nested_hclust: Nested (Constrained) hierarchical clustering

In paleolimbot/tidypaleo: Tidy Tools for Paleoenvironmental Archives

Nested (Constrained) hierarchical clustering

Description

Powered by chclust and hclust; broken stick using bstick.

Usage

nested_hclust(
  .data,
  data_column = "data",
  qualifiers_column = "qualifiers",
  distance_fun = stats::dist,
  n_groups = NULL,
  ...,
  .fun = stats::hclust,
  .reserved_names = character(0)
)

nested_chclust_conslink(
  .data,
  data_column = "data",
  qualifiers_column = "qualifiers",
  distance_fun = stats::dist,
  n_groups = NULL,
  ...
)

nested_chclust_coniss(
  .data,
  data_column = "data",
  qualifiers_column = "qualifiers",
  distance_fun = stats::dist,
  n_groups = NULL,
  ...
)

Arguments

.data	A data frame with a list column of data frames, possibly created using nested_data.
data_column	An expression that evalulates to the data object within each row of .data
qualifiers_column	The column that contains the qualifiers
distance_fun	A distance function like dist or vegdist.
n_groups	The number of groups to use (can be a vector or expression using vars in .data)
...	Passed to chclust or hclust.
.fun	Function powering the clustering. Must return an hclust object of some kind.
.reserved_names	Names that should not be allowed as columns in any data frame within this object

Value

.data with additional columns

References

Bennett, K. (1996) Determination of the number of zones in a biostratigraphic sequence. New Phytologist, 132, 155-170. doi: 10.1111/j.1469-8137.1996.tb04521.x (Broken stick)

Grimm, E.C. (1987) CONISS: A FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares. Computers & Geosciences, 13, 13-35. doi: 10.1016/0098-3004(87)90022-7

Juggins, S. (2017) rioja: Analysis of Quaternary Science Data, R package version (0.9-15.1). (https://cran.r-project.org/package=rioja).

See hclust for hierarchical clustering references

Examples

library(tidyr)
library(dplyr, warn.conflicts = FALSE)

nested_coniss <- keji_lakes_plottable %>%
  group_by(location) %>%
  nested_data(depth, taxon, rel_abund, fill = 0) %>%
  nested_chclust_coniss()

# plot the dendrograms using base graphics
plot(nested_coniss, main = location, ncol = 1)

# plot broken stick dispersion to verify number of plausible groups
library(ggplot2)

nested_coniss %>%
  select(location, broken_stick) %>%
  unnest(broken_stick) %>%
  tidyr::gather(type, value, broken_stick_dispersion, dispersion) %>%
  ggplot(aes(x = n_groups, y = value, col = type)) +
  geom_line() +
  geom_point() +
  facet_wrap(vars(location))

paleolimbot/tidypaleo documentation built on Jan. 25, 2023, 2:43 a.m.