57  Statistical Topics

Table 57.1 summarizes the S3 generic functions, or the “pseudo” S3 methods, for the statistical functionalities implemented in these packages and/or covered in this Quarto book, and where to find them.

Table 57.1: Index of Statistical Topics
Topic S3 Generic Function, or (Pseudo) S3 Method Where to Find
A
aggregation stats::aggregate() of vectorlist (Section 42.4), of hyperframe (Section 25.6)
aggregate marks-statistics aggregate_marks() (Table 35.9) of ppp (Section 35.7), of ppplist (Section 36.4), of hyperframe (Section 25.7)
append to (existing) marks `append_marks<-` (Table 35.11) of ppp (Section 35.9), of psp (Section 37.2), of tess (Section 41.2)
attribute per element attr_ (Table 14.3) of anylist (Section 14.4), of hyperframe (Section 25.10)
F
function-value, recommended, etc. keyval() (Table 19.3) of fv (Section 19.2), of fvlist (Section 20.4), of hyperframe (Section 25.14)
function-value, replace with theoretical values .illegal2theo() (Table 19.5), .disrecommend2theo() (Table 19.6) of fv (Section 19.5.1), of fvlist (Section 20.5), of hyperframe (Section 25.16)
function-value-tables from eligible marks, batch process Emark_(), Vmark_(), etc. (Table 35.21) of ppp (Section 35.14), of ppplist (Section 36.9), of hyperframe (Section 25.13)
G
global envelope test, batch process global_envelope_test_() (Table 14.2) of anylist (Section 14.3), of hyperframe (Section 25.12)
group-generic, Math methods::Math() (Table 35.2, Table 35.3, Table 36.2) of numeric marks in ppp (Section 35.3.1), psp (Section 37.1.1), ppplist (Section 36.5.1) and tess (Section 41.1.1), of fvlist (Section 20.7.1), of hyperframe (Section 55.3.1)
group-generic, Summary methods::Summary() (Table 35.4, Table 35.5, Table 36.3) of numeric marks in ppp (Section 35.3.2), psp (Section 37.1.2), ppplist (Section 36.5.2) and tess (Section 41.1.2), of fvlist (Section 20.7.2), of hyperframe (Section 55.3.2)
grouped hyper data frame, to create as.groupedHyperframe() (Table 17.2) from data.frame (Section 17.1), from groupedData (Section 23.1), from hyperframe (Section 25.8)
I
interpolation approxfun.*(), splinefun.*(), interpSpline_.fv() of fv (Section 19.6.1)
K
kernel density (Becker, Chambers, and Wilks 1988) estimates kerndens() (Table 32.2) of numeric vector (Section 32.1), of anylist (Section 14.1), of hyperframe (Section 25.4)
kernel density (Becker, Chambers, and Wilks 1988) of numeric marks density_marks() (Table 35.6) of ppp (Section 35.4), of ppplist (Section 36.2)
kernel density (Becker, Chambers, and Wilks 1988) estimates of numeric marks kerndens() (Table 32.2) of ppp (Section 35.4), of ppplist (Section 36.2), of hyperframe (Section 25.4)
\(k\)-means clustering (Hartigan and Wong 1979) kmeans.*() of ppp (Section 35.11), of ppplist (Section 36.7), of hyperframe (Section 25.17)
L
length base::length() of hyperframe (Section 25.3)
M
Q
quantile stats::quantile() (Table 35.8) of anylist (Section 14.2), of hyperframe (Section 25.5)
quantile of numeric marks stats::quantile() (Table 35.8) of ppp (Section 35.5), of ppplist (Section 36.3), of hyperframe (Section 25.5)
R
random re-labelling envelope residual rlabelRes() (Table 35.20) of ppp (Section 35.13), of ppplist (Section 36.8), of hyperframe (Section 25.12)
\(r_\text{max}\), default .rmax() (Table 35.13) of ppp (Section 35.10), of fv (Section 19.4), of ppplist (Section 36.6), of hyperframe (Section 25.11)
\(r_\text{max}\), legal of fv (Section 19.5)
S
smoothing loess.*(), ksmooth.*(), smooth.spline.*() of fv (Section 19.6.2)
split, by \(k\)-means clustering base::split() (Table 35.16) of ppp (Section 35.11.2), of ppplist (Section 36.7.1), of hyperframe (Section 25.17.2)
split (default method) base::split.default() on anylist (Section 14.5)
superimpose spatstat.geom::superimpose() of hyperframe (Section 25.9)
T
Tjøstheim (1978)’s coefficient, pairwise pairwise_cor_spatial() (Table 35.17) of ppp (Section 35.12), of ppplist (🚧)
trapezoidal integration, (cumulative) average vertical height vtrapz(), cumvtrapz() (Table 10.1) theory (Section 10.2), of numeric vector (Section 32.2), of fv (Section 19.3, Figure 10.9), of fvlist (Section 20.6), of hyperframe (Section 25.15)
trapezoidal integration, (cumulative) average vertical height, visualize visualize_vtrapz() (Table 10.2) gallery (Section 10.3), of numeric vector (Section 32.2), of function (Section 18.1), of fv (Section 19.3, Figure 10.9), of listof (Section 43.1)

Table 57.2 keeps track of the number of S3 generic functions, S3 methods and all other non-S3 functions in these package.

Listing 57.1: Table: Number of functions in these package
Code
c('groupedHyperframe', 'groupedHyperframe.random', 'hyper.gam', 'maxEff') |>
  lapply(FUN = \(pkg) {
    ns = pkg |> getNamespace()
    nm = ns |> 
      as.list.environment() |>
      names()
    id_generic = nm |>
      vapply(FUN = sloop::is_s3_generic, env = ns, FUN.VALUE = NA)
    id_method = nm |>
      vapply(FUN = sloop::is_s3_method, env = ns, FUN.VALUE = NA)
    return(list(
      'Package' = sprintf(fmt = '**`%s`** (v%s)', pkg, packageVersion(pkg)),
      '`S3` Generic Function' = sum(id_generic),
      '`S3` Method' = sum(id_method),
      'non-`S3` Function' = sum(!id_generic & !id_method)
    ))
  }) |>
  c(list(f = c)) |>
  do.call(what = Map, args = _) |>
  as.data.frame.list(check.names = FALSE) |>
  within.data.frame(expr = {
    Total = `\`S3\` Generic Function` + `\`S3\` Method` + `non-\`S3\` Function`
  }) |>
  knitr::kable(
    caption = 'Number of `S3` generic functions, `S3` methods and non-`S3` functions in *these package*'
  )
Table 57.2: Number of S3 generic functions, S3 methods and non-S3 functions in these package
Package S3 Generic Function S3 Method non-S3 Function Total
groupedHyperframe (v0.3.4) 23 107 45 175
groupedHyperframe.random (v0.2.2) 0 0 3 3
hyper.gam (v0.2.2) 2 7 7 16
maxEff (v0.2.2) 1 14 6 21