Simulate heterogenous pattern
Arguments
- x
ppp
- i
Mark of points that are not not changed.
- j
Mark of points that are randomized.
- nsim
Number of patterns to simulate.
- heterogenous
If TRUE, points with the mark j are randomized using a heterogeneous Poisson process.
- ...
Arguments passed to
spatstat.explore::density.ppp().
Details
Simulate point patterns as null model data for spatstat.explore::envelope() using
antecedent conditions as null model. x must be a marked point pattern with
two types of marks. Antecedent conditions are suitable as a null model if points
of type i may influence points of type j, but not the other way around (Velazquez et al 2016).
One example are the positions of seedlings that may be influenced by the position
of mature trees.
Returns a list with ppp objects.
References
Velázquez, E., Martínez, I., Getzin, S., Moloney, K.A., Wiegand, T., 2016. An evaluation of the state of spatial point pattern analysis in ecology. Ecography 39, 1–14. <https://doi.org/10.1111/ecog.01579>
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)
pattern_a <- spatstat.random::runifpoint(n = 20)
spatstat.geom::marks(pattern_a) <- "a"
pattern_b <- spatstat.random::runifpoint(n = 100)
spatstat.geom::marks(pattern_b) <- "b"
pattern <- spatstat.geom::superimpose(pattern_a, pattern_b)
null_model <- simulate_antecedent(x = pattern, i = "a", j = "b", nsim = 19)
spatstat.explore::envelope(Y = 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 ‘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]