MyNixOS website logo
Description

Predicting Totals and Weighted Sums from Spatial Data.

Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.

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.

Installation

sptotal can be installed from CRAN

install.packages("sptotal")

or 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.

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")]

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.

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." \emph{Environmental and Ecological Statistics} 15, no. 1 (2008): 3-13.

Citation

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

citation("sptotal")
Metadata

Version

1.0.1

License

Unknown

Platforms (75)

    Darwin
    FreeBSD 13
    Genode
    GHCJS
    Linux
    MMIXware
    NetBSD
    none
    OpenBSD
    Redox
    Solaris
    WASI
    Windows
Show all
  • aarch64-darwin
  • aarch64-genode
  • aarch64-linux
  • aarch64-netbsd
  • aarch64-none
  • aarch64_be-none
  • arm-none
  • armv5tel-linux
  • armv6l-linux
  • armv6l-netbsd
  • armv6l-none
  • armv7a-darwin
  • armv7a-linux
  • armv7a-netbsd
  • armv7l-linux
  • armv7l-netbsd
  • avr-none
  • i686-cygwin
  • i686-darwin
  • i686-freebsd13
  • i686-genode
  • i686-linux
  • i686-netbsd
  • i686-none
  • i686-openbsd
  • i686-windows
  • javascript-ghcjs
  • loongarch64-linux
  • m68k-linux
  • m68k-netbsd
  • m68k-none
  • microblaze-linux
  • microblaze-none
  • microblazeel-linux
  • microblazeel-none
  • mips-linux
  • mips-none
  • mips64-linux
  • mips64-none
  • mips64el-linux
  • mipsel-linux
  • mipsel-netbsd
  • mmix-mmixware
  • msp430-none
  • or1k-none
  • powerpc-netbsd
  • powerpc-none
  • powerpc64-linux
  • powerpc64le-linux
  • powerpcle-none
  • riscv32-linux
  • riscv32-netbsd
  • riscv32-none
  • riscv64-linux
  • riscv64-netbsd
  • riscv64-none
  • rx-none
  • s390-linux
  • s390-none
  • s390x-linux
  • s390x-none
  • vc4-none
  • wasm32-wasi
  • wasm64-wasi
  • x86_64-cygwin
  • x86_64-darwin
  • x86_64-freebsd13
  • x86_64-genode
  • x86_64-linux
  • x86_64-netbsd
  • x86_64-none
  • x86_64-openbsd
  • x86_64-redox
  • x86_64-solaris
  • x86_64-windows