ppFit {fExtremes} | R Documentation |
This is a collection of functions to model point processes, PP,
over a threshold, based on R's ismev
package. The parameter
estimation allows to include generalized linear modelling of
each parameter.
The functions are:
1 | potSim | generates data from a point process, |
2 | potFit | fits empirical or simulated data to a point process, |
3 | print | print method for a fitted POT object of class ..., |
4 | plot | plot method for a fitted GEV object, |
5 | summary | summary method for a fitted GEV object, |
6 | gevrlevelPlot | k-block return level with confidence intervals. |
ppFit(x, threshold, npy = 365, y = NULL, mul = NULL, sigl = NULL, shl = NULL, mulink = identity, siglink = identity, shlink = identity, method = "Nelder-Mead", maxit = 10000, ...) ## S3 method for class 'ppFit': print(x, ...) ## S3 method for class 'ppFit': plot(x, which = "ask", ...) ## S3 method for class 'ppFit': summary(object, doplot = TRUE, which = "all", ...)
doplot |
a logical. Should the results be plotted? |
maxit |
[ppFit] - the maximum number of iterations. |
method |
[ppFit] -
The optimization method (see optim for details).
|
mul, sigl, shl |
[ppFit] -
numeric vectors of integers, giving the columns
of ydat that contain covariates for generalized linear
modelling of the location, scale and shape parameters repectively
(or NULL (the default) if the corresponding parameter is
stationary).
|
mulink, siglink, shlink |
[ppFit] - inverse link functions for generalized linear modelling of the location, scale and shape parameters repectively. |
npy |
[ppFit] - the number of observations per year/block. |
object |
[summary] -
a fitted object of class "ppFit" .
|
threshold |
[ppFit] -
the threshold; a single number or a numeric
vector of the same length as x .
|
which |
[print][plot][summary] - a logical for each plot, denoting which plots should be created. |
x |
[ppFit] -
a numeric vector of data to be fitted.
[print][plot] - a fitted object of class "ppFit" .
|
y |
[ppFit] -
a matrix of covariates for generalized linear modelling
of the parameters (or NULL (the default) for stationary
fitting). The number of rows should be the same as the length
of x .
|
... |
[ppFit] -
control parameters and plot parameters optionally passed to the
optimization and/or plot function. Parameters for the optimization
function are passed to components of the control argument of
optim .
|
For non-stationary fitting it is recommended that the covariates
within the generalized linear models are (at least approximately)
centered and scaled (i.e. the columns of ydat
should be
approximately centered and scaled).
A list containing the following components. A subset of these
components are printed after the fit. If show
is
TRUE
, then assuming that successful convergence is
indicated, the components nexc
, nllh
, mle
and se
are always printed.
trans |
An logical indicator for a non-stationary fit. |
model |
A list with components mul , sigl
and shl .
|
link |
A character vector giving inverse link functions. |
threshold |
The threshold, or vector of thresholds. |
npy |
The number of observations per year/block. |
nexc |
The number of data points above the threshold. |
data |
The data that lie above the threshold. For non-stationary models, the data is standardized. |
conv |
The convergence code, taken from the list returned by
optim . A zero indicates successful convergence.
|
nllh |
The negative logarithm of the likelihood evaluated at the maximum likelihood estimates. |
vals |
A matrix with four columns containing the maximum likelihood estimates of the location, scale and shape parameters, and the threshold, at each data point. |
gpd |
A matrix with three rows containing the maximum likelihood estimates of corresponding GPD location, scale and shape parameters at each data point. |
mle |
A vector containing the maximum likelihood estimates. |
cov |
The covariance matrix. |
se |
A vector containing the standard errors. |
For stationary models two plots are produced; a probability plot
and a quantile plot. For non-stationary models two plots are produced;
a residual probability plot and a residual quantile plot.
Alec Stephenson for the code implemented from R's ismev package,
Stuart Scott for the original code, and
Diethelm Wuertz for this R-port.
Coles S. (2001); Introduction to Statistical Modelling of Extreme Values, Springer.
## Use Rain Data: data(rain) ## Fit Point Process Model: xmpExtremes("Start: Parameter Fit for Point Process > ") fit = ppFit(x = rain[1:200], threshold = 10) print(fit) ## Summarize Results: xmpExtremes("Next: Diagnostic Analysis > ") par(mfrow = c(2, 2), cex = 0.75) summary(fit) xmpExtremes("Next: Interactive Plot > ") ## Interactive Plot: ##> par(mfrow = c(2, 2), cex = 0.75) ##> plot(fit)