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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "tools/README-"
)
```
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://github.com/poissonconsulting/jmbr/actions/workflows/R-CMD-check.yaml)
[](https://codecov.io/gh/poissonconsulting/jmbr?branch=master)
[](https://opensource.org/license/mit/)
<!-- badges: end -->
# jmbr
## Introduction
`jmbr` (pronounced jimber) is an R package to facilitate analyses using Just Another Gibbs Sampler ([`JAGS`](http://mcmc-jags.sourceforge.net)).
It is part of the [mbr](https://github.com/poissonconsulting/mbr) family of packages.
## Demonstration
```{r, message = FALSE}
library(jmbr)
library(mbr)
```
```{r}
# define model in JAGS language
model <- model("model {
alpha ~ dnorm(0, 10^-2)
beta1 ~ dnorm(0, 10^-2)
beta2 ~ dnorm(0, 10^-2)
beta3 ~ dnorm(0, 10^-2)
log_sAnnual ~ dnorm(0, 10^-2)
log(sAnnual) <- log_sAnnual
for(i in 1:nAnnual) {
bAnnual[i] ~ dnorm(0, sAnnual^-2)
}
for (i in 1:length(Pairs)) {
log(ePairs[i]) <- alpha + beta1 * Year[i] + beta2 * Year[i]^2 + beta3 * Year[i]^3 + bAnnual[Annual[i]]
Pairs[i] ~ dpois(ePairs[i])
}
}")
# add R code to calculate derived parameters
model <- update_model(model, new_expr = "
for (i in 1:length(Pairs)) {
log(prediction[i]) <- alpha + beta1 * Year[i] + beta2 * Year[i]^2 + beta3 * Year[i]^3 + bAnnual[Annual[i]]
}")
# define data types and center year
model <- update_model(model,
select_data = list("Pairs" = integer(), "Year*" = integer(), Annual = factor()),
derived = "sAnnual",
random_effects = list(bAnnual = "Annual")
)
data <- bauw::peregrine
data$Annual <- factor(data$Year)
set_analysis_mode("report")
# analyse
analysis <- analyse(model, data = data)
analysis <- reanalyse(analysis)
coef(analysis, simplify = TRUE)
plot(analysis)
```
```{r, message = FALSE}
# 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
```{r, comment="", echo=FALSE}
citation(package = "jmbr")
```
## Contribution
Please report any [issues](https://github.com/poissonconsulting/jmbr/issues).
[Pull requests](https://github.com/poissonconsulting/jmbr/pulls) are always welcome.
## Code of Conduct
Please note that the jmbr project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
## Inspiration
- [jaggernaut](https://github.com/poissonconsulting/jaggernaut)