suppressPackageStartupMessages(library(dplyr))
library(ggplot2)
library(dplyr)
source('./code/likelihood-ugly.R')
# grab data, 372 observations# "week", "transect", "time", "cluster", "totalSpiders"## (this dataframe was generated by the likelihood-ugly.R function generateLikelihoodV2() )#source.url<- c("https://raw.githubusercontent.com/cordphelps/brm/master/data/clusterData.csv")
clusterData.df<- read.csv(source.url, header=TRUE, row.names=NULL)
How plausible is it that an oakMargin transect row will have more spiders than a control transect row?
# plot likelihood for all observations (24 hours), for daylight observations (collected in the 'pm'), and # for nighttime observations (collected in the 'am')#gg.likelihood<- generateLikelihoodV2(df=clusterData.df, daytime='24h')
## Loading required package: Rcpp
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## can be found by typing help('brms'). A more detailed introduction
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## To avoid recompilation of unchanged Stan programs, we recommend calling
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## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').
## Run theme_set(theme_default()) to use the default bayesplot theme.
## rstan (Version 2.17.3, GitRev: 2e1f913d3ca3)
## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
## Compiling the C++ model
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## Loading 'brms' package (version 2.6.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').
## Run theme_set(theme_default()) to use the default bayesplot theme.
## rstan (Version 2.17.3, GitRev: 2e1f913d3ca3)
## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
## Compiling the C++ model
## recompiling to avoid crashing R session
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