# RFdistr: Evaluating distribution families In RandomFields: Simulation and Analysis of Random Fields

## Description

Through `RRdistr` distribution families can be passed to RandomFields to create distributions available in the `RMmodel` definitions.

## Usage

 ```1 2 3 4 5``` ```RFddistr(model, x, params, dim=1, ...) RFpdistr(model, q, params, dim=1, ...) RFqdistr(model, p, params, dim=1, ...) RFrdistr(model, n, params, dim=1, ...) RFdistr(model, x, q, p, n, params, dim=1, ...) ```

## Arguments

 `model,params` an `RRmodel`. `x` the location where the density is evaluated `q` the location where the probability function is evaluated `p` the value where the quantile function is evaluated `n` the number of random values to be drawn `dim` the dimension of the vector to be drawn `...` for advanced use: further options and control arguments for the simulation that are passed to and processed by `RFoptions`

## Details

`RFdistr` is the generic function for the 4 functions belonging to a distribution.

## Value

as described in the arguments

`RRgauss`, RR
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again ## a very toy example to understand the use model <- RRdistr(norm()) v <- 0.5 Print(RFdistr(model=model, x=v), dnorm(x=v)) Print(RFdistr(model=model, q=v), pnorm(q=v)) Print(RFdistr(model=model, p=v), qnorm(p=v)) n <- 10 r <- RFdistr(model=model, n=n, seed=0) set.seed(0); Print(r, rnorm(n=n)) ## note that a conditional covariance function given the ## random parameters is given here: model <- RMgauss(scale=exp()) for (i in 1:3) { RFoptions(seed = i + 10) readline(paste("Model no.", i, ": press return", sep="")) plot(model) readline(paste("Simulation no.", i, ": press return", sep="")) plot(RFsimulate(model, x=seq(0,10,0.1))) } ```