plot.bsts.predictors | R Documentation |
Creates a time series plot showing the most likely
predictors of a time series used to fit a bsts
object.
PlotBstsPredictors(bsts.object, burn = SuggestBurn(.1, bsts.object), inclusion.threshold = .1, ylim = NULL, flip.signs = TRUE, show.legend = TRUE, grayscale = TRUE, short.names = TRUE, ...)
bsts.object |
An object of class |
burn |
The number of observations you wish to discard as burn-in. |
inclusion.threshold |
Plot predictors with marginal inclusion probabilities above this threshold. |
ylim |
Scale for the vertical axis. |
flip.signs |
If true then a predictor with a negative sign will be flipped before being plotted, to better align visually with the target series. |
show.legend |
Should a legend be shown indicating which predictors are plotted? |
grayscale |
Logical. If |
short.names |
Logical. If |
... |
Extra arguments to be passed to |
bsts
PlotDynamicDistribution
plot.lm.spike
data(AirPassengers) y <- log(AirPassengers) lag.y <- c(NA, head(y, -1)) ss <- AddLocalLinearTrend(list(), y) ss <- AddSeasonal(ss, y, nseasons = 12) ## Call bsts with na.action = na.omit to omit the leading NA in lag.y model <- bsts(y ~ lag.y, state.specification = ss, niter = 500, na.action = na.omit) plot(model, "predictors")
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