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https://github.com/psolymos/bsims
Bird Point Count Simulator
https://github.com/psolymos/bsims
bias birds detectability shiny simulation survey-design
Last synced: 3 months ago
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Bird Point Count Simulator
- Host: GitHub
- URL: https://github.com/psolymos/bsims
- Owner: psolymos
- Created: 2019-05-16T03:12:02.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-21T16:12:04.000Z (9 months ago)
- Last Synced: 2024-05-23T02:04:55.378Z (9 months ago)
- Topics: bias, birds, detectability, shiny, simulation, survey-design
- Language: R
- Homepage: https://peter.solymos.org/bSims/
- Size: 57.4 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
output:
md_document:
variant: gfm
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
set.seed(0)
```
# bSims: Bird Point Count Simulator
A highly scientific and utterly addictive bird point count simulator to test statistical assumptions and to aid survey design.
[](https://CRAN.R-project.org/package=bSims)
[](https://peter.solymos.org/bSims/)
[](https://github.com/psolymos/bSims/actions/workflows/check.yml)
> _"I've yet to see any problem, however complicated, which when you looked at it the right way didn't become still more complicated."_
> -- Poul Anderson, Call Me Joe
> *“Love the simulation we're dreaming in”* - Dua Lipa, Physical
The goal of the package is to:
- test statistical assumptions,
- aid survey design,
- and have fun while doing it!
Design objectives:
- small (point count) scale implementation,
- habitat is considered homogeneous except for edge effects,
- realistic but efficient implementation of biological mechanisms and observation process,
- defaults chosen to reflect common practice and assumptions,
- extensible (PRs are welcome).
See the package in action in the [**QPAD
Book**](https://peter.solymos.org/qpad-book/).
Check out the [**QPAD workshop**](https://peter.solymos.org/qpad-workshop/).
Read/cite the paper [**Agent-based simulations improve abundance estimation**](https://rdcu.be/doDwI) (DOI 10.1007/s42977-023-00183-2).
## Install
CRAN version:
```{r eval=FALSE}
install.packages("bSims")
```
Development version:
```{r eval=FALSE}
remotes::install_github("psolymos/bSims")
```
See what is new in the [NEWS](NEWS.md) file.
## License
[GPL-2](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html)
Please cite (see `citation("bSims")`) the paper:
Solymos, P. 2023. Agent-based simulations improve abundance estimation. _Biologia Futura_ 74, 377--392 [DOI 10.1007/s42977-023-00183-2](https://doi.org/10.1007/s42977-023-00183-2), [link to PDF](https://rdcu.be/doDwI).
## Contributing
Feedback and contributions are welcome:
- submit feature request or report issues [here](https://github.com/psolymos/bSims/issues),
- fork the project and submit pull request, see [CoC](CODE_OF_CONDUCT.md).
## Examples
### Command line
```{r message=FALSE,warning=FALSE}
library(bSims)
phi <- 0.5
tau <- 1:3
dur <- 10
rbr <- c(0.5, 1, 1.5, Inf)
tbr <- c(3, 5, 10)
l <- bsims_init(10, 0.5, 1)
p <- bsims_populate(l, 1)
a <- bsims_animate(p, vocal_rate=phi, duration=dur)
o <- bsims_detect(a, tau=tau)
x <- bsims_transcribe(o, tint=tbr, rint=rbr)
get_table(x)
head(get_events(a))
head(get_detections(o))
```
### Shiny apps
A few [Shiny](https://shiny.posit.co/) apps come with the package.
These can be used to interactively explore the effects of different settings.
Compare distance functions:
```{r eval=FALSE}
run_app("distfunH")
run_app("distfunHER")
```
Compare simulation settings for single landscape:
```{r eval=FALSE}
run_app("bsimsH")
run_app("bsimsHER")
```
### Replicating simulations
Interactive sessions can be used to explore different settings.
Settings can be copied from the Shiny apps and replicated using the
`bsims_all` function:
```{r}
b <- bsims_all(extent=5, road=1, density=c(1,1,0))
b
```
The object has handy methods:
```{r eval=FALSE}
b$settings() # retrieve settings
b$new() # replicate once
b$replicate(10) # replicate 10x
```
The `$replicate()` function also runs on multiple cores:
```{r}
library(parallel)
b <- bsims_all(density=0.5)
B <- 4 # number of runs
nc <- 2 # number of cores
## sequential
system.time(bb <- b$replicate(B, cl=NULL))
## parallel clusters
cl <- makeCluster(nc)
## note: loading the package is optional
system.time(clusterEvalQ(cl, library(bSims)))
system.time(bb <- b$replicate(B, cl=cl))
stopCluster(cl)
## parallel forking
if (.Platform$OS.type != "windows") {
system.time(bb <- b$replicate(B, cl=nc))
}
```