37 Statistical Topics
Table 37.1 summarizes the S3 generic functions, or “pseudo” S3 methods, for statistical functionalities implemented in these packages and/or covered in this Quarto book, and where to find them.
| Topic | S3 Generic Function, or “Pseudo” S3 Method |
Where to Find |
|---|---|---|
| A | ||
| aggregation | stats::aggregate() |
of vectorlist (Section 32.4), of hyperframe (Section 20.5) |
| aggregate marks-statistics | aggregate_marks() (Table 27.6) |
of ppp (Section 27.7), of ppplist (Section 28.3), of hyperframe (Section 20.6) |
| append to (existing) marks | `append_marks<-`() (Table 27.8) |
of ppp (Section 27.9) |
| B | ||
| batch process on eligible marks | Emark_(), Vmark_(), etc. (Table 27.17) |
of ppp (Section 27.14), of ppplist (Section 28.6), of hyperframe (Section 20.14) |
| F | ||
| function-value, recommended or others | keyval() (Table 15.3) |
of fv (Section 15.1), of fvlist (Section 16.3), of hyperframe (Section 20.10) |
| G | ||
| grouped hyper data frame, to create | as.groupedHyperframe() (Table 14.2) |
from data.frame (Section 14.1), from groupedData (Section 18.1), from hyperframe (Section 20.7) |
| global envelope test | 🚧 | of ppp (Section 27.13), of ppplist (🚧), of hyperframe (🚧) |
| I | ||
| interpolation | approxfun.*(), splinefun.*() |
of fv (Section 15.5) |
| K | ||
| kernel density (Becker, Chambers, and Wilks 1988) of numeric-marks | density_marks() (Table 27.3) |
of ppp (Section 27.4), of ppplist (Section 28.1) |
| kernel density estimates | kerndens() (Table 27.5) |
of numeric vector and of ppp (Section 27.4.1), of ppplist (Section 28.1.1), of anylist (Section 13.1), of hyperframe (Section 20.3) |
| \(k\)-means clustering (Hartigan and Wong 1979) | kmeans.*() |
of ppp (Section 27.11), of ppplist (Section 28.5), of hyperframe (Section 20.13) |
| M | ||
Math groupGeneric of numeric-marks |
Math.*() |
of ppp (Section 27.3) |
| Q | ||
| quantile | stats::quantile() |
of ppp (Section 27.5), of ppplist (Section 28.2), of anylist (Section 13.2), of hyperframe (Section 20.4) |
| R | ||
| \(r_\text{max}\), default | .rmax() (Table 27.9) |
of ppp (Section 27.10), of fv (Section 15.3), of ppplist (Section 28.4), of hyperframe (Section 20.9) |
| \(r_\text{max}\), legal | of fv (Section 15.4) |
|
\(r_\text{max}\), replace with theoretical values |
.illegal2theo() (Table 15.4), .disrecommend2theo() (Table 15.5) |
of fv (Section 15.4.1), of fvlist (Section 16.4), of hyperframe (Section 20.12) |
| S | ||
| smoothing | loess.*() |
of fv (Section 15.5) |
| split, by \(k\)-means clustering | base::split() (Table 27.13) |
of ppp (Section 27.11.2), of ppplist (Section 28.5.2), of hyperframe (Section 20.13.2) |
| split (default method) | base::split.default() |
on anylist (Section 13.3) |
| superimpose | spatstat.geom::superimpose() |
of hyperframe (Section 20.8) |
| T | ||
| Tjøstheim (1978)’s coefficient, pairwise | pairwise_cor_spatial() (Table 27.14) |
of ppp (Section 27.12), of ppplist (🚧) |
| trapezoidal integration | pracma::trapz(), pracma::cumtrapz() |
theory (Chapter 11) |
| trapezoidal integration, cumulative average vertical height | vtrapz(), cumvtrapz() (Table 11.1), visualize_vtrapz() (Table 11.2) |
theory and default method (Section 11.1), of fv (Section 15.2, Figure 11.6), of fvlist (Section 16.5), of hyperframe (Section 20.11) |