/densities at unobserved locations. A prediction for the
total count as well as a prediction variance are given by default.
For more details on how to use these functions, please see the Vignette by running
The methods in this package are based on the following reference:
Ver Hoef, Jay M. “Spatial methods for plot-based sampling of wildlife populations.” 15, no. 1 (2008): 3-13.
To cite this package in the literature, run the following line: