# mixture.sim: Simulation Of A Mixture Of Two Normal Or Gamma Distributions In charlottedion/mixedsde: Estimation Methods for Stochastic Differential Mixed Effects Models

## Description

Simulation of M random variables from a mixture of two Gaussian or Gamma distributions

## Usage

 `1` ```mixture.sim(M, density.phi, param) ```

## Arguments

 `M` number of simulated variables `density.phi` name of the chosen density 'mixture.normal' or 'mixture.gamma' `param` vector of parameters with the proportion of mixture of the two distributions and means and standard-deviations of the two normal or shapes and scales of the two Gamma distribution

## Details

If 'mixture.normal', the distribution is p N(μ1,σ1^2) + (1-p)N(μ2, σ2^2)

and param=c(p, μ1, σ1, μ2, σ2)

If 'mixture.gamma', the distribution is p Gamma(shape1,scale1) + (1-p)Gamma(shape2,scale2)

and param=c(p, shape1, scale1, shape2, scale2)

## Value

 `Y` vector of simulated variables

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```density.phi <- 'mixture.gamma' param <- c(0.2,1.8,0.5,5.05,1); M <- 200 gridf <- seq(0, 8, length = 200) f <- param * 1/gamma(param) * (gridf)^(param-1) * exp(-(gridf) / param) / param^param + (1-param) * 1/gamma(param) * (gridf)^(param-1) * exp(-(gridf) / param) / param^param Y <- mixture.sim(M, density.phi, param) hist(Y) lines(gridf, f) ```

charlottedion/mixedsde documentation built on May 13, 2019, 3:35 p.m.