Simulate heterogeneous pattern
Arguments
- x
ppp
- nsim
Number of patterns to simulate.
- fix_n
Logical if true the null model patterns have exactly the same number of points ais input.
- ...
Arguments passed to
spatstat.explore::density.ppp().
Details
Simulate heterogeneous point patterns as null model data for spatstat.explore::envelope().
A heterogeneous Poisson process is used, meaning that there are no interaction between points,
however, the simulated coordinates depend on the intensity \(\lambda\) of the input pattern.
Returns a list with ppp objects.
References
Baddeley, A., Rubak, E., Turner, R., 2015. Spatial point patterns: Methodology and applications with R. Chapman and Hall/CRC Press, London, UK. <isbn:978-1-4822-1020-0>
Wiegand, T., Moloney, K.A., 2014. Handbook of spatial point-pattern analysis in ecology. Chapman and Hall/CRC Press, Boca Raton, USA. <isbn:978-1-4200-8254-8>
Examples
set.seed(42)
input_pattern <- spatstat.random::rpoispp(lambda = function(x , y) {100 * exp(-3 * x)}, nsim = 1)
null_model <- rheteroppp(input_pattern, nsim = 19)
spatstat.explore::envelope(Y = input_pattern, fun = spatstat.explore::pcf, nsim = 19,
simulate = null_model)
#> Extracting 19 point patterns from list ...
#> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
#> 19.
#>
#> Done.
#> Pointwise critical envelopes for g(r)
#> and observed value for ‘input_pattern’
#> Edge correction: “iso”
#> Obtained from 19 point pattern datasets in user-supplied list
#> Alternative: two.sided
#> Significance level of pointwise Monte Carlo test: 2/20 = 0.1
#> ......................................................................
#> Math.label Description
#> r r distance argument r
#> obs hat(g)[obs](r) observed value of g(r) for data pattern
#> mmean bar(g)(r) sample mean of g(r) from simulations
#> lo hat(g)[lo](r) lower pointwise envelope of g(r) from simulations
#> hi hat(g)[hi](r) upper pointwise envelope of g(r) from simulations
#> ......................................................................
#> Default plot formula: .~r
#> where “.” stands for ‘obs’, ‘mmean’, ‘hi’, ‘lo’
#> Columns ‘lo’ and ‘hi’ will be plotted as shading (by default)
#> Recommended range of argument r: [0, 0.25]
#> Available range of argument r: [0, 0.25]