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README.Rmd
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README.Rmd
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---
title: "README"
author: "Max Pichler"
output:
html_document:
keep_md: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
[](http://www.repostatus.org/#active)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13687)
# s-jSDM - Fast and accurate Joint Species Distribution Modeling
## About the method
The method is described in Pichler & Hartig (2021) A new joint species distribution model for faster and more accurate inference of species associations from big community data, https://doi.org/10.1111/2041-210X.13687. The code for producing the results in this paper is available under the subfolder publications in this repo.
The method itself is wrapped into an R package, available under subfolder sjSDM. You can also use it stand-alone under Python (see instructions below). Note: for both the R and the python package, python >= 3.6 and pytorch must be installed (more details below).
## Installing the R / Python package
### R-package
Install the package via
```{r,eval=FALSE}
devtools::install_github("https://github.com/TheoreticalEcology/s-jSDM", subdir = "sjSDM")
```
Depencies for the package can be installed before or after installing the package. Detailed explanations of the dependencies are provided in vignette("Dependencies", package = "sjSDM"), source code [here](https://github.com/TheoreticalEcology/s-jSDM/blob/master/sjSDM/vignettes/Dependencies.Rmd). Very briefly, the dependencies can be automatically installed from within R:
```{r,eval=FALSE}
sjSDM::install_sjSDM(version = "gpu") # or
sjSDM::install_sjSDM(version = "cpu")
```
To cite sjSDM, please use the following citation:
```{r,eval=FALSE}
citation("sjSDM")
```
Once the dependencies are installed, the following code should run:
Simulate a community and fit model:
```{r}
library(sjSDM)
set.seed(42)
community <- simulate_SDM(sites = 100, species = 10, env = 3, se = TRUE)
Env <- community$env_weights
Occ <- community$response
SP <- matrix(rnorm(200, 0, 0.3), 100, 2) # spatial coordinates (no effect on species occurences)
model <- sjSDM(Y = Occ, env = linear(data = Env, formula = ~X1+X2+X3), spatial = linear(data = SP, formula = ~0+X1:X2), se = TRUE, family=binomial("probit"), sampling = 100L)
summary(model)
plot(model)
```
We also support also other response families:
Count data:
```{r,eval=FALSE}
model <- sjSDM(Y = Occ, env = linear(data = Env, formula = ~X1+X2+X3), spatial = linear(data = SP, formula = ~0+X1:X2), se = TRUE, family=poisson("log"))
```
Gaussian (normal):
```{r,eval=FALSE}
model <- sjSDM(Y = Occ, env = linear(data = Env, formula = ~X1+X2+X3), spatial = linear(data = SP, formula = ~0+X1:X2), se = TRUE, family=gaussian("identity"))
```
Let's have a look at the importance of the three groups (environment, associations, and space) on the occurences:
```{r,fig.height=5, fig.width=5}
imp = importance(model)
print(imp)
plot(imp)
```
As expected, space has no effect on occurences.
Let's have a look on community level how the three groups contribute to the overall explained variance
```{r,fig.height=7, fig.width=6.3}
an = anova(model)
print(an)
plot(an)
```
The anova shows the relative changes in the logLik of the groups and their intersections.
If it fails, check out the help of ?install_sjSDM, ?installation_help, and vignette("Dependencies", package = "sjSDM").
#### Installation workflow:
1. Try install_sjSDM()
2. New session, if no 'PyTorch not found' appears it should work, otherwise see ?installation_help
3. If do not get the pkg to run, create an issue [issue tracker](https://github.com/TheoreticalEcology/s-jSDM/issues) or write an email to maximilian.pichler at ur.de
### Python Package
```{bash,eval=FALSE}
pip install sjSDM_py
```
Python example
```{python,eval=TRUE}
import sjSDM_py as fa
import numpy as np
import torch
Env = np.random.randn(100, 5)
Occ = np.random.binomial(1, 0.5, [100, 10])
model = fa.Model_sjSDM(device=torch.device("cpu"), dtype=torch.float32)
model.add_env(5, 10)
model.build(5, optimizer=fa.optimizer_adamax(0.001),scheduler=False)
model.fit(Env, Occ, batch_size = 20, epochs = 10)
# print(model.weights)
# print(model.covariance)
```
Calculate Importance:
```{python}
Beta = np.transpose(model.env_weights[0])
Sigma = ( model.sigma @ model.sigma.t() + torch.diag(torch.ones([1])) ).data.cpu().numpy()
covX = fa.covariance( torch.tensor(Env).t() ).data.cpu().numpy()
fa.importance(beta=Beta, covX=covX, sigma=Sigma)
```