Functions to plot the results of a model fit using
bsts.
# S3 method for bsts
plot(x, y = c("state", "components", "residuals",
"coefficients", "prediction.errors",
"forecast.distribution",
"predictors", "size", "dynamic", "seasonal", "monthly",
"help"),
...) PlotBstsCoefficients(bsts.object, burn = SuggestBurn(.1, bsts.object),
inclusion.threshold = 0, number.of.variables = NULL, ...)
PlotBstsComponents(bsts.object,
burn = SuggestBurn(.1, bsts.object),
time,
same.scale = TRUE,
layout = c("square", "horizontal", "vertical"),
style = c("dynamic", "boxplot"),
ylim = NULL,
components = 1:length(bsts.object$state.specification),
...)
PlotDynamicRegression(bsts.object,
burn = SuggestBurn(.1, bsts.object),
time = NULL,
same.scale = FALSE,
style = c("dynamic", "boxplot"),
layout = c("square", "horizontal", "vertical"),
ylim = NULL,
zero.width = 2,
zero.color = "green",
...)
PlotBstsState(bsts.object, burn = SuggestBurn(.1, bsts.object),
time, show.actuals = TRUE,
style = c("dynamic", "boxplot"),
scale = c("linear", "mean"),
ylim = NULL,
...)
PlotBstsResiduals(bsts.object, burn = SuggestBurn(.1, bsts.object),
time, style = c("dynamic", "boxplot"), means =
TRUE, ...)
PlotBstsPredictionErrors(bsts.object, cutpoints = NULL,
burn = SuggestBurn(.1, bsts.object),
style = c("dynamic", "boxplot"),
xlab = "Time", ylab = "", main = "",
...)
PlotBstsForecastDistribution(bsts.object, cutpoints = NULL,
burn = SuggestBurn(.1, bsts.object),
style = c("dynamic", "boxplot"),
xlab = "Time",
ylab = "",
main = "",
show.actuals = TRUE,
col.actuals = "blue",
...)
PlotBstsSize(bsts.object, burn = SuggestBurn(.1, bsts.object), style =
c("histogram", "ts"), ...)
PlotSeasonalEffect(bsts.object, nseasons = 7, season.duration = 1,
same.scale = TRUE, ylim = NULL, get.season.name = NULL,
burn = SuggestBurn(.1, bsts.object), ...)
PlotMonthlyAnnualCycle(bsts.object, ylim = NULL, same.scale = TRUE,
burn = SuggestBurn(.1, bsts.object), ...)
An object of class bsts.
An object of class bsts.
A character string indicating the aspect of the model that should be plotted.
The number of MCMC iterations to discard as burn-in.
The color to use for the actual data when comparing actuals vs forecasts.
A numeric vector indicating which components to plot. Component indices correspond to elements of the state specification that was used to build the bsts model being plotted.
A numeric vector of integers, or NULL. For
diagnostic plots of prediction errors or forecast distributions, the
model will be re-fit with a separate MCMC run for each entry in
'cutpoints'. Data up to each cutpoint will be included in the fit,
and one-step prediction errors for data after the cutpoint will be
computed.
A function that can be used to infer the title
of each seasonal plot. It should take a single POSIXt,
Date, or similar object as an argument, and return
a single string that can be used as a panel title. If
get.season.name is NULL and nseasons is
specified or inferred to be one of the following values, then the
following functions will be used.
An inclusion probability that individual
coefficients must exceed in order to be displayed when what ==
"coefficients". See the help file for
plot.lm.spike.
For controlling the layout of functions that generate mutiple plots.
Main title for the plot.
Logical. If TRUE then the mean of each residual is plotted as a blue dot. If false only the distribution of the residuals is plotted.
If there is only one seasonal component in the model,
this argument is ignored. If there are multiple seasonal
components then nseasons and season.duration are used
to select the desired one.
If non-NULL this specifies the
number of coefficients to plot, taking precedence over
inclusion.threshold. See plot.lm.spike.
Logical. If TRUE then all the state
components will be plotted with the same scale on the vertical axis.
If FALSE then each component will get its own scale for the
vertical axis.
The scale on which to plot the state. If the error family is "logit" or "poisson" then the state can either be plotted on the scale of the linear predictor (e.g. trend + seasonal + regression) or the linear predictor can be passed through the link function so as to plot the distribution of the conditional mean.
If there is only one seasonal component in the
model, this argument is ignored. If there are multiple seasonal
components then nseasons and season.duration are used
to select the desired one.
Logical. If TRUE then actual values from
the fitted series will be shown on the plot.
The desired plot style. Partial matching is allowed, so "dyn" would match "dynamic", for example.
An optional vector of values to plot against. If missing, the default is to diagnose the time scale of the original time series.
Label for the horizontal axis.
Label for the vertical axis.
Limits for the vertical axis. If NULL these will
be inferred from the state components and the same.scale
argument. Otherwise all plots will be created with the same
ylim values.
A numerical value for the width of the reference
line at zero. If NULL then the line will be omitted.
A color for the width of the reference line at zero.
If NULL then the line will be omitted.
Additional arguments to be passed to
PlotDynamicDistribution, or
TimeSeriesBoxplot.
These functions are called for their side effect, which is to produce a plot on the current graphics device.
PlotBstsState invisibly returns the state object being plotted.
PlotBstsState, PlotBstsComponents, and
PlotBstsResiduals all produce dynamic distribution
plots. PlotBstsState plots the aggregate state
contribution (including regression effects) to the mean, while
PlotBstsComponents plots the contribution of each state
component. PlotBstsResiduals plots the posterior
distribution of the residuals given complete data (i.e. looking
forward and backward in time). PlotBstsPredictionErrors
plots filtering errors (i.e. the one-step-ahead prediction errors
given data up to the previous time point).
PlotBstsForecastDistribution plots the one-step-ahead
forecasts instead of the prediction errors.
PlotBstsCoefficients creates a significance plot for
the predictors used in the state space regression model. It is
obviously not useful for models with no regressors.
PlotBstsSize plots the distribution of the number of
predictors included in the model.
PlotSeasonalEffect generates an array of plots showing
how the distibution of the seasonal effect changes, for each season,
for models that include a seasonal state component.
PlotMonthlyAnnualCycle produces an array of plots much
like PlotSeasonalEffect, for models that include a
MonthlyAnnualCycle state component.
# NOT RUN {
data(AirPassengers)
y <- log(AirPassengers)
ss <- AddLocalLinearTrend(list(), y)
ss <- AddSeasonal(ss, y, nseasons = 12)
model <- bsts(y, state.specification = ss, niter = 500)
plot(model, burn = 100)
plot(model, "residuals", burn = 100)
plot(model, "components", burn = 100)
plot(model, "forecast.distribution", burn = 100)
# }
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