plot.bsts.prediction {bsts}  R Documentation 
Plot the posterior predictive distribution from a
bsts
prediction object.
## S3 method for class 'bsts.prediction' plot(x, y = NULL, burn = 0, plot.original = TRUE, median.color = "blue", median.type = 1, median.width = 3, interval.quantiles = c(.025, .975), interval.color = "green", interval.type = 2, interval.width = 2, style = c("dynamic", "boxplot"), ylim = NULL, ...)
x 
An object of class 
y 
A dummy argument necessary to match the signature of the

plot.original 
Logical or numeric. If 
burn 
The number of observations you wish to discard as burnin
from the posterior predictive distribution. This is in addition
to the burnin discarded using 
median.color 
The color to use for the posterior median of the prediction. 
median.type 
The type of line (lty) to use for the posterior median of the prediction. 
median.width 
The width of line (lwd) to use for the posterior median of the prediction. 
interval.quantiles 
The lower and upper limits of the credible interval to be plotted. 
interval.color 
The color to use for the upper and lower limits of the 95% credible interval for the prediction. 
interval.type 
The type of line (lty) to use for the upper and lower limits of the 95% credible inerval for of the prediction. 
interval.width 
The width of line (lwd) to use for the upper and lower limits of the 95% credible inerval for of the prediction. 
style 
Either "dynamic", for dynamic distribution plots, or "boxplot", for box plots. Partial matching is allowed, so "dyn" or "box" would work, for example. 
ylim 
Limits on the vertical axis. 
... 
Extra arguments to be passed to

Plots the posterior predictive distribution described by
x
using a dynamic distribution plot generated by
PlotDynamicDistribution
. Overlays the
posterior median and 95% prediction limits for the predictive
distribution.
Returns NULL.
bsts
PlotDynamicDistribution
plot.lm.spike
data(AirPassengers) y < log(AirPassengers) ss < AddLocalLinearTrend(list(), y) ss < AddSeasonal(ss, y, nseasons = 12) model < bsts(y, state.specification = ss, niter = 500) pred < predict(model, horizon = 12, burn = 100) plot(pred)