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etasFLP (version 1.2.0)

plot.etasclass: Plot method for etasclass objects

Description

This is the main method to visualize graphically the output of an object of class etasclass.

By default the space-time region is the same used for the estimation of the ETAS model. Background, triggered and total space intensities are also plotted for a grid of values.

Usage

## S3 method for class 'etasclass':
plot(x,pdf=FALSE,file ="etasplot", 
ngrid=201,nclass=10,tfixed=0,flag.3D=FALSE,flag.log=FALSE,...)

Arguments

x
an etaclass object.
pdf
If TRUE, then 2D plots are sent to a pdf file
file
name of the pdf file
ngrid
number of points for each direction (x, y) of a ngrid*ngrid grid where estimated intensities must be evaluated. Default value= 201.
nclass
number of class for each direction (x, y) of a grid of nclass*nclass cells where estimated intensities must be evaluated. Must divide ngrid-1. Default value= 10.
tfixed
If a positive value is given, then the triggered intensity at time tfixed is estimated and visualized.
flag.3D
If TRUE a 3D plot is also produced.
flag.log
If TRUE then a log scale is used to plot intensities.
...
other arguments.

Value

  • This plot method computes, among others, back.grid, trig.grid, with coordinates x.grid and y.grid used to obtain image plots of background, triggered and total spatial estimated intensities (see etasclass to see the details of the mixed estimation method used).
  • x.gridx grid values.
  • y.gridy grid values.
  • back.gridbackground intensity estimated on a ngrid x ngrid grid.
  • trig.gridtriggered intensities estimated on a grid of ngrid x ngrid points.
  • tot.gridtotal intensities estimated on a grid of ngrid x ngrid points.
  • back.gridbackground space intensity estimated for observed points.
  • trig.gridtriggered space intensities estimated for observed points.
  • tot.gridtotal space intensities estimated for observed points.

Details

Different plots of the output of an object of class etasclass.

By default the space-time region is the same used for the estimation of the ETAS model. Background, triggered and total space intensities are also computed and plotted for a grid of values.

If a positive value is given for tfixed, then the triggered intensity at time tfixed is estimated and visualized. A tipical use can be with tfixed a day after a big earthquake.

Starting with the package version 1.2.0 different kind of residual analysis are computed and visualized, separately for the space and time dimensions. (8 plot on three windows for the space and 2 plots on one window for the time)

For space dimension,

Space residuals are computed dividing the observed rectangular space area in a equally spaced grid of nclass intervals for each dimension, so to divide the observed space area in nclass x nclass rectangular cells. We obtain the classical comparison between observed and theoretical frequencies. All frequencies are related to the whole time interval (and thus theoretical frequencies are obtained integrating estimated intensities with respect to time).

Fifth graph (image plot)

We define nclass x nclass standardized residuals: $$z_{\ell j} \ = \frac{n_{\ell j}-\hat{\nu}_{\ell j}}{\sqrt{\hat{\nu}_{\ell j}}} \qquad (\ell =1,2,...,nclass; \ j=1,2,...,nclass)$$ For each cell $\ell j$ we have observed ($n_{\ell j}$) and theoretical frequency ($\hat{\nu}_{\ell j}$).

Sixth graph (image plot)

We used a similar technique to compute residuals for the background seismicity only, to check if at least the estimation of the background component is appropriate. To this purpose the observed background frequencies (${}_b n_{ \ell j}$) are now computed by the sum of the estimated weights rho.weights and the theoretical background frequency ${}_b \hat{\nu}_{\ell j}$ by the estimated marginal space background intensity in each cell.

From these quantities we obtain nclass x nclass standardized residuals for the background intensity only:

$${}_b z_{\ell j} \ = \frac{{}_b n_{\ell j} \ - \ {}_b \hat{\nu}_{\ell j}}{\sqrt{{}_b \hat{\nu}_{\ell j}}} \qquad (\ell =1,2,...,nclass; \ j=1,2,...,nclass)$$

seventh plot: (space intensities (integrated over time))

A 3x2 plot: first column for observed vs.theoretical , second column for standardized residuals vs theoretical values. First row for total intensity, second row for background intensity, and third row for their difference, the triggered intensities

eight-th graph:

To check departure of the model for the time dimension, we first integrated the estimated intensity function with respect to the observed space region, so to obtain an estimated time process (a one dimensional ETAS model):

$$\hat{\lambda}(t)= \int \int_{\Omega_{(x,y)}}\, \hat{\lambda}(x,y,t)\,d x \, d y$$

As known, a non-homogeneous time process can be transformed to a homogeneous one through the integral transformation:

$$\tau_i = \int_{t_0}^{t_{i}} \hat{\lambda}(t) \ d t$$

Then, a plot of $\tau_i$ versus $i$ can give information about the departures of the models in the time dimension. In particular, this plot, together with a plot of the estimated time intensities, drawn on the same graphic winodw, can inform on the time at which departures are more evident

If pdf=TRUE all graphs are printed on a pdf file, as spcified by file; otherwise default screen device is used.

See Also

etasclass, eqcat, profile.etasclass

Examples

Run this code
data("italycatalog")
# load a sample catalog of the italian seismicity

class(italycatalog)<-"eqcat"

etas3.1<-etasclass(italycatalog,description="etas flp",magn.threshold=3.1,thinning=FALSE,flp=TRUE,
is.backconstant=FALSE,magn.threshold.back=3.5,sectoday=TRUE,
onlytime=FALSE,declustering=TRUE,epsmax=0.00001,
params.ind=c(1,1,1,1,1,1,1,1),k0=0.005,c=0.005,p=1.01,a=1.05,gamma=0.6,q=1.52,d=1.1,
compsqm=TRUE,usenlm=TRUE,ndeclust=15)

# execution of etasclass for events with minimum magnitude of 3.1. 
# The events with magnitude at least 3.5 are used to build a first approximation
# for the background intensity function
# (magn.threshold.back=3.5)


# plot method

> plot(etas3.1)

# profile likelihood for the 5-th parameter (a), with plot:

prof=profile(etas3.1,nprofile=7,iprofile=5)
plot(prof)

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