Function spatstat.geom::solist() returns an R object of S3 class 'ppplist', if all of the input objects are two-dimensional point-patterns (ppp.object, Chapter 36). The S3 class 'ppplist' inherits from the classes 'solist' (Chapter 39), 'anylist' (Chapter 15), 'listof' and 'list'. Listing 37.1 summarizes the S3 methods for the class 'ppplist' in the spatstat.* family of packages,
Listing 37.2 creates a point-pattern-list pppL_num that contains the point-patterns anemones (Section 10.1) and longleaf (Section 10.15), both with one numeric mark.
Listing 37.2: Data: a point-pattern-list with one numeric mark
Listing 37.4 creates a point-pattern-list pppL_mt that contains the point-patterns ants (Section 10.2) and hyytiala (Section 10.13), both with one multi-type mark.
Listing 37.4: Data: a point-pattern-list with one multi-type mark
The S3 method density_marks.ppplist() (Section 36.4, Table 36.4) applies the S3 method density_marks.ppp() (Section 36.4) to each point-pattern in the input point-pattern-list.
The S3 method kerndens.ppplist() (Section 33.1, Table 33.2) applies the S3 method kerndens.ppp() (Section 36.4) to each point-pattern in the input point-pattern-list.
Listing 37.9 and Listing 37.10 showcase the exception handling, as the input point-pattern-list btb.extra (Section 10.6) does not contain numeric mark.
Listing 37.9: Exception: function density_marks.ppplist(), no numeric marks
spatstat.data::btb.extra |>density_marks() |>lengths()# full standard # 0 0
Listing 37.10: Exception: function kerndens.ppplist(), no numeric marks
As explained in Section 36.4, Table 36.5, the S3 method density_marks.ppplist() is different from the S3 method spatstat.explore::density.ppplist(), which uses the \(x\)- and \(y\)-coords only thus provides an identical return with the marks removed from the input point-pattern-list (Listing 37.11).
aggregates and vectorizes the marks of each point-pattern in the input point-pattern-list using the S3 method aggregate_marks.ppp() (Section 36.7);
returns a numeric list or vectorlist (Chapter 41).
Listing 37.14 and Listing 37.15 aggregate the sample mean, or both the sample mean and the sample standard deviation sd, of the numeric mark in the point-pattern-list pppL_num (Listing 37.2) and return a numeric vectorlist (Chapter 41).
Listing 37.14: Example: function aggregate_marks.ppplist(), for sample mean (Listing 37.2)
Listing 37.16 aggregates the relative frequencies of the multi-type mark in the point-pattern-list pppL_mt (Listing 37.4). Note that Listing 37.16 returns a numeric list, but not a vectorlist (Chapter 41).
Listing 37.16: Example: function aggregate_marks.ppplist(), for relative frequencies (Listing 37.4)
Listing 37.17 creates a point-pattern-list by duplicating the point-pattern betacells (Section 10.4), aggregates the numeric mark area by the multi-type mark type using the sample mean and the sample standard deviation sd, and returns a numeric vectorlist (Chapter 41).
Listing 37.17: Example: function aggregate_marks.ppplist(), for sample mean and sd of area-by-type
spatstat.geom::solist( spatstat.data::betacells, spatstat.data::betacells) |>aggregate_marks(by = area ~ type, FUN = \(z) c(mean =mean(z), sd =sd(z)))# A 'vectorlist' of 2 vectors # Storage Mode: numeric # Individual Vector Length: 4
Listing 37.18 creates a point-pattern-list by duplicating the point-pattern gorillas (Section 10.11), aggregates one multi-type mark season by another multi-type mark group using the relative frequencies, and returns a numeric vectorlist (Chapter 41).
Listing 37.18: Example: function aggregate_marks.ppplist(), for relative frequencies of season-by-group
spatstat.geom::solist( spatstat.data::gorillas, spatstat.data::gorillas) |>aggregate_marks(by = season ~ group, FUN = \(z) table(z)/length(z))# A 'vectorlist' of 2 vectors # Storage Mode: numeric # Individual Vector Length: 4
Listing 37.19 shows that the S3 method t.vectorlist() (Section 41.3) is the fastest way to extract a “slice” from the returned numeric-vectorlist.
Listing 37.19: Advanced: function t.vectorlist()
spatstat.geom::solist( spatstat.data::gorillas, spatstat.data::gorillas) |>aggregate_marks(by = season ~ group, FUN = \(z) table(z)/length(z)) |>t.vectorlist()# A 'vectorlist' of 4 vectors # Name(s): major.season.dry, major.season.rainy, minor.season.dry, minor.season.rainy # Storage Mode: numeric # Individual Vector Length: 2
37.5Math Group-Generic of Numeric Mark(s)
The S3 method Math.ppplist()
applies the S3 method Math.ppp() (Section 36.3) to all point-patterns of the input point-pattern-list;
Listing 37.20 applies the log-transformation on the numeric marks of each point-pattern in the point-pattern-list pppL_num (Listing 37.2, Figure 37.1) and visualize the result in Figure 37.3.
