bvcpp(x, mar = 2, ci = TRUE, main = "Conditional Probability Plot",
xlab = "Empirical", ylab = "Model", ...)
"bvevd"
.TRUE
(the default), plot simulated
95% confidence intervals.ppoints
and $c_i$ is the $i$th largest
value from the sample
${G(z_{j1}|z_{j2}), j = 1,\ldots,m}.$
When $\code{mar} = 1$ the margins are reversed, so that
$G(.|.)$ is the conditional distribution of the second margin
given the first.
For non-stationary models the data are transformed to stationarity.
The plot then corresponds to the distribution obtained when all
covariates are zero.bvdens
, bvdp
,
plot.bvevd
bvdata <- rbvlog(100, dep = 0.6)
M1 <- fbvlog(bvdata)
bvcpp(M1)
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