Last data update: 2014.03.03
R: Predictive Density of GPD
predictive.gpd R Documentation
Predictive Density of GPD
Description
Predictive density resulting of posterior distribution of GPD parameters.
Usage
predictive.gpd(vector, data, threshold)
Arguments
vector
a list object returned by posterior.gpd
data
data vector
threshold
a threshold value
Value
The program draws the predictive distribution of dataset fitted using
the posterior distribution of the GPD parameteres.
Examples
# Fitting the predictive distribution on simulated data
x=rgpd(500,xi=0.3,mu=9,beta=2)
ajuste=posterior.gpd(x,9,800)
predictive.gpd(ajuste,x,9)
# Danish data
data(danish)
out=posterior.gpd(danish,10,500)
predictive.gpd(out,danish,10)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MCMC4Extremes)
Loading required package: evir
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MCMC4Extremes/predictive.gpd.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predictive.gpd
> ### Title: Predictive Density of GPD
> ### Aliases: predictive.gpd
>
> ### ** Examples
>
> # Fitting the predictive distribution on simulated data
> x=rgpd(500,xi=0.3,mu=9,beta=2)
> ajuste=posterior.gpd(x,9,800)
[1] 0.008333333
[1] 0.01666667
[1] 0.025
[1] 0.03333333
[1] 0.04166667
[1] 0.05
[1] 0.05833333
[1] 0.06666667
[1] 0.075
[1] 0.08333333
[1] 0.09166667
[1] 0.1
[1] 0.1083333
[1] 0.1166667
[1] 0.125
[1] 0.1333333
[1] 0.1416667
[1] 0.15
[1] 0.1583333
[1] 0.1666667
[1] 0.175
[1] 0.1833333
[1] 0.1916667
[1] 0.2
[1] 0.2083333
[1] 0.2166667
[1] 0.225
[1] 0.2333333
[1] 0.2416667
[1] 0.25
[1] 0.2583333
[1] 0.2666667
[1] 0.275
[1] 0.2833333
[1] 0.2916667
[1] 0.3
[1] 0.3083333
[1] 0.3166667
[1] 0.325
[1] 0.3333333
[1] 0.3416667
[1] 0.35
[1] 0.3583333
[1] 0.3666667
[1] 0.375
[1] 0.3833333
[1] 0.3916667
[1] 0.4
[1] 0.4083333
[1] 0.4166667
[1] 0.425
[1] 0.4333333
[1] 0.4416667
[1] 0.45
[1] 0.4583333
[1] 0.4666667
[1] 0.475
[1] 0.4833333
[1] 0.4916667
[1] 0.5
[1] 0.5083333
[1] 0.5166667
[1] 0.525
[1] 0.5333333
[1] 0.5416667
[1] 0.55
[1] 0.5583333
[1] 0.5666667
[1] 0.575
[1] 0.5833333
[1] 0.5916667
[1] 0.6
[1] 0.6083333
[1] 0.6166667
[1] 0.625
[1] 0.6333333
[1] 0.6416667
[1] 0.65
[1] 0.6583333
[1] 0.6666667
[1] 0.675
[1] 0.6833333
[1] 0.6916667
[1] 0.7
[1] 0.7083333
[1] 0.7166667
[1] 0.725
[1] 0.7333333
[1] 0.7416667
[1] 0.75
[1] 0.7583333
[1] 0.7666667
[1] 0.775
[1] 0.7833333
[1] 0.7916667
[1] 0.8
[1] 0.8083333
[1] 0.8166667
[1] 0.825
[1] 0.8333333
[1] 0.8416667
[1] 0.85
[1] 0.8583333
[1] 0.8666667
[1] 0.875
[1] 0.8833333
[1] 0.8916667
[1] 0.9
[1] 0.9083333
[1] 0.9166667
[1] 0.925
[1] 0.9333333
[1] 0.9416667
[1] 0.95
[1] 0.9583333
[1] 0.9666667
[1] 0.975
[1] 0.9833333
[1] 0.9916667
[1] 1
> predictive.gpd(ajuste,x,9)
[1] 4.729532e-01 2.189453e-01 1.159186e-01 6.731754e-02 4.185702e-02
[6] 2.743766e-02 1.876072e-02 1.327863e-02 9.673367e-03 7.221319e-03
