bearloga

#!/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

mike-lawrence

data{
	# n_subj: number of subjects
	int n_subj ;
	# n_y: number of outcomes
	int n_y ;
	# y: matrix of outcomes
	matrix[n_subj,n_y] y ;
	# n_fac: number of latent factors
	int n_fac ;
	# y_fac: list of which factor is associated with each outcome

emhart

library("dplyr")
library("audio")
notes <- c(A = 0, B = 2, C = 3, D = 5, E = 7, F = 8, G = 10)
pitch <- "D D E D G F# D D E D A G D D D5 B G F# E C5 C5 B G A G"
duration <- c(rep(c(0.75, 0.25, 1, 1, 1, 2), 2),
              0.75, 0.25, 1, 1, 1, 1, 1, 0.75, 0.25, 1, 1, 1, 2)
bday <- data_frame(pitch = strsplit(pitch, " ")[[1]],
                   duration = duration)

MatsuuraKentaro

data {
  int N;
  int M;
  real<lower=0> Y[N];
}

parameters {
  real<lower=0> mu;
  real<lower=0> phi;
  real<lower=1, upper=2> theta;

davharris

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)

lauratboyer

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

mabarbour

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

### 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 <- 0.5
Ro <- 0.5
m <- 0.5
a <- 1.6