# normalp: Posterior Distribution with Normal Density In MCMC4Extremes: Posterior Distribution of Extreme Value Models in R

## Description

MCMC runs of posterior distribution of data with `Normal(mu,1/tau)` density, where `tau` is the inverse of variance.

## Usage

 `1` ```normalp(data, int=1000) ```

## Arguments

 `data` data vector `int` number of iteractions selected in MCMC. The program selects 1 in each 10 iteraction, then `thin=10`. The first `thin*int/3` iteractions is used as burn-in. After that, is runned `thin*int` iteraction, in which 1 of thin is selected for the final MCMC chain, resulting the number of int iteractions

## Value

An object of class `gumbelp` that gives a list containing the points of posterior distributions of `mu` and `tau` of the normal distribution, the data, mean posterior, median posterior and the credibility interval of the parameters.

## Note

The non-informative prior distribution of these parameters are `Normal(0,10000000)` for the parameter mu and `Gamma(0.001,0.001)` for the parameter `tau` . During the MCMC runs, screen shows the proportion of iteractions made.

`plot.normalp`

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```# Obtaining posterior distribution of a vector of simulated points x=rnorm(300,2,sqrt(10)) # Obtaning 1000 points of posterior distribution ajuste=normalp(x, 200) # Posterior distribution of river Nile dataset ## Not run: data(Nile) ## Not run: postnile=normalp(Nile,1000) ```

### Example output

```Loading required package: evir
[1] 0.03333333
[1] 0.06666667
[1] 0.1
[1] 0.1333333
[1] 0.1666667
[1] 0.2
[1] 0.2333333
[1] 0.2666667
[1] 0.3
[1] 0.3333333
[1] 0.3666667
[1] 0.4
[1] 0.4333333
[1] 0.4666667
[1] 0.5
[1] 0.5333333
[1] 0.5666667
[1] 0.6
[1] 0.6333333
[1] 0.6666667
[1] 0.7
[1] 0.7333333
[1] 0.7666667
[1] 0.8
[1] 0.8333333
[1] 0.8666667
[1] 0.9
[1] 0.9333333
[1] 0.9666667
[1] 1
```

MCMC4Extremes documentation built on May 1, 2019, 8:50 p.m.