# prior: Construction of Prior Distributions In evdbayes: Bayesian Analysis in Extreme Value Theory

## Description

Constructing prior distibutions for the location, scale and shape parameters using normal, beta or gamma distributions. A linear trend for the location can also be specified, using a prior normal distribution centered at zero for the trend parameter.

## Usage

 ```1 2 3 4``` ```prior.prob(quant, alpha, trendsd = 0) prior.quant(prob = 10^-(1:3), shape, scale, trendsd = 0) prior.norm(mean, cov, trendsd = 0) prior.loglognorm(mean, cov, trendsd = 0) ```

## Arguments

 `quant, alpha` Numeric vectors of length three and four respectively. Beta prior distibutions are placed on probability ratios corresponding to the quantiles given in `quant`. `prob, shape, scale` Numeric vectors of length three. Gamma prior distibutions, with parameters `shape` and `scale`, are placed on quantile differences corresponding to the probabilities given in `prob`. `mean, cov` The prior distibution for the location, log(scale) and shape is taken to be trivariate normal, with mean `mean` (a numeric vector of length three) and covariance matrix `cov` (a symmetric positive definite three by three matrix). `trendsd` The standard deviation for the marginal normal prior distribution (with mean zero) placed on the linear trend parameter for the location. If this is zero (the default) a linear trend is not implemented.

## Details

See the user's guide.

## Value

Returns an object of class `"evprior"`, which is essentially just a list of the arguments passed.

## See Also

`posterior`, `pplik`

## Examples

 ```1 2 3 4``` ```mat <- diag(c(10000, 10000, 100)) prior.norm(mean = c(0,0,0), cov = mat, trendsd = 10) prior.quant(shape = c(38.9,7.1,47), scale = c(1.5,6.3,2.6)) prior.prob(quant = c(85,88,95), alpha = c(4,2.5,2.25,0.25)) ```

evdbayes documentation built on May 29, 2017, 8:36 p.m.