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#!/bin/bash

# Usage: mkproj [projectname]
# projectname defaults to name of current directory

template="Version: 1.0\nRestoreWorkspace: Default\nSaveWorkspace: Default\nAlwaysSaveHistory: Default\n\nEnableCodeIndexing: Yes\nUseSpacesForTab: Yes\nNumSpacesForTab: 4\nEncoding: UTF-8\n\nRnwWeave: knitr\nLaTeX: pdfLaTeX"

wd=$(basename `pwd`)

if [ -z $1 ]; then

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## Empty list -- just use the empty environment for this.
nil <- function() {
  emptyenv()
}

## Test if a list is the empty list:
is_empty <- function(lis) {
  identical(lis, nil())
}

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set.seed(1)

# Create fake data
x = runif(100, 0, 5)
y = .25 * x^3 - x^2 + rnorm(length(x))
data = data.frame(x = x, y = y)

# Identify 500 points to include in the plots
x.sequence = seq(0, 5, length = 500)

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

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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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## 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 ([email protected])
## 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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######### 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