[11] 5.505181e-03 4.274078e-03 3.371721e-03 2.697691e-03 2.185698e-03
[16] 1.790922e-03 1.482405e-03 1.238353e-03 1.043156e-03 8.854563e-04
[21] 7.568734e-04 6.511410e-04 5.635179e-04 4.903772e-04 4.289155e-04
[26] 3.769458e-04 3.327466e-04 2.949518e-04 2.624690e-04 2.344184e-04
[31] 2.100865e-04 1.888910e-04 1.703540e-04 1.540811e-04 1.397446e-04
[36] 1.270716e-04 1.158330e-04 1.058361e-04 9.691785e-05 8.893997e-05
[41] 8.178446e-05 7.535040e-05 6.955114e-05 6.431202e-05 5.956852e-05
[46] 5.526468e-05 5.135185e-05 4.778759e-05 4.453478e-05 4.156087e-05
[51] 3.883726e-05 3.633873e-05 3.404300e-05 3.193035e-05 2.998328e-05
[56] 2.818623e-05 2.652533e-05 2.498821e-05 2.356379e-05 2.224215e-05
[61] 2.101438e-05 1.987246e-05 1.880919e-05 1.781803e-05 1.689312e-05
[66] 1.602912e-05 1.522121e-05 1.446500e-05 1.375650e-05 1.309209e-05
[71] 1.246846e-05
> # Danish data
> data(danish)
> out=posterior.gpd(danish,10,500)
[1] 0.01333333
[1] 0.02666667
[1] 0.04
[1] 0.05333333
[1] 0.06666667
[1] 0.08
[1] 0.09333333
[1] 0.1066667
[1] 0.12
[1] 0.1333333
[1] 0.1466667
[1] 0.16
[1] 0.1733333
[1] 0.1866667
[1] 0.2
[1] 0.2133333
[1] 0.2266667
[1] 0.24
[1] 0.2533333
[1] 0.2666667
[1] 0.28
[1] 0.2933333
[1] 0.3066667
[1] 0.32
[1] 0.3333333
[1] 0.3466667
[1] 0.36
[1] 0.3733333
[1] 0.3866667
[1] 0.4
[1] 0.4133333
[1] 0.4266667
[1] 0.44
[1] 0.4533333
[1] 0.4666667
[1] 0.48
[1] 0.4933333
[1] 0.5066667
[1] 0.52
[1] 0.5333333
[1] 0.5466667
[1] 0.56
[1] 0.5733333
[1] 0.5866667
[1] 0.6
[1] 0.6133333
[1] 0.6266667
[1] 0.64
[1] 0.6533333
[1] 0.6666667
[1] 0.68
[1] 0.6933333
[1] 0.7066667
[1] 0.72
[1] 0.7333333
[1] 0.7466667
[1] 0.76
[1] 0.7733333
[1] 0.7866667
[1] 0.8
[1] 0.8133333
[1] 0.8266667
[1] 0.84
[1] 0.8533333
[1] 0.8666667
[1] 0.88
[1] 0.8933333
[1] 0.9066667
[1] 0.92
[1] 0.9333333
[1] 0.9466667
[1] 0.96
[1] 0.9733333
[1] 0.9866667
[1] 1
> predictive.gpd(out,danish,10)
[1] 1.445380e-01 7.109718e-02 4.053160e-02 2.534129e-02 1.690326e-02
[6] 1.183445e-02 8.605288e-03 6.451091e-03 4.959453e-03 3.894230e-03
[11] 3.113474e-03 2.528336e-03 2.081264e-03 1.733867e-03 1.459867e-03
[16] 1.240874e-03 1.063753e-03 9.189560e-04 7.994322e-04 6.998960e-04
[21] 6.163340e-04 5.456621e-04 4.854835e-04 4.339168e-04 3.894716e-04
[26] 3.509563e-04 3.174112e-04 2.880570e-04 2.622568e-04 2.394865e-04
[31] 2.193120e-04 2.013721e-04 1.853643e-04 1.710339e-04 1.581656e-04
[36] 1.465766e-04 1.361108e-04 1.266345e-04 1.180327e-04 1.102061e-04
[41] 1.030687e-04 9.654571e-05 9.057193e-05 8.509029e-05 8.005073e-05
[46] 7.540921e-05 7.112686e-05 6.716930e-05 6.350603e-05 6.010996e-05
[51] 5.695693e-05 5.402539e-05 5.129602e-05 4.875153e-05 4.637637e-05
[56] 4.415654e-05 4.207942e-05 4.013359e-05 3.830874e-05 3.659551e-05
[61] 3.498539e-05 3.347066e-05 3.204428e-05 3.069982e-05 2.943141e-05
[66] 2.823371e-05 2.710177e-05 2.603111e-05 2.501757e-05 2.405735e-05
[71] 2.314694e-05
>
>
>
>
>
> dev.off()
null device
1
>