PointProcess {QRM} | R Documentation |
Functions for estimating point processes.
extremalPP(data, threshold = NA, nextremes = NA, ...) unmark(PP) fit.POT(PP, markdens = "GPD", ...) fit.sePP(PP, model = c("Hawkes", "ETAS"), mark.influence = TRUE, std.errs = FALSE, ...) fit.seMPP(PP, markdens = "GPD", model = c("Hawkes", "ETAS"), mark.influence = TRUE, predictable = FALSE, std.errs = FALSE, ...) stationary.sePP(sePP) sePP.negloglik(theta, PP, case) seMPP.negloglik(theta, PP, case, markdens) volfunction(anytimes, times, marks, theta, model) ## S3 method for class 'MPP' plot(x, ...) ## S3 method for class 'PP' plot(x, ...) ## S3 method for class 'sePP' plot(x, ...)
anytimes |
|
data |
|
case |
|
markdens |
|
mark.influence |
|
marks |
|
model |
|
nextremes |
|
PP |
|
predictable |
|
sePP |
|
std.errs |
|
theta |
|
threshold |
|
times |
|
x |
|
... |
ellipsis, arguments passed to |
extremalPP()
: returns a list describing a marked point process
(see pages 298-301 of QRM).
fit.POT()
: fits the POT (peaks-over-threshold) model to a point
process of class PP
or MPP
. Note that if point process
is of class PP
, then function simply esitmates the rate of a
homogeneous Poisson process (see pages 301–305 of QRM).
fit.seMPP()
: fits a marked self-exciting process to a point
process object of class MPP
.
fit.sePP()
: fits self-exciting process to a point process
object of class PP
(unmarked) or MPP
(marked).
seMPP.negloglik()
: evaluates negative log-likelihood of a
marked self-exciting point process model; this objective function will
be passed to the optimizing function.
sePP.negloglik()
: evaluates negative log-likelihood of a
self-exciting point process model (unmarked).
stationary.sePP()
: checks a sufficient condition for
stationarity of a self-exciting model and gives information about
cluster size.
unmark()
: strips marks from a marked point process.
volfunction()
: calculates a self-excitement function for use in
the negloglik methods used in fit.sePP()
and
fit.seMPP()
.
The function extremalPP()
returns a list describing class MPP
(marked point process) consisting of times and magnitudes of threshold
exceedances:
times |
vector of julian day counts (since 1/1/1960) for each exceedance |
marks |
vector of exceedances values (differences between value and threshold at each mark) |
startime |
the julian count one day prior to the first date in the entire timeSeries |
endtime |
value of last julian count in entire timeSeries |
threshold |
value of threshold above which exceedances are calculated |
The functions fit.POT()
, fit.seMPP()
, and
fit.sePP()
return a list containing the fitted model.
The plot
-methods return invisibly the data for producing
these.
## Extremal PP data(sp500) l <- -returns(sp500) lw <- window(l, start = "1995-12-31", end = end(l)) mod1 <- extremalPP(lw, ne = 100) mod1$marks[1:5] mod1$threshold mod2a <- fit.sePP(mod1, mark.influence = FALSE, std.errs = TRUE) mod2b <- fit.seMPP(mod1, mark.influence = FALSE, std.errs = TRUE) stationary.sePP(mod2b) mod2c <- fit.POT(mod1, method = "BFGS") plot(mod1) plot(unmark(mod1)) plot(mod2a)