Converts adpcr object to the list of ppp.objects allowing spatial analysis.

adpcr2ppp(input, marks = TRUE, plot = FALSE)

Arguments

input
Object of the adpcr class containing data from one or more panels.
marks
If TRUE, marks values for non-empty partitions.
plot
If TRUE, array is plotted.

Value

A list containing objects with class ppp.object with the length equal to the number of arrays (minimum 1).

Details

Each array is independently converted by ppp function. marks attached to each point represent values contained by the adpcr object.

See also

ppp.object, ppp.

Examples

many_panels <- sim_adpcr(m = 400, n = 765, times = 1000, pos_sums = FALSE, n_panels = 5)
#> The assumed volume of partitions in each run is equal to 1.
#> The assumed volume uncertainty in each run is equal to 0.
# Convert all arrays to ppp objects adpcr2ppp(many_panels)
#> $Experiment1.1 #> Marked planar point pattern: 290 points #> marks are numeric, of storage type ‘double’ #> window: rectangle = [1, 45] x [1, 17] units #> #> $Experiment1.2 #> Marked planar point pattern: 317 points #> marks are numeric, of storage type ‘double’ #> window: rectangle = [1, 45] x [1, 17] units #> #> $Experiment1.3 #> Marked planar point pattern: 318 points #> marks are numeric, of storage type ‘double’ #> window: rectangle = [1, 45] x [1, 17] units #> #> $Experiment1.4 #> Marked planar point pattern: 299 points #> marks are numeric, of storage type ‘double’ #> window: rectangle = [1, 45] x [1, 17] units #> #> $Experiment1.5 #> Marked planar point pattern: 322 points #> marks are numeric, of storage type ‘double’ #> window: rectangle = [1, 45] x [1, 17] units #>
# Convert all arrays to ppp objects and get third plate third_plate <- adpcr2ppp(many_panels)[[3]] # Convert only third plate to ppp object third_plate2 <- adpcr2ppp(extract_run(many_panels, 3)) # Check the class of a new object class(third_plate2)
#> [1] "list"
# It's a list with the length 1. The third plate is a first element on this #list class(third_plate2[[1]])
#> [1] "ppp"