plot.evpred: Plot diagnostics for an evpred object

In revdbayes: Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis

Plot diagnostics for an evpred object

Description

plot method for class "evpred". Plots summarising the predictive distribution of the largest value to be observed in N years are produced. The plot produced depends on x$type. If x$type = "d", "p" or "q" then matplot is used to produce a line plot of the predictive density, distribution or quantile function, respectively, with a line for each value of N in x$n_years. If x$type = "r" then estimates of the predictive density (from density) are plotted with a line for each N. If x$type = "i" then lines representing estimated predictive intervals are plotted, with the level of the interval indicated next to the line.

Usage

## S3 method for class 'evpred'
plot(
  x,
  ...,
  leg_pos = NULL,
  leg_text = NULL,
  which_int = c("long", "short", "both")
)

Arguments

x	An object of class "evpost", a result of a call to rpost.
...	Additional arguments passed on to matplot.
leg_pos	A character scalar. Keyword for the position of legend. See legend.
leg_text	A character or expression vector. Text for legend. See legend.
which_int	A character scalar. If x$type = "i" which intervals should be plotted? "long" for equi-tailed intervals, "short" for the shortest possible intervals, "both" for both.

Value

Nothing is returned.

See Also

predict.evpost for the S3 predict method for objects of class evpost.

Examples

data(portpirie)
mat <- diag(c(10000, 10000, 100))
pn <- set_prior(prior = "norm", model = "gev", mean = c(0,0,0), cov = mat)
gevp  <- rpost(n = 1000, model = "gev", prior = pn, data = portpirie)

# Predictive density function
d_gevp <- predict(gevp, type = "d", n_years = c(100, 1000))
plot(d_gevp)

# Predictive distribution function
p_gevp <- predict(gevp, type = "p", n_years = c(100, 1000))
plot(p_gevp)

# Predictive quantiles
q_gevp <- predict(gevp, type = "q", n_years = c(100, 1000))
plot(q_gevp)

# Predictive intervals
i_gevp <- predict(gevp, type = "i", n_years = c(100, 1000), hpd = TRUE)
plot(i_gevp, which_int = "both")

# Sample from predictive distribution
r_gevp <- predict(gevp, type = "r", n_years = c(100, 1000))
plot(r_gevp)
plot(r_gevp, xlim = c(4, 10))

revdbayes documentation built on Sept. 10, 2023, 1:07 a.m.