bsts (version 0.6.2)

plot.dynamic.distribution: Plots the pointwise evolution of a distribution over an index set.

Description

Produces an dynamic distribution plot where gray scale shading is used to show the evolution of a distribution over an index set. This function is particularly useful when the index set is too large to do side-by-side boxplots.

Usage

PlotDynamicDistribution(curves,
                        timestamps = NULL,
                        quantile.step=.01,
                        xlim = NULL,
                        xlab = "Time",
                        ylim = range(curves, na.rm = TRUE),
                        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 different points in the index set. For example, a long time series would be a wide matrix.

timestamps

An optional vector of "time stamps" that curves will be plotted against. The length of timestamps must match the number of columns in curves. If timestamps is NULL then the func

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)