Predictive Density of Gumbel

Description

Predictive density resulting of posterior distribution of Gumbel parameters.

Usage

predictive.gumbel(vector, data)

Arguments

Value

The program draws the predictive distribution of datased fitted using the posterior distribution of the Gumbel parameters.

Examples

# Vector of maxima return for each 15 days for ibovespa data
data(ibovespa)
postibv=posterior.gumbel(ibovespa[,4],15,1000)
datai=gev(ibovespa[,4],15)$data
predictive.gumbel(postibv,datai)

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.gumbel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predictive.gumbel
> ### Title: Predictive Density of Gumbel
> ### Aliases: predictive.gumbel
> 
> ### ** Examples
> 
> # Vector of maxima return for each 15 days for ibovespa data
> data(ibovespa)
> postibv=posterior.gumbel(ibovespa[,4],15,1000)
[1] 0.006666667
[1] 0.01333333
[1] 0.02
[1] 0.02666667
[1] 0.03333333
[1] 0.04
[1] 0.04666667
[1] 0.05333333
[1] 0.06
[1] 0.06666667
[1] 0.07333333
[1] 0.08
[1] 0.08666667
[1] 0.09333333
[1] 0.1
[1] 0.1066667
[1] 0.1133333
[1] 0.12
[1] 0.1266667
[1] 0.1333333
[1] 0.14
[1] 0.1466667
[1] 0.1533333
[1] 0.16
[1] 0.1666667
[1] 0.1733333
[1] 0.18
[1] 0.1866667
[1] 0.1933333
[1] 0.2
[1] 0.2066667
[1] 0.2133333
[1] 0.22
[1] 0.2266667
[1] 0.2333333
[1] 0.24
[1] 0.2466667
[1] 0.2533333
[1] 0.26
[1] 0.2666667
[1] 0.2733333
[1] 0.28
[1] 0.2866667
[1] 0.2933333
[1] 0.3
[1] 0.3066667
[1] 0.3133333
[1] 0.32
[1] 0.3266667
[1] 0.3333333
[1] 0.34
[1] 0.3466667
[1] 0.3533333
[1] 0.36
[1] 0.3666667
[1] 0.3733333
[1] 0.38
[1] 0.3866667
[1] 0.3933333
[1] 0.4
[1] 0.4066667
[1] 0.4133333
[1] 0.42
[1] 0.4266667
[1] 0.4333333
[1] 0.44
[1] 0.4466667
[1] 0.4533333
[1] 0.46
[1] 0.4666667
[1] 0.4733333
[1] 0.48
[1] 0.4866667
[1] 0.4933333
[1] 0.5
[1] 0.5066667
[1] 0.5133333
[1] 0.52
[1] 0.5266667
[1] 0.5333333
[1] 0.54
[1] 0.5466667
[1] 0.5533333
[1] 0.56
[1] 0.5666667
[1] 0.5733333
[1] 0.58
[1] 0.5866667
[1] 0.5933333
[1] 0.6
[1] 0.6066667
[1] 0.6133333
[1] 0.62
[1] 0.6266667
[1] 0.6333333
[1] 0.64
[1] 0.6466667
[1] 0.6533333
[1] 0.66
[1] 0.6666667
[1] 0.6733333
[1] 0.68
[1] 0.6866667
[1] 0.6933333
[1] 0.7
[1] 0.7066667
[1] 0.7133333
[1] 0.72
[1] 0.7266667
[1] 0.7333333
[1] 0.74
[1] 0.7466667
[1] 0.7533333
[1] 0.76
[1] 0.7666667
[1] 0.7733333
[1] 0.78
[1] 0.7866667
[1] 0.7933333
[1] 0.8
[1] 0.8066667
[1] 0.8133333
[1] 0.82
[1] 0.8266667
[1] 0.8333333
[1] 0.84
[1] 0.8466667
[1] 0.8533333
[1] 0.86
[1] 0.8666667
[1] 0.8733333
[1] 0.88
[1] 0.8866667
[1] 0.8933333
[1] 0.9
[1] 0.9066667
[1] 0.9133333
[1] 0.92
[1] 0.9266667
[1] 0.9333333
[1] 0.94
[1] 0.9466667
[1] 0.9533333
[1] 0.96
[1] 0.9666667
[1] 0.9733333
[1] 0.98
[1] 0.9866667
[1] 0.9933333
[1] 1
> datai=gev(ibovespa[,4],15)$data
> predictive.gumbel(postibv,datai)
  [1] 4.376590e-03 6.865629e-03 1.060802e-02 1.614712e-02 2.421896e-02
  [6] 3.580184e-02 5.217157e-02 7.496021e-02 1.062155e-01 1.484567e-01
 [11] 2.047225e-01 2.786043e-01 3.742611e-01 4.964078e-01 6.502749e-01
 [16] 8.415340e-01 1.076189e+00 1.360435e+00 1.700481e+00 2.102355e+00
 [21] 2.571688e+00 3.113491e+00 3.731934e+00 4.430143e+00 5.210016e+00
 [26] 6.072081e+00 7.015388e+00 8.037453e+00 9.134251e+00 1.030026e+01
 [31] 1.152854e+01 1.281089e+01 1.413799e+01 1.549962e+01 1.688488e+01
 [36] 1.828239e+01 1.968058e+01 2.106786e+01 2.243288e+01 2.376472e+01
 [41] 2.505303e+01 2.628826e+01 2.746170e+01 2.856564e+01 2.959338e+01
 [46] 3.053932e+01 3.139896e+01 3.216887e+01 3.284668e+01 3.343104e+01
 [51] 3.392155e+01 3.431870e+01 3.462377e+01 3.483877e+01 3.496635e+01
