---
title: "Rosenzweig-MacArthur predator-prey model (Pineda-Krch et al., 2007)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Rosenzweig-MacArthur predator-prey model (Pineda-Krch et al., 2007)}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

<!-- github markdown built using 
rmarkdown::render("vignettes/rm_predator_prey.Rmd", output_format = "github_document")
-->

```{r, setseed, echo=FALSE}
set.seed(1)
knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
```

Rosenzweig-MacArthur predator-prey model (Pineda-Krch et al., 2007, Pineda-Krch, 2008)

```
dN/dt = r(1-N/K - alpha/(1+wN))NP
dP/dt = c*alpha/(1+wN))NP
```

This model has five reactions with the following per capita rates,
```
prey birth:     b
prey death:     d+(b-d)N/K
predation:      alpha/(1+wN)
predator birth: c*alpha/(1+wN)N
predator death: g
```

Propensity functions:
```
a1 = b * N
a2 = (d+(b-d)N/K) * N
a3 = alpha/(1+wN) * N * P
a4 = c*alpha/(1+wN) * N * P
a5 = g * P
```

Load package
```{r}
library(GillespieSSA)
```

Define parameters
```{r}
parms <- c(b=2, d=1, K=1000, alpha=0.005, 
           w=0.0025, c=2, g=2)
tf <- 10                                               # Final time
simName <- "Rosenzweig-MacArthur predator-prey model"  # Name
```

Define initial state vector
```{r}
x0  <- c(N=500, P=500)
```

Define state-change matrix
```{r}
nu  <- matrix(c(+1, -1, -1,  0,  0,
                 0,  0,  0, +1, -1),     
                 nrow=2,byrow=TRUE) 
```

Define propensity functions
```{r}
a <- c(
  "b*N",
  "(d+(b-d)*N/K)*N",
  "alpha/(1+w*N)*N*P",
  "c*alpha/(1+w*N)*N*P",
  "g*P"
) 
```

Run simulations with the Direct method
```{r direct}
set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.d(),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, show.title = TRUE, show.legend = FALSE)
```

Run simulations with the Explict tau-leap method
```{r etl}
set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.etl(tau = .01),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, show.title = TRUE, show.legend = FALSE)
```

Run simulations with the Binomial tau-leap method
```{r btl}
set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.btl(),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, show.title = TRUE, show.legend = FALSE)
```

Run simulations with the Optimized tau-leap method
```{r otl}
set.seed(1)
out <- ssa(
  x0 = x0,
  a = a,
  nu = nu,
  parms = parms,
  tf = tf,
  method = ssa.otl(),
  simName = simName,
  verbose = FALSE,
  consoleInterval = 1
) 
ssa.plot(out, show.title = TRUE, show.legend = FALSE)
```
