View source: R/utilities_svdraws.R
updatesummary | R Documentation |
Creates or updates a summary of an svdraws
object.
updatesummary(
x,
quantiles = c(0.05, 0.5, 0.95),
esspara = TRUE,
esslatent = FALSE
)
x |
|
quantiles |
numeric vector of posterior quantiles to be computed. The
default is |
esspara |
logical value which indicates whether the effective sample
size (ESS) should be calculated for the parameter draws. This is
achieved by calling |
esslatent |
logical value which indicates whether the effective sample
size (ESS) should be calculated for the latent log-volatility draws.
This is achieved by calling |
updatesummary
will always calculate the posterior mean and the
posterior standard deviation of the raw draws and some common
transformations thereof. Moroever, the posterior quantiles, specified by the
argument quantiles
, are computed. If esspara
and/or
esslatent
are TRUE
, the corresponding effective sample size
(ESS) will also be included.
The value returned is an updated list object of class svdraws
holding
para |
|
latent |
|
latent0 |
|
y |
argument |
runtime |
|
priors |
|
thinning |
|
summary |
|
To display the output, use print
, summary
and plot
. The
print
method simply prints the posterior draws (which is very likely
a lot of output); the summary
method displays the summary statistics
currently stored in the object; the plot
method gives a graphical
overview of the posterior distribution by calling volplot
,
traceplot
and densplot
and displaying the
results on a single page.
updatesummary
does not actually overwrite the object's current
summary, but in fact creates a new object with an updated summary. Thus,
don't forget to overwrite the old object if this is want you intend to do.
See the examples below for more details.
svsample
## Here is a baby-example to illustrate the idea.
## Simulate an SV time series of length 51 with default parameters:
sim <- svsim(51)
## Draw from the posterior:
res <- svsample(sim$y, draws = 2000, priorphi = c(10, 1.5))
## Check out the results:
summary(res)
plot(res)
## Look at other quantiles and calculate ESS of latents:
newquants <- c(0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99)
res <- updatesummary(res, quantiles = newquants, esslatent = TRUE)
## See the difference?
summary(res)
plot(res)
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