mbjoseph

# Multi-species dynamic occupancy model with R and JAGS
# Written by Max Joseph
# maxwell.b.joseph@colorado.edu
# see http://www.colorado.edu/eeb/gradstudents/joseph/community_occ.html
# for details

# convenience functions
logit <- function(x) {
  log(x/(1 - x))
}

mbjoseph

vioplot2 <- function (x, ..., range = 1.5, h = NULL, ylim = NULL, names = NULL, 
                      horizontal = FALSE, col = "magenta", border = "black", lty = 1, 
                      lwd = 1, rectCol = "black", colMed = "white", pchMed = 19, 
                      at, add = FALSE, wex = 1, drawRect = TRUE, side="both") 
{
  datas <- list(x, ...)
  n <- length(datas)
  if (missing(at)) 
    at <- 1:n
  upper <- vector(mode = "numeric", length = n)

mbjoseph

## Simple bivariate Poisson model in stan
## following parameterization in Karlis and Ntzoufras 2003
## Simulate data
n <- 50

# indpendent Poisson components
theta <- c(2, 3, 1)

X_i <- array(dim=c(n, 3))
for (i in 1:3){

mbjoseph

#===============================================================================
#  Date:    	May 22, 2016
#  Author:  	Lindy Nelson
#  Purpose: 	This script downloads monthly MODIS burn data from 
#  
#
#  FTP Server:  fuoco.geog.umd.edu
#  
#

mbjoseph

---
title: "Coloring lidar point clouds with RGB imagery in R"
author: "Max Joseph"
date: "June 30, 2016"
output:
  html_document:
    keep_md: true
---

```{r setup, echo = FALSE}

mbjoseph

# Script to get MCD14ML MODIS data
library(RCurl)
library(dplyr)

# fetch_MCD14ML() downloads all of the zipped data files for the
# MODIS MCD14ML data product
#
# args:
#   dir: the name of a directory to store the zip files (string)
#   overwrite: should existing data be overrwitten? (logical)

mbjoseph

---
title: "Your title here"
subtitle: a clever subtitle
author: "Your name"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  beamer_presentation:
    latex_engine: xelatex
    fonttheme: "structurebold"
header-includes: 

mbjoseph

# Importing data from USDA into large data file ---------------------------
library(stringr)
library(dplyr)

path_to_data <- "~/Desktop/ams_cattle_data/"
data_files <- list.files(path = path_to_data, pattern = "cattle", 
                         full.names = TRUE)

# Define helper functions -------------------------------------------------

mbjoseph

data {
  int<lower=1> n;      
  vector[n] x;
  vector[n] y;
}

parameters {
  real alpha;
  vector[2] beta;
  real<lower=0> sigma;

mbjoseph

# Simulation script for factor analysis ala Leung & Drton (2016) ----------
library(rstan)
library(bayesplot)

m <- 5 # dimension of observed data (e.g., # traits)
k <- 2 # number of latent factors
n <- 100 # number of sample units (e.g., # species)

# residual variance matrix (is diagonal)
Omega <- diag(.3 + abs(rnorm(m, sd = .3)))