`sptotal`

implements finite population block kriging (Ver Hoef (2008)), a geostatistical approach to predicting means and totals of count data for finite populations. `sptotal`

is currently under development.

## Installation

`sptotal`

can be installed using `devtools`

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

## Simple Example

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.

```{r, results = “hide”} 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) ## look at the predictions pred$Pred_df[1:6, c(“xco”, “yco”, “counts”, “counts_pred_count”)]

```
## Methods and Basic Functions
`sptotal` Main Functions:
`slmfit()` fits a spatial linear model to the response on the
observed/sampled sites. \code{check.variogram} 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.
`get.predinfo()` and `get.predplot()` take the resulting object from
`predict.slmfit()` to construct (1) summary information, including the
prediction, prediction variance, and a prediction interval as well as
(2) a plot of the site-wise predictions.
For more details on how to use these functions, please see the Vignette by running
```{r}
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.

## Citation

To cite this package in the literature, run the following line:

`citation("sptotal")`