# sv: Semi-variogram computation In sptotal: Predicting Totals and Weighted Sums from Spatial Data

## Description

Compute the empirical semivariogram for a data set with specified x and y spatial coordinates and residuals.

## Usage

 `1` ```sv(data, xcoordcol, ycoordcol, residcol, bins = 15, cutoff = NULL, ...) ```

## Arguments

 `data` A data frame or tibble that contains variables for the x coordinates, y coordinates, and residuals. `xcoordcol` is the name of the column in the data frame with x coordinates or longitudinal coordinates `ycoordcol` is the name of the column in the data frame with y coordinates or latitudinal coordinates `residcol` is the name of the column in the data frame with residuals. `bins` Number of equally spaced semivariogram bins. The default is 15. `cutoff` The maximum spatial distance to be considered. The default is half of the maximum observed distance in the data frame. `...` Additional arguments to be used by `stats::dist()` for spatial distance calculations.

## Value

A data frame with the average distance in each bin (dist), the semivariance (gamma), and the number of unique pairs in each bin (np)

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```data(exampledataset) ## load a toy data set slmobj <- slmfit(formula = counts ~ pred1 + pred2, data = exampledataset, xcoordcol = 'xcoords', ycoordcol = 'ycoords', areacol = 'areavar') sampleddataset <- na.omit(exampledataset) svexample <- data.frame( xcoords = sampleddataset\$xcoords, ycoords = sampleddataset\$ycoords, resids = residuals(slmobj) ) svdata <- sv(svexample, "xcoords", "ycoords", "resids") ```

sptotal documentation built on July 6, 2021, 5:07 p.m.