# stepLVc: A function for advancing the state of a Lotka-Volterra model... In smfsb: Stochastic Modelling for Systems Biology

## Description

A function for advancing the state of a Lotka-Volterra model by calling some C code implementing the Gillespie algorithm. The function can be used in conjunction with other functions (such as `simTs`) for simulating realisations of Lotka-Volterra models. Should be functionally identical to the function obtained by `data(spnModels)`, `stepLV=StepGillespie(LV)`, but much faster.

## Usage

 `1` ```stepLVc(x0,t0,deltat,th=c(1,0.005,0.6)) ```

## Arguments

 `x0` A vector representing the state of the system at the initial time, `t0`. `t0` The time corresponding to the initial state, `x0`. `deltat` The time in advance of the initial time at which the new state is required. `th` A vector of length 3 representing the rate constants associated with the 3 LV reactions. Defaults to `c(1,0.005,0.6)`.

## Value

A 2-vector representing the new state of the LV system.

`StepGillespie`, `spnModels`, `simTs`, `simSample`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# load the LV model data(spnModels) # create a stepping function stepLV = StepGillespie(LV) # step the function print(stepLV(c(x1=50,x2=100),0,1)) # simulate a realisation of the process and plot it out = simTs(c(x1=50,x2=100),0,100,0.1,stepLV) plot(out) # now use "stepLVc" instead... out = simTs(c(x1=50,x2=100),0,100,0.1,stepLVc) plot(out) ```