Spread rate

Estimate the local spread rate of an epidemiological invasion.

sr(x, r = estimation_mask(x, buffer_size = max(st_distance(x))/10))

Arguments

x	A sr_obs object defined with sr_obs.
r	A raster template for results.

Details

This function will compute spread-rate estimates for all of the Monte Carlo samples in the dataset. It uses internally the function future_map which will take advantage of multiple processors or cluster access if you set a proper plan beforehand. See examples.

Examples

require(sf)
## Randomly sample 50 point in the unit square
x_mat <- matrix(runif(100), ncol = 2)

## Make it spatial point in a projected CRS
x_sfc <- st_cast(st_sfc(st_multipoint(x_mat), crs = 3857), "POINT")

## Assign dates proportional to the squared distance to the origin
## So that spread-rate decreases linearly
sro <- sr_obs(st_sf(x_sfc, date = sqrt(rowSums(x_mat**2))), "date")
sro <- sr_obs(st_sf(x_sfc, date = rowSums(x_mat**2)), "date",
uq = sr_uq(6, .2, .2, .3))

## Estimate local spread-rate
## in the units of the coordinates divided by the units of time
sre <- sr(sro)
#> Warning: 
#> Grid searches over lambda (nugget and sill variances) with  minima at the endpoints: 
#>   (GCV) Generalized Cross-Validation 
#>    minimum at  right endpoint  lambda  =  0.0006152913 (eff. df= 7.599955 )
#> Warning: 
#> Grid searches over lambda (nugget and sill variances) with  minima at the endpoints: 
#>   (GCV) Generalized Cross-Validation 
#>    minimum at  right endpoint  lambda  =  0.0003524432 (eff. df= 9.499992 )
#> Warning: 
#> Grid searches over lambda (nugget and sill variances) with  minima at the endpoints: 
#>   (GCV) Generalized Cross-Validation 
#>    minimum at  right endpoint  lambda  =  0.0002831076 (eff. df= 11.39999 )
#> Warning: 
#> Grid searches over lambda (nugget and sill variances) with  minima at the endpoints: 
#>   (GCV) Generalized Cross-Validation 
#>    minimum at  right endpoint  lambda  =  0.0004388292 (eff. df= 8.5498 )
#> Warning: 
#> Grid searches over lambda (nugget and sill variances) with  minima at the endpoints: 
#>   (GCV) Generalized Cross-Validation 
#>    minimum at  right endpoint  lambda  =  0.0003516891 (eff. df= 9.499995 )
plot(sre, col = hcl.colors(12))
points(st_coordinates(sro))