`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

`install.packages("sptotal")`

or using `devtools`

```
library(devtools)
install_git("https://github.com/highamm/sptotal.git")
```

The `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 response.

```
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 with `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

`browseVignettes("sptotal")`

and clicking `HTML`

.

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.