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README.Rmd
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README.Rmd
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---
output:
md_document:
variant: gfm
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-figs/",
cache.path = "README-cache/"
)
options(width = 120) # for printing Stan output
```
# glmmfields <img src="inst/logo.png" align="right" />
[](https://github.com/seananderson/glmmfields/actions)
[](https://cran.r-project.org/package=glmmfields)
<!-- [](https://codecov.io/github/seananderson/glmmfields) -->
The glmmfields R package implements Bayesian spatiotemporal models that allow for extreme
spatial deviations through time. It uses a predictive process approach with
random fields implemented through a multivariate-t distribution instead of a
multivariate normal. The models are fit with [Stan](http://mc-stan.org/).
We published a paper describing the model and package in *Ecology*:
Anderson, S. C., Ward, E. J. 2019. Black swans in space: modelling
spatiotemporal processes with extremes. 100(1):e02403.
<https://doi.org/10.1002/ecy.2403>
You can install the [CRAN version](https://cran.r-project.org/package=glmmfields) of the package with:
```{r, eval=FALSE}
install.packages("glmmfields")
```
If you have a C++ compiler installed, you can install the development version of the package with:
```{r, eval=FALSE}
# install.packages("remotes")
remotes::install_github("seananderson/glmmfields", build_vignettes = TRUE)
```
glmmfields can also fit spatial GLMs with Stan. See the vignette:
```{r, eval=FALSE}
vignette("spatial-glms", package = "glmmfields")
```
## An example spatiotemporal model
```{r libraries, cache=FALSE}
library(glmmfields)
library(ggplot2)
```
Simulate data:
```{r simulate, cache=TRUE}
set.seed(42)
s <- sim_glmmfields(
df = 2.8, n_draws = 12, n_knots = 12, gp_theta = 2.5,
gp_sigma = 0.2, sd_obs = 0.1
)
head(s$dat)
```
```{r plot-sim, cache=TRUE, dependson="simulate"}
print(s$plot)
```
Fit the model:
```{r fit, cache=TRUE, warning=FALSE, message=FALSE, results='hide', dependson="simulate", cache.comments=FALSE}
options(mc.cores = parallel::detectCores()) # for parallel processing
m <- glmmfields(y ~ 0,
data = s$dat, time = "time",
lat = "lat", lon = "lon",
nknots = 12, estimate_df = TRUE, iter = 800, seed = 1
)
```
```{r print, cache=FALSE, dependson="fit"}
print(m)
```
Plot:
```{r plot-predictions, dependson="fit"}
plot(m, type = "prediction") + scale_color_gradient2()
plot(m, type = "spatial-residual")
```
Predictions:
```{r get-predictions, dependson="fit"}
# link scale:
p <- predict(m)
head(p)
# posterior predictive intervals on new observations (include observation error):
p <- predictive_interval(m)
head(p)
```
Use the `tidy` method to extract parameter estimates as a data frame:
```{r tidy, dependson="fit"}
x <- tidy(m, conf.int = TRUE)
head(x)
```
Make predictions on a fine-scale spatial grid:
```{r grid-predictions, dependson="fit"}
pred_grid <- expand.grid(
lat = seq(min(s$dat$lat), max(s$dat$lat), length.out = 25),
lon = seq(min(s$dat$lon), max(s$dat$lon), length.out = 25),
time = unique(s$dat$time)
)
pred_grid$prediction <- predict(m,
newdata = pred_grid, type = "response", iter = 100, estimate_method = "median"
)$estimate
ggplot(pred_grid, aes(lon, lat, fill = prediction)) +
facet_wrap(~time) +
geom_raster() +
scale_fill_gradient2()
```
# References
Anderson, S. C., Ward, E. J. 2019. Black swans in space: modelling
spatiotemporal processes with extremes. 100(1):e02403.
<https://doi.org/10.1002/ecy.2403>
Latimer, A. M., S. Banerjee, H. Sang Jr, E. S. Mosher, and J. A. Silander Jr. 2009. Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States. Ecology Letters 12:144–154.
Shelton, A. O., J. T. Thorson, E. J. Ward, and B. E. Feist. 2014. Spatial semiparametric models improve estimates of species abundance and distribution. Canadian Journal of Fisheries and Aquatic Sciences 71:1655–1666.
### NOAA Disclaimer
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Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is
provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of
Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed
by all applicable Federal law. Any reference to specific commercial products, processes, or services by service
mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or
favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a
DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by
DOC or the United States Government.