mbjoseph

library(tidyverse)
library(sf)
library(here)
library(brms)
library(lubridate)
library(reshape2)

# Get ecoregion data ------------------------------------------------------
download.file("ftp://newftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/us_eco_l4.zip", 
              destfile = "ecoregions.zip")

mbjoseph

library(rstan)
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)

psi <- .2
theta <- .3
p <- .5

nsite <- 100
ntime <- 4

mbjoseph

## DATA GENERATION
# reproducibility
library(bayesplot)
set.seed(1839)

# matches the stan function
inv_logit <- function(x){
  exp(x) / (1 + exp(x))
}

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mbjoseph

data {
  int<lower=1> n_site;
  int<lower=1> n_distance_bins;
  vector[n_distance_bins + 1] bin_breakpoints;
  array[n_site, n_distance_bins] int y;
  array[n_site] int n_obs;
}

transformed data {
  real max_distance = max(bin_breakpoints);

mbjoseph

library(forcats)
library(ggplot2)
library(testthat)
path <- testthat_example("success")

l <- test_file(path, reporter = "list")

l %>%
  as.data.frame() %>%
  ggplot(aes(y = fct_reorder(test, real), x = real)) + 

mbjoseph

---
title: "Using earthpy via R Markdown"
author: "Max Joseph"
date: "December 18, 2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(reticulate)

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)

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# Distance sampling model with data augmentation. 
# Based on section 8.3.1 of the Applied Hierarchical Modeling book by Royle and Kery

library(rstan)

B <- 50

# note that I'm dividing by 10 and adding 1e-6 to put this on a manageable
# scale and prevent values == 0
d_obs <- c(71.93, 26.05, 58.47, 92.35, 163.83, 84.52, 163.83, 157.33,

mbjoseph