tidypaleo (version 0.1.4)

nested_hclust: 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,
  ...
)

Value

.data with additional columns

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

References

Bennett, K. (1996) Determination of the number of zones in a biostratigraphic sequence. New Phytologist, 132, 155-170. tools:::Rd_expr_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. tools:::Rd_expr_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))