bsts (version 0.6.1)

PlotDynamicDistribution: Plots the evolution of a distribution over time.

Description

Produces an dynamic distribution plot where gray scale shading is used to show the evolution of a distribution over time. This function is useful when there are too many time points to do side-by-side boxplots.

Usage

PlotDynamicDistribution(curves, time = 1:ncol(curves), quantile.step=.01,
                        xlim = range(time), xlab = "time",
                        ylim = range(curves), ylab = "distribution",
                        add=FALSE, ...)

Arguments

curves

A matrix where each row represents a curve (e.g. a simulation of a time series from a posterior distribution) and columns represent time. A long time series would be a wide matrix.

time

An optional vector of time points that 'curves' will be plotted against. Good choices for time are numeric, or Date (see as.Date).

quantile.step

Each color step in the plot corresponds to this difference in quantiles.. Smaller values make prettier plots, but the plots take longer to produce.

xlim

The x limits (x1, x2) of the plot. Note that x1 > x2 is allowed and leads to a "reversed axis".

xlab

Label for the horzontal axis.

ylim

The y limits (y1, y2) of the plot. Note that y1 > y2 is allowed and leads to a "reversed axis".

ylab

Label for the vertical axis.

add

Logical. If true then add the plot to the current plot. Otherwise a fresh plot will be created.

...

Extra arguments to pass on to plot

Value

• This function is called for its side effect, which is to produce a plot on the current graphics device.

Details

The function works by passing many calls to polygon. Each polygon is associated with a quantile level, with darker shading near the median.

Examples

x <- t(matrix(rnorm(1000 * 100, 1:100, 1:100), nrow=100))
  ## x has 1000 rows, and 100 columns.  Column i is N(i, i^2) noise.

  PlotDynamicDistribution(x)
  time <- as.Date("2010-01-01", format = "%Y-%m-%d") + (0:99 - 50)*7
  PlotDynamicDistribution(x, time)