jmbr

Introduction

jmbr (pronounced jimber) is an R package to facilitate analyses using Just Another Gibbs Sampler (JAGS).

It is part of the mbr family of packages.

Demonstration 

# define model in JAGS language
model <- model("model {
  alpha ~ dnorm(0, 10^-2)
  beta1 ~ dnorm(0, 10^-2)
  beta2 ~ dnorm(0, 10^-2)
  beta3 ~ dnorm(0, 10^-2)

  log_sAnnual ~ dnorm(0, 10^-2)
  log(sAnnual) <- log_sAnnual

  for(i in 1:nAnnual) {
    bAnnual[i] ~ dnorm(0, sAnnual^-2)
  }

  for (i in 1:length(Pairs)) {
    log(ePairs[i]) <- alpha + beta1 * Year[i] + beta2 * Year[i]^2 + beta3 * Year[i]^3 + bAnnual[Annual[i]]
    Pairs[i] ~ dpois(ePairs[i])
  }
}")

# add R code to calculate derived parameters
model <- update_model(model, new_expr = "
for (i in 1:length(Pairs)) {
  log(prediction[i]) <- alpha + beta1 * Year[i] + beta2 * Year[i]^2 + beta3 * Year[i]^3 + bAnnual[Annual[i]]
}")

# define data types and center year
model <- update_model(model, 
  select_data = list("Pairs" = integer(), "Year*" = integer(), Annual = factor()),
  derived = "sAnnual",
  random_effects = list(bAnnual = "Annual"))

data <- bauw::peregrine
data$Annual <- factor(data$Year)

set_analysis_mode("report")

# analyse
analysis <- analyse(model, data = data)
#> Registered S3 method overwritten by 'rjags':
#>   method               from 
#>   as.mcmc.list.mcarray mcmcr
#> # A tibble: 1 × 8
#>       n     K nchains niters nthin   ess  rhat converged
#>   <int> <int>   <int>  <int> <int> <int> <dbl> <lgl>    
#> 1    40     5       3    500     1     9  5.11 FALSE
analysis <- reanalyse(analysis)
#> # A tibble: 1 × 8
#>       n     K nchains niters nthin   ess  rhat converged
#>   <int> <int>   <int>  <int> <int> <int> <dbl> <lgl>    
#> 1    40     5       3    500     2    44  3.47 FALSE

coef(analysis, simplify = TRUE)
#> # A tibble: 5 × 5
#>   term        estimate  lower upper svalue
#>   <term>         <dbl>  <dbl> <dbl>  <dbl>
#> 1 alpha         4.25    3.05  4.35  10.6  
#> 2 beta1         1.16   -1.13  1.33   1.55 
#> 3 beta2        -0.0160 -0.205 0.419  0.520
#> 4 beta3        -0.254  -0.338 0.768  1.35 
#> 5 log_sAnnual  -2.10   -2.82  0.366  1.78

plot(analysis)

# make predictions by varying year with other predictors including the random effect of Annual held constant
year <- predict(analysis, new_data = "Year")

# plot those predictions
library(ggplot2)

ggplot(data = year, aes(x = Year, y = estimate)) +
  geom_point(data = bauw::peregrine, aes(y = Pairs)) +
  geom_line() +
  geom_line(aes(y = lower), linetype = "dotted") +
  geom_line(aes(y = upper), linetype = "dotted") +
  expand_limits(y = 0)

Installation

To install from GitHub

install.packages("devtools")
devtools::install_github("poissonconsulting/jmbr")

Citation 

To cite jmbr in publications use:

  Joe Thorley (2018) jmbr: Analyses Using JAGS. doi:
  https://doi.org/10.5281/zenodo.1162355.

A BibTeX entry for LaTeX users is

  @Misc{,
    author = {Joe Thorley},
    year = {2018},
    title = {jmbr: Analyses Using JAGS},
    doi = {https://doi.org/10.5281/zenodo.1162355},
  }

Please also cite JAGS.

Contribution

Please report any issues.

Pull requests are always welcome.

Code of Conduct

Please note that the jmbr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Inspiration