Description Usage Arguments Details See Also Examples
Four plots (selectable by which
) are currently provided:
a PP plot, a QQ plot, a density plot and a return level plot.
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x 
An object that inherits from class 
which 
If a subset of the plots is required, specify a
subset of the numbers 
main 
Title of each plot. If given, must be a character
vector with the same length as 
ask 
Logical; if 
ci 
Logical; if 
cilwd 
Line width for confidence interval lines. 
a 
Passed through to 
adjust, jitter, nplty 
Arguments to the density plot.
The density of the fitted model is plotted with a rug plot and
(optionally) a nonparameteric estimate. The argument

... 
Other parameters to be passed through to plotting functions. 
The following discussion assumes that the fitted model is stationary. For nonstationary generalized extreme value models the data are transformed to stationarity. The plot then corresponds to the distribution obtained when all covariates are zero.
The PP plot consists of the points
{(G_n(z_i), G(z_i)), i = 1,…,m}
where G_n is the empirical distribution function
(defined using ppoints
), G is the model based
estimate of the distribution (generalized extreme value
or generalized Pareto), and z_1,…,z_m are the data
used in the fitted model, sorted into ascending order.
The QQ plot consists of the points
{(G^{1}(p_i), z_i), i = 1,…,m}
where G^{1} is the model based estimate of the quantile
function (generalized extreme value or generalized Pareto),
p_1,…,p_m are plotting points defined by
ppoints
, and z_1,…,z_m are the data
used in the fitted model, sorted into ascending order.
The return level plot for generalized extreme value models is defined as follows.
Let G be the generalized extreme value distribution function, with location, scale and shape parameters a, b and s respectively. Let z_t be defined by G(z_t) = 1  1/t. In common terminology, z_t is the return level associated with the return period t.
Let y_t = 1/log(1  1/t). It follows that
z_t = a + b((y_t)^s  1)/s.
When s = 0, z_t is defined by continuity, so that
z_t = a + b log(y_t).
The curve within the return level plot is z_t plotted against y_t on a logarithmic scale, using maximum likelihood estimates of (a,b,s). If the estimate of s is zero, the curve will be linear. For large values of t, y_t is approximately equal to the return period t. It is usual practice to label the xaxis as the return period.
The points on the plot are
{(1/log(p_i), z_i), i = 1,…,m}
where p_1,…,p_m are plotting points defined by
ppoints
, and z_1,…,z_m are the data
used in the fitted model, sorted into ascending order.
For a good fit the points should lie “close” to the curve.
The return level plot for peaks over threshold models is defined as follows.
Let G be the generalized Pareto distribution function,
with location, scale and shape parameters u, b
and s respectively, where u is the model threshold.
Let z_m denote the m period return level
(see fpot
and the notation therein).
It follows that
z_m = u + b((pmN)^s  1)/s.
When s = 0, z_m is defined by continuity, so that
z_m = u + b log(pmN).
The curve within the return level plot is z_m plotted against m on a logarithmic scale, using maximum likelihood estimates of (b,s,p). If the estimate of s is zero, the curve will be linear.
The points on the plot are
{(1/(pN(1p_i)), z_i), i = 1,…,m}
where p_1,…,p_m are plotting points defined by
ppoints
, and z_1,…,z_m are the data
used in the fitted model, sorted into ascending order.
For a good fit the points should lie “close” to the curve.
plot.bvevd
, density
,
jitter
, rug
, ppoints
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