Listing 37.20: Figure: log-transformed numeric marks in pppL_num (Listing 37.2)
pppL_num |>rlabelRes(fun = spatstat.explore::Kmark, f =`*`) |>global_envelope_test_()# anemones:# Global envelope test (1d):# * Based on the measure: "erl"# * 95% global envelope# * p-value of the global test: 0.01# * Significance level of the global test: 0.05# * Number of r with observed function outside the envelope: 424# * Total number of argument values r : 513# The object contains: # $r - Argument values : num [1:513] 0 0.0879 0.1758 0.2637 0.3516 ...# $obs - Observed function : num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $central - Central function : num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $lo - Lower boundary of the global envelope: num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $hi - Upper boundary of the global envelope: num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# # longleaf:# Global envelope test (1d):# * Based on the measure: "erl"# * 95% global envelope# * p-value of the global test: 0.01# * Significance level of the global test: 0.05# * Number of r with observed function outside the envelope: 508# * Total number of argument values r : 513# The object contains: # $r - Argument values : num [1:513] 0 0.0977 0.1953 0.293 0.3906 ...# $obs - Observed function : num [1:513] 0 0 0 -0.401 -0.783 ...# $central - Central function : num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $lo - Lower boundary of the global envelope: num [1:513] 0 0 0 -0.384 -0.801 ...# $hi - Upper boundary of the global envelope: num [1:513] 0 0 0 1.03 1.91 ...
Listing 37.25 performs the random re-labelling envelope residual and test on the point-pattern-list pppL_mt (Listing 37.4).
pppL_mt |>rlabelRes(fun = spatstat.explore::Gcross) |>global_envelope_test_()# ants:# Global envelope test (1d):# * Based on the measure: "erl"# * 95% global envelope# * p-value of the global test: 0.02# * Significance level of the global test: 0.05# * Number of r with observed function outside the envelope: 122# * Total number of argument values r : 513# The object contains: # $r - Argument values : num [1:513] 0 0.297 0.594 0.891 1.188 ...# $obs - Observed function : num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $central - Central function : num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $lo - Lower boundary of the global envelope: num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $hi - Upper boundary of the global envelope: num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# # hyytiala:# Global envelope test (1d):# * Based on the measure: "erl"# * 95% global envelope# * p-value of the global test: 0.75# * Significance level of the global test: 0.05# * Number of r with observed function outside the envelope: 0# * Total number of argument values r : 513# The object contains: # $r - Argument values : num [1:513] 0 0.0181 0.0363 0.0544 0.0725 ...# $obs - Observed function : num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $central - Central function : num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $lo - Lower boundary of the global envelope: num [1:513] 0 0 0 0 0 0 0 0 0 0 ...# $hi - Upper boundary of the global envelope: num [1:513] 0 0 0 0 0 0 0 0 0 0 ...
37.9 Batch Process on Eligible Marks
The S3 methods Emark_.ppplist(), Vmark_.ppplist(), etc., in Table 37.1,
collects the return of the corresponding S3 method in Table 36.20, for each point-pattern (ppp.object, Chapter 36) in the input point-pattern-list;
organizes these returns into a list-of-fvlist (Chapter 21) per numeric mark, e.g., Listing 37.26.
The S3 methods Gcross_.ppplist(), Kcross_.ppplist(), etc., in Table 37.1,
collects the return of the corresponding S3 method in Table 36.22, for each point-pattern (ppp.object, Chapter 36) in the input point-pattern-list;
organizes these returns into a list-of-fvlist (Chapter 21) per multi-type mark, e.g., Listing 37.27.
collects the return of the corresponding S3 method in Table 36.23, for each point-pattern (ppp.object, Chapter 36) in the input point-pattern-list;
organizes these returns into a list-of-anylist (Chapter 15) per multi-type mark, e.g., Listing 37.28.
The low-level utility function op_ppplist(), for batch operation on ppplist, is the underlying mechanism of the batch processes (Section 3.2). Function op_ppplist()
applies the operation to each point-pattern (ppp.object, Chapter 36) in the input point-pattern-list, and returns a two-level hierarchical list, the first level corresponds to the individual point-patterns, and the second level corresponds to the eligible numeric marks (for operations in Table 36.20) or eligible multi-type marks (foroperations in Table 36.22 and Table 36.23);
‘flips’ the hierarchy, such that the first level represents the eligible marks, and the second level corresponds to the individual point-patterns.
Listing 37.26: Example: function markcorr_.ppplist() (Listing 37.6)
betacells_type |>markcorr_()# $area# An 'fvlist' of 2 fv.objects k[mm](r) # Name(s): off, on # Available rmax: 187.5 # Minimum Legal rmax: 187.5
Listing 37.27: Example: function Gcross_.ppplist()
