# evmc: Simulate Markov Chains With Extreme Value Dependence... In evd: Functions for Extreme Value Distributions

## Description

Simulation of first order Markov chains, such that each pair of consecutive values has the dependence structure of one of nine parametric bivariate extreme value distributions.

## Usage

 ```1 2 3``` ```evmc(n, dep, asy = c(1,1), alpha, beta, model = c("log", "alog", "hr", "neglog", "aneglog", "bilog", "negbilog", "ct", "amix"), margins = c("uniform","rweibull","frechet","gumbel")) ```

## Arguments

 `n` Number of observations. `dep` Dependence parameter for the logistic, asymmetric logistic, Husler-Reiss, negative logistic and asymmetric negative logistic models. `asy` A vector of length two, containing the two asymmetry parameters for the asymmetric logistic and asymmetric negative logistic models. `alpha, beta` Alpha and beta parameters for the bilogistic, negative bilogistic, Coles-Tawn and asymmetric mixed models. `model` The specified model; a character string. Must be either `"log"` (the default), `"alog"`, `"hr"`, `"neglog"`, `"aneglog"`, `"bilog"`, `"negbilog"`, `"ct"` or `"amix"` (or any unique partial match), for the logistic, asymmetric logistic, Husler-Reiss, negative logistic, asymmetric negative logistic, bilogistic, negative bilogistic, Coles-Tawn and asymmetric mixed models respectively. The definition of each model is given in `rbvevd`. If parameter arguments are given that do not correspond to the specified model those arguments are ignored, with a warning. `margins` The marginal distribution of each value; a character string. Must be either `"uniform"` (the default), `"rweibull"`, `"frechet"` or `"gumbel"` (or any unique partial match), for the uniform, standard reverse Weibull, standard Gumbel and standard Frechet distributions respectively.

## Value

A numeric vector of length `n`.

`marma`, `rbvevd`

## Examples

 ```1 2``` ```evmc(100, alpha = 0.1, beta = 0.1, model = "bilog") evmc(100, dep = 10, model = "hr", margins = "gum") ```

### Example output

```  [1] 0.22535212 0.15715143 0.21256015 0.22058824 0.25330570 0.29727715
[7] 0.27971920 0.30311910 0.34990547 0.26640911 0.29985679 0.25833195
[13] 0.26679564 0.28273415 0.33072016 0.24729353 0.27882566 0.14757000
[19] 0.16865205 0.11194485 0.12611968 0.12396104 0.13183754 0.08308394
[25] 0.12800603 0.23903568 0.23604279 0.17037117 0.10684468 0.08668579
[31] 0.11976214 0.11898219 0.20872094 0.11105640 0.11900752 0.14357974
[37] 0.12336118 0.10762519 0.10181286 0.07886083 0.08721942 0.08197737
[43] 0.03870585 0.05004468 0.04440667 0.05800825 0.05967323 0.12772690
[49] 0.26477148 0.32201754 0.10182478 0.14143418 0.11618089 0.11505582
[55] 0.09715476 0.06515135 0.08912352 0.10681991 0.11540227 0.10385443
[61] 0.09222833 0.08205286 0.07318836 0.07561305 0.03486264 0.06484435
[67] 0.07344496 0.06555828 0.03565864 0.03409975 0.01715101 0.01426321
[73] 0.01480331 0.01112404 0.02692261 0.05457642 0.03996328 0.03448280
[79] 0.03354613 0.02754254 0.02104980 0.01977294 0.01140048 0.01137600
[85] 0.03021658 0.01777483 0.01340932 0.16075517 0.10522786 0.13621765
[91] 0.15399195 0.15789554 0.13770494 0.19635639 0.19669379 0.19414984
[97] 0.21801794 0.47102453 0.50902243 0.46966128
[1] 1.792726 2.074428 2.054864 2.162692 2.000053 2.157921 2.378670 2.221457
[9] 1.989869 1.814650 2.097859 2.280393 2.518226 2.751558 2.746162 2.985727
[17] 2.996626 3.027700 2.831430 2.562243 2.641778 2.661751 2.698034 3.430932
[25] 3.514566 3.756141 3.895851 3.723356 3.803344 4.054237 4.025193 4.106481
[33] 3.959528 3.921070 3.985273 4.214100 4.097299 3.797324 4.138780 4.128831
[41] 3.843219 4.070280 4.286090 4.351686 4.127076 3.971123 4.443896 4.259858
[49] 4.372567 4.180044 4.199320 4.428227 4.677724 4.710152 4.583216 4.562048
[57] 4.993842 5.096783 5.213336 5.132983 4.953541 5.116368 5.061399 4.762243
[65] 4.678276 4.582025 4.117019 3.781368 3.604430 3.580334 3.491994 3.314828
[73] 3.495452 3.574943 3.577436 3.695517 3.812083 3.532135 3.532191 3.517594
[81] 3.553391 3.141269 3.033873 2.419846 2.512226 2.689055 2.716792 2.574501
[89] 2.979582 3.024674 3.240753 3.023901 3.000034 2.905543 2.728379 2.774679
[97] 2.672528 2.560752 2.211281 2.133363
```

evd documentation built on May 1, 2019, 10:11 p.m.