kenkellner

library(unmarked)
library(shiny)
library(shinymanager)

credentials <- data.frame(
  user = ********,
  password = *********,
  stringsAsFactors = FALSE
)

kenkellner

cat("Applying occuMulti maxOrder patch\n")
fl_getY <- unmarked:::fl_getY

setMethod("fl_getY", "unmarkedFitOccuMulti", function(fit, ...){
  maxOrder <- fit@call$maxOrder
  if(is.null(maxOrder)) maxOrder <- length(fit@data@ylist)
  getDesign(getData(fit), fit@detformulas, fit@stateformulas, maxOrder)$y
})

name_to_ind <- unmarked:::name_to_ind

kenkellner

get_shapefile <- function(path){
  temp_dir <- tempdir()
  dest_dir <- tempfile()
  dir.create(dest_dir)

  if(grepl('http',path)){
    dest <- paste0(dest_dir,"/",basename(path))
    utils::download.file(path, dest, quiet=T)
    path <- dest
  }

kenkellner

#Streamline process of reading in CSV and Shapefile data
#from Buffalo Open Data Portal (via Socrata API)
#
#Requires RSocrata, tidyverse, and sf
#
#read_bufdata() with no arguments for a list of possible dataset titles

read_bufdata <- function(title=NULL, format='CSV'){

  if ( ! format %in% c('CSV', 'csv', 'Shapefile', 'shapefile') ){

kenkellner

#Get a list of all individual text files
fnames <- list.files(pattern='\\.txt')

#Get number of individuals
ninds <- length(fnames)

#Make blank output data frame
output <- as.data.frame(matrix(NA,nrow=ninds,ncol=6))
names(output) <- c('ID','PctSafe','PctRisky','Switches','FinalState','Fate')

kenkellner

#There isn't really an easy way to do this, unfortunately.

#In your stated example, the populations are ordered, so you should be able to calculate the mean in JAGS like this:

Pop1.mean <- mean(data[1:3,1:10])
Pop2.mean <- mean(data[4:6,1:10])

#but maybe your actual dataset isn't organized that way, and even if it was you'd have to 
#manually go through the dataset to figure out the cutoffs.

kenkellner

###########################################################
######                                              #######
######          GET ACTUAL START/STOP TIMES         #######
######                                              #######
###########################################################

#Read in input file
#Should be called startstop.csv and should be in same directory as the .dat files
#File should have three columns labeled start, stop, and plot (for plot names)
#Dates in start stop should be formatted e.g. 7/16/2014 13:12

kenkellner

#############################################################
## Permutation test for interaction between 2 main effects ##
#############################################################

#Based on http://r.789695.n4.nabble.com/Randomization-of-a-two-way-ANOVA-td874581.html

#Read in data
data <- read.csv('final.project.data.csv',h=T)

#Full model with interaction

kenkellner

#Manual calc of odds ratio:

#Pyrantel: 10 taken / 29 left
#Control 12 taken / 9 left

(10/29)/(12/9) #0.2586

1/((10/29)/(12/9)) #3.87 (more intuitive)

log(0.2586) #On log scale -1.3524

kenkellner

#How many simulations?
niter=100000

#Create matrix to hold final drawings (final)
#and vector to hold whether or not person "A" (Jeremy) won in a particular simulation (test)

final <- matrix(NA,nrow=niter,ncol=3)
test <- vector(length=niter)

#Iterate through $niter simulations one at a time in a {for} loop