# plot.svpredict: Graphical Summary of the Posterior Predictive Distribution In stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models

## Description

`plot.svpredict` and `plot.svlpredict` generate some plots visualizing the posterior predictive distribution of future volatilites and future observations.

## Usage

 ```1 2``` ```## S3 method for class 'svpredict' plot(x, quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95), ...) ```

## Arguments

 `x` `svpredict` or `svlpredict` object. `quantiles` Which quantiles to plot? Defaults to `c(.05, .25, .5, .75, .95)`. `...` further arguments are passed on to the invoked `ts.plot` or `boxplot` function.

## Value

Called for its side effects. Returns argument `x` invisibly.

## Note

Note that `svpredict` or `svlpredict` objects can also be used within `plot.svdraws` for a possibly more useful visualization. See the examples in `predict.svdraws` and those below for use cases.

Other plotting: `paradensplot()`, `paratraceplot.svdraws()`, `paratraceplot()`, `plot.svdraws()`, `volplot()`
Other plotting: `paradensplot()`, `paratraceplot.svdraws()`, `paratraceplot()`, `plot.svdraws()`, `volplot()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## Simulate a short and highly persistent SV process sim <- svsim(100, mu = -10, phi = 0.99, sigma = 0.1) ## Obtain 5000 draws from the sampler (that's not a lot) draws <- svsample(sim\$y, draws = 5000, burnin = 1000) ## Predict 10 steps ahead pred <- predict(draws, 10) ## Visualize the predicted distributions plot(pred) ## Plot the latent volatilities and some forecasts plot(draws, forecast = pred) ```