sigex.graph | R Documentation |
Background: A sigex model consists of process x = sum y, for stochastic components y. Each component process y_t is either stationary or is reduced to stationarity by application of a differencing polynomial delta(B), i.e. w_t = delta(B) y_t is stationary. We have a model for each w_t process, and can compute its autocovariance function (acf), and denote its autocovariance generating function (acgf) via gamma_w (B). The signal extraction filter for y_t is determined from this acgf and delta.
sigex.graph(
extract,
reg = NULL,
start.date,
period,
series,
displace,
color,
fade
)
extract |
T x N matrix of the signal estimates, e.g. output of sigex.extract |
reg |
A T x N matrix of fixed effects, to be added to extraction. Note: only the column of reg corresponding to "series" is utilized. (To incorporate multiple reg effects, add these all up before hand.) |
start.date |
Date of first time obersvation; the format is c(year,season) |
period |
Number of seasons per year |
series |
Index of the particular series under consideration |
displace |
Gives a vertical shift to the plot |
color |
Given as a number in the range of colors() |
fade |
Gives shading proportion for uncertainty (NULL if none) |
NA
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.