In case you want to reconstruct several patterns at once (e.g. for different points in time if repeated censuses are available), you can use the following code.

Please note that the maximum number of iterations has been set to max_runs = 1000 and n_random = 3 to keep computational time low for this example. For real-world applications, it is advisable to raise these values. Additionally, we set verbose = FALSE in the vignette to minimize printed output. We recommend using the default setting verbose = TRUE when executing the code to view progress reports.

library(shar)
library(spatstat)
library(terra)

In case you want to only create the spatial characteristics, this is straightforward using lapply().

# create list with patterns
list_pattern <- list(species_a, species_b)

# reconstruct all patterns in list
result <- lapply(list_pattern, function(x) reconstruct_pattern(pattern = x, n_random = 3,
max_runs = 1000, verbose = FALSE))

The result will be a nested list including all m randomization (including the observed pattern) of the n provided input patterns.

# get mean energy
lapply(result, function(x) calculate_energy(pattern = x,
verbose = FALSE))
#> [[1]]
#> randomized_1 randomized_2 randomized_3
#>   0.02121578   0.01976184   0.02181316
#>
#> [[2]]
#> randomized_1 randomized_2 randomized_3
#>   0.03079873   0.04046341   0.03398510

Another possible would be to first reconstruct n times the spatial characteristics and afterwards reconstruct the marks m times for each of the n spatial reconstructions.

Firstly, reconstruct only the spatial characteristics n times. The observed pattern is not needed in this case, so you can put return_input = FALSE.

# reconstruct spatial strucutre
reconstructed_pattern <- reconstruct_pattern(species_a, n_random = 3,
max_runs = 1000, return_input = FALSE,
verbose = FALSE)

Secondly, to reconstruct the (numeric) marks of the observed pattern for each of the spatially reconstructed patterns, just use lapply() in combination with reconstruct_pattern_marks().

# get only selected marks of input (numeric marks)
species_a_marks <- subset(species_a, select = dbh)

# reconstruct marks 3 times for each input pattern
result_marks <- lapply(reconstructed_pattern$randomized, function(x) reconstruct_pattern_marks(pattern = x, marked_pattern = species_a_marks, max_runs = 1000, n_random = 3, verbose = FALSE)) Again, the result is a nested list with the same dimensions as provided input patterns and reconstructions. # get energy lapply(result_marks, function(x) calculate_energy(pattern = x, verbose = FALSE)) #>$randomized_1
#> randomized_1 randomized_2 randomized_3
#>  0.009258516  0.009975463  0.009858007
#>
#> $randomized_2 #> randomized_1 randomized_2 randomized_3 #> 0.009995799 0.009691552 0.008402482 #> #>$randomized_3
#> randomized_1 randomized_2 randomized_3
#>  0.009932602  0.009880717  0.009899017