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######### These are my attempts to incorporate McCann's models into R to help me interpret these food web models ######################

library(deSolve)

### Section 2.2.5
# parameters and state variables for the R-M model
r <- 1.0 # per capita rate of increase in resource
K <- 2.0
e <- 0.7
Ro <- 0.5

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## simulation of experiment

## to test patterns of randomization

## first set up the relationships

## volume is a uniform variable


n.mathoms <- 30

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## a simulation of detecting effect size on gall parasitisim rates
## and occupancy.
## basically ask: how many larvae are in each gall (poisson)
## how many samples do you need to see the difference?
##

#first, a homemade function for the Zero-Inflated Negative Binomial distribution
rzinbinom <- function(n,mu,size,zprob){
  ifelse(runif(n)<zprob,0,rnbinom(n,mu=mu,size=size))
}

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# by Andrew MacDonald Nov 2011
## please add your own tips and tricks!

## the idea here is to demonstrate how for loops are used
## and then immediately demonstrate how they can be avoided.

## we'll begin by creating a fake dataset.
## This experiment is a 3x3 factorial with 2 replicates of each treatment combination.
## The scientist applied nitrogen and phosphorous to the soil
## then she measured two responses: plant growth and seed production.