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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.

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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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## 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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######### 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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## file-scan.r
## Function to find a pattern in files with user-defined extension (.r by default)
## Returns file name and approximate row matches if positive.
## -------------------------------------------------------
## Author: Laura Tremblay-Boyer (l.boyer@fisheries.ubc.ca)
## Written on: March 26, 2013
## Time-stamp: <2013-03-26 16:19:28 Laura>

file.scan <- function(pattern, dir=getwd(), fpattern=NA, ftype=".r") {

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library(lme4)
library(ggplot2)

#create some levels
levs <- as.factor(c("l1","l2","l3","l4","l5"))

#set the factor means
f_means <- c(6,16,2,10,13)

# set individual as a factor

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getmatch <<- function(x,str2match,...) {
          unlist(regmatches(x,regexpr(str2match,x,...))) }

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x1 <- rnorm(100,5,sd=2)
x2 <- rnorm(200,15,3)
x3 <- c(x1,x2)

## calculate the density function 
den_x <- density(x3)]
## this can be plotted:
plot(den_x)

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eps <- function(file, onefile=FALSE, width=5.5, height = 5.5, 	horizontal=FALSE,paper="special", ...){
  		postscript(file=file,width=width,height=height,onefile=onefile,horizontal=horizontal,paper=paper,title=file,...)
  		par(bty='l')
		}

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## make a fake data frame
test <- data.frame(foo=gl(n=3,k=2,labels=c("low","med","high")),bar=rep(letters[1:2],3),zoop=runif(6))

## these are all factors
str(test)

## I want them to be characters, not factors, but I am too lazy to run as.character on every categorical factor

library(plyr)
test_char <- catcolwise(as.character)(test)