ArchaeoPhases (version 1.8)

tempo_activity_plot: Plot the derivative of the tempo plot Bayesian estimate

Description

A statistical graphic designed for the archaeological study of rhythms of the long term that embodies a theory of archaeological evidence for the occurrence of events

Usage

tempo_activity_plot(
  data,
  position = 1:ncol(data),
  title = "Tempo Activity Plot",
  subtitle = NULL,
  caption = "ArcheoPhases",
  x_label = "Calendar year",
  y_label = "Activity",
  line_types = c("solid"),
  width = 7,
  height = 7,
  units = "in",
  x_min = NULL,
  x_max = NULL,
  file = NULL,
  x_scale = "calendar",
  elapsed_origin_position = NULL,
  new_window = TRUE,
  plot_result = TRUE
)

Arguments

data

Data frame containing the output of the MCMC algorithm.

position

Numeric vector containing the position of the column corresponding to the MCMC chains of interest, or a vector of column names.

title

Title of the plot.

subtitle

Subtitle of the plot.

caption

Caption of the plot.

x_label

Label of the x-axis.

y_label

Label of the y-axis.

line_types

Type of the lines drawn on the plot.

width

Width of the plot in units.

height

Height of the plot in units.

units

Units used to specify width and height, one of "in" (default),"cm", or "mm".

x_min

Minimum value for x-axis.

x_max

Maximum value for x-axis.

file

Name of the file to be saved if specified. If Null, then no file is saved.

x_scale

One of "calendar", "bp", or "elapsed".

elapsed_origin_position

If x_scale is "elapsed", the position of the column corresponding to the event from which elapsed time is calculated.

new_window

Whether or not the plot is drawn within a new window.

plot_result

If TRUE, then draw a plot on the display, else suppress drawing.

Value

An archaeophases_plot object with the data and metadata needed to reproduce the plot.

References

Dye, T.S. (2016) Long-term rhythms in the development of Hawaiian social stratification. Journal of Archaeological Science, 71, 1--9.

Examples

# NOT RUN {
  data(Events);
  tempo_activity_plot(Events[1:1000, ], c(2:5))

# }