 [56] 3.500970e+01 3.497247e+01 3.485869e+01 3.467270e+01 3.441905e+01
 [61] 3.410245e+01 3.372771e+01 3.329965e+01 3.282309e+01 3.230281e+01
 [66] 3.174345e+01 3.114954e+01 3.052546e+01 2.987539e+01 2.920334e+01
 [71] 2.851308e+01 2.780818e+01 2.709199e+01 2.636762e+01 2.563797e+01
 [76] 2.490569e+01 2.417325e+01 2.344287e+01 2.271659e+01 2.199623e+01
 [81] 2.128344e+01 2.057969e+01 1.988626e+01 1.920429e+01 1.853477e+01
 [86] 1.787854e+01 1.723631e+01 1.660869e+01 1.599615e+01 1.539908e+01
 [91] 1.481778e+01 1.425244e+01 1.370320e+01 1.317012e+01 1.265319e+01
 [96] 1.215237e+01 1.166754e+01 1.119855e+01 1.074522e+01 1.030732e+01
[101] 9.884593e+00 9.476775e+00 9.083562e+00 8.704640e+00 8.339678e+00
[106] 7.988334e+00 7.650257e+00 7.325089e+00 7.012469e+00 6.712031e+00
[111] 6.423408e+00 6.146234e+00 5.880145e+00 5.624780e+00 5.379781e+00
[116] 5.144794e+00 4.919472e+00 4.703474e+00 4.496465e+00 4.298117e+00
[121] 4.108110e+00 3.926132e+00 3.751879e+00 3.585054e+00 3.425369e+00
[126] 3.272546e+00 3.126313e+00 2.986407e+00 2.852575e+00 2.724570e+00
[131] 2.602156e+00 2.485103e+00 2.373189e+00 2.266201e+00 2.163933e+00
[136] 2.066188e+00 1.972773e+00 1.883506e+00 1.798209e+00 1.716713e+00
[141] 1.638855e+00 1.564478e+00 1.493431e+00 1.425569e+00 1.360755e+00
[146] 1.298854e+00 1.239740e+00 1.183290e+00 1.129387e+00 1.077917e+00
[151] 1.028775e+00 9.818556e-01 9.370611e-01 8.942968e-01 8.534722e-01
[156] 8.145006e-01 7.772992e-01 7.417886e-01 7.078929e-01 6.755397e-01
[161] 6.446595e-01 6.151860e-01 5.870557e-01 5.602080e-01 5.345849e-01
[166] 5.101311e-01 4.867935e-01 4.645216e-01 4.432672e-01 4.229838e-01
[171] 4.036276e-01 3.851562e-01 3.675295e-01 3.507091e-01 3.346581e-01
[176] 3.193415e-01 3.047259e-01 2.907793e-01 2.774711e-01 2.647722e-01
[181] 2.526548e-01 2.410922e-01 2.300592e-01 2.195315e-01 2.094861e-01
[186] 1.999008e-01 1.907546e-01 1.820273e-01 1.736999e-01 1.657540e-01
[191] 1.581722e-01 1.509377e-01 1.440346e-01 1.374478e-01 1.311628e-01
[196] 1.251657e-01 1.194433e-01 1.139831e-01 1.087730e-01 1.038015e-01
[201] 9.905775e-02 9.453127e-02 9.021209e-02 8.609071e-02 8.215806e-02
[206] 7.840549e-02 7.482472e-02 7.140789e-02 6.814748e-02 6.503631e-02
[211] 6.206754e-02 5.923464e-02 5.653138e-02 5.395181e-02 5.149027e-02
[216] 4.914133e-02 4.689985e-02 4.476088e-02 4.271974e-02 4.077194e-02
[221] 3.891319e-02 3.713943e-02 3.544675e-02 3.383143e-02 3.228994e-02
[226] 3.081889e-02 2.941505e-02 2.807534e-02 2.679683e-02 2.557672e-02
[231] 2.441232e-02 2.330109e-02 2.224060e-02 2.122852e-02 2.026263e-02
[236] 1.934082e-02 1.846107e-02 1.762147e-02 1.682016e-02 1.605541e-02
[241] 1.532553e-02 1.462893e-02 1.396409e-02 1.332957e-02 1.272396e-02
[246] 1.214595e-02 1.159429e-02 1.106775e-02 1.056521e-02 1.008555e-02
[251] 9.627738e-03 9.190772e-03 8.773700e-03 8.375614e-03 7.995647e-03
[256] 7.632972e-03 7.286799e-03 6.956375e-03 6.640982e-03 6.339934e-03
[261] 6.052576e-03 5.778283e-03 5.516460e-03 5.266539e-03 5.027976e-03
[266] 4.800253e-03 4.582878e-03 4.375377e-03 4.177301e-03 3.988220e-03
[271] 3.807725e-03 3.635425e-03 3.470946e-03 3.313933e-03 3.164045e-03
[276] 3.020958e-03 2.884362e-03 2.753962e-03 2.629476e-03 2.510635e-03
[281] 2.397183e-03 2.288874e-03 2.185473e-03 2.086759e-03 1.992518e-03
[286] 1.902546e-03 1.816650e-03 1.734645e-03 1.656353e-03 1.581606e-03
[291] 1.510243e-03 1.442110e-03 1.377060e-03 1.314955e-03 1.255659e-03
[296] 1.199045e-03 1.144992e-03 1.093384e-03 1.044109e-03 9.970620e-04
[301] 9.521415e-04
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>