Plot of the predictive distribution of an extreme value mixture model.
pred(x, ...)# S3 method for evmm
pred(
x,
x_axis = seq(min(x$data), max(x$data), length.out = 1000),
cred = 0.95,
xlim = c(min(x$data), max(x$data)),
ylim = NULL,
...
)
A plot of the estimate of the predictive distribution together with the data histogram.
the output of a model estimated with extrememix.
additional arguments for compatibility.
vector of points where to estimate the predictive distribution.
amplitude of the posterior credibility interval.
limits of the x-axis.
limits of the y-axis.
Consider an extreme value mixture model \(f(y|\theta)\) and suppose a sample \((\theta^{(1)},\dots,\theta^{(S)})\) from the posterior distribution is available. The predictive distribution at the point \(y\) is estimated as $$\frac{1}{S}\sum_{s=1}^Sf(y|\theta^{(s)})$$
do Nascimento, Fernando Ferraz, Dani Gamerman, and Hedibert Freitas Lopes. "A semiparametric Bayesian approach to extreme value estimation." Statistics and Computing 22.2 (2012): 661-675.