jebyrnes

library(tidyverse)
library(gapminder)
library(broom)
library(ggplot2)

life_exp_mods <- gapminder %>%
  
  #do this for each country
  group_by(country) %>%
  

jebyrnes

library(tidyverse)
library(ggplot2)
library(visreg)

#some fake data
set.seed(35)
my_data <- data.frame(x1 = runif(100,-50,50), x2 = runif(100, -50, 50)) %>%
  mutate(y = rnorm(100, 0.001*x2*(x1 - x1^2), 100))

#let's see that data

jebyrnes

Traceback (most recent call last):
  File "/usr/local/bin/gdal_polygonize.py", line 36, in <module>
    import gdal, ogr, osr
  File "/Library/Frameworks/GDAL.framework/Versions/1.11/Python/2.7/site-packages/gdal.py", line 2, in <module>
    from osgeo.gdal import deprecation_warn
  File "/Library/Frameworks/GDAL.framework/Versions/1.11/Python/2.7/site-packages/osgeo/__init__.py", line 21, in <module>
    _gdal = swig_import_helper()
  File "/Library/Frameworks/GDAL.framework/Versions/1.11/Python/2.7/site-packages/osgeo/__init__.py", line 17, in swig_import_helper
    _mod = imp.load_module('_gdal', fp, pathname, description)
ImportError: dlopen(/Library/Frameworks/GDAL.framework/Versions/1.11/Python/2.7/site-packages/osgeo/_gdal.so, 2): Symbol not found: _GTIFAllocDefn

jebyrnes

###########
# Libraries
###########

library(sp)
library(rgdal)
library(rgeos)
library(raster)
library(tidyverse)
library(spdplyr)

jebyrnes

library(tidyverse)
library(broom)
library(sp)
library(spdplyr)
library(ggplot2)
library(rgdal)
library(raster)
library(rgeos)
library(lubridate)

jebyrnes

library(tidyverse)
library(broom)
library(sp)
library(spdplyr)
library(ggplot2)
library(ggmap)
library(rgdal)
library(raster)

#read in spatialPolygonsDataFrame (shapefile)

jebyrnes

---
title: "Bayesian SEM with BRMS"
author: "Jarrett Byrnes"
date: "12/20/2017"
output: html_document
---

```{r setup, include=FALSE}
#http://sites.tufts.edu/emotiononthebrain/2017/08/12/blog-posting-from-r-markdown-to-wordpress/
knitr::opts_chunk$set(echo = TRUE)

jebyrnes

#from rethinking library for numerically stable log sums
log_sum_exp <- function (x) {
  xmax <- max(x)
  xsum <- sum(exp(x - xmax))
  xmax + log(xsum)
}
  

#function for WAIC from an LM
waic.lm <- function(mod, n.sims=1e3){

jebyrnes

library(rethinking)

#make a multivariate vector with known correlation
sigma_mat <- matrix(c(1,.3, 0.2,
                      0.3, 1, 0.4,
                      0.2, 0.4, 1), nrow=3)
z <- rmvnorm(1, c(0,0, 0), sigma_mat)
z_2 <- z[2]
z[2] <- NA

jebyrnes

kelp_mod_noyear <- alist(
  kelp_s ~ dnorm(mu, sigma),
  
  mu <- a[time_idx],  
  a[time_idx] ~ GPL2( Dmat , etasq , rhosq , delta_sq),
  
  sigma ~ dcauchy(0,5),
  etasq ~ dcauchy(0,2),
  delta_sq ~ dcauchy(0,2),