https://github.com/mncube/mxsrquick
Streamline workflows for Bayesian mixing model and MixSIAR projects
https://github.com/mncube/mxsrquick
bayesian mixsiar r statistics
Last synced: 30 days ago
JSON representation
Streamline workflows for Bayesian mixing model and MixSIAR projects
- Host: GitHub
- URL: https://github.com/mncube/mxsrquick
- Owner: mncube
- License: other
- Created: 2021-10-12T19:37:40.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-10-28T16:59:29.000Z (over 4 years ago)
- Last Synced: 2023-10-20T21:52:00.379Z (over 2 years ago)
- Topics: bayesian, mixsiar, r, statistics
- Language: R
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# mxsrquick
The goal of mxsrquick is to is to make MixSIAR project run quicker
## Installation
You can install the development version of stepurr from [GitHub](https://github.com/) with:
```{r}
# install.packages("devtools")
# devtools::install_github("mncube/mxsrquick")
```
## Example
This is a basic example which shows you how to solve a common problem:
```{r example}
library(mxsrquick)
#Create a dataframe which mimics isospace source data
iso_data <- data.frame(iso_a = c(2.2, 4.4, 3.3, 5.1, 3.4),
iso_b = c(1.6, 3.9, 5.2, 4.2, 3.7),
prot = c("bug", "bug", "bug", "plant", "plant"))
#Create an isospace plot using source groups' means and standard deviations
#Use tdf1 and tdf2 to correct for trophic discrimination factors
source_biplot(data = iso_data, group = prot,
var1 = iso_a, var2 = iso_b,
tdf1 = c(2, 1), tdf2 = c(1, 1),
x_lab = "A", y_lab = "B")
```