The geostan R package supports a complete spatial analysis workflow with Bayesian models for areal data, including a suite of functions for visualizing spatial data and model results. geostan
models were built using Stan, a state-of-the-art platform for Bayesian modeling.
Introductions to the software can be found at r-spatial.org and in the package vignettes.
Features include:
geostan
model.geostan
can model disease or mortality risk for small areas with censored observations or with missing observations.For public health research, geostan complements the surveil R package for the study of time trends in disease incidence or mortality data.
There are two ways to install `geostan: directly from the package github repository or from the Comprehensive R Archive Network (CRAN).
Using your R console, you can install geostan
from CRAN:
install.packages("geostan")
If that fails, you can install from github.
You can install geostan
from github:
if (!require('devtools')) install.packages('devtools')
devtools::install_github("connordonegan/geostan")
If you are using Windows and installing with install_github
, you may need to install Rtools first (this is not needed when installing from CRAN). To install Rtools:
.exe
file you just downloaded and double-click to begin installation of Rtools.If you are using Mac and installing with install_github
then you may need to install Xcode Command Line Tools first.
All functions and methods are documented (with examples) on the website reference page. See the package vignettes for more on exploratory spatial analysis, spatial measurement error models, spatial regression with raster layers, and building custom spatial model in Stan.
To ask questions, report a bug, or discuss ideas for improvements or new features please visit the issues page, start a discussion, or submit a pull request.
Load the package and the georgia
county mortality data set:
This has county population and mortality data by sex for ages 55-64, and for the period 2014-2018. As is common for public access data, some of the observations missing because the CDC has censored them.
The sp_diag
function provides visual summaries of spatial data, including a histogram, Moran scatter plot, and map. Here is a visual summary of crude female mortality rates (as deaths per 10,000):
A <- shape2mat(georgia, style = "B")
#> Contiguity condition: queen
#> Number of neighbors per unit, summary:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.000 4.000 5.000 5.409 6.000 10.000
#>
#> Spatial weights, summary:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1 1 1 1 1 1
mortality_rate <- georgia$rate.female * 10e3
sp_diag(mortality_rate, georgia, w = A)
#> 3 NA values found in x will be dropped from data x and matrix w
#> Warning: Removed 3 rows containing non-finite outside the scale range
#> (`stat_bin()`).
The following code fits a spatial conditional autoregressive (CAR) model to female county mortality data. These models are used for estimating disease risk in small areas like counties, and for analyzing covariation of health outcomes with other area qualities. The R syntax for fitting the models is similar to using lm
or glm
. We provide the population at risk (the denominator for mortality rates) as an offset term, using the log-transform. In this case, three of the observations are missing because they have been censored; per CDC criteria, this means that there were 9 or fewer deaths in those counties. By using the censor_point
argument and setting it to censor_point = 9
, the model will account for the censoring process when providing estimates of the mortality rates:
cars <- prep_car_data(A)
#> Range of permissible rho values: -1.661, 1
fit <- stan_car(deaths.female ~ offset(log(pop.at.risk.female)),
censor_point = 9,
data = georgia,
car_parts = cars,
family = poisson(),
cores = 4, # for multi-core processing
refresh = 0) # to silence some printing
#> 3 NA values identified in the outcome variable
#> Found in rows: 55, 126, 157
#>
#> *Setting prior parameters for intercept
#> Distribution: normal
#> location scale
#> 1 -4.7 5
#>
#> *Setting prior for CAR scale parameter (car_scale)
#> Distribution: student_t
#> df location scale
#> 1 10 0 3
#>
#> *Setting prior for CAR spatial autocorrelation parameter (car_rho)
#> Distribution: uniform
#> lower upper
#> 1 -1.7 1
Passing a fitted model to the sp_diag
function will return a set of diagnostics for spatial models:
sp_diag(fit, georgia, w = A)
#> Using sp_diag(y, shape, rates = TRUE, ...). To examine data as (unstandardized) counts, use rates = FALSE.
#> 3 NA values found in x will be dropped from data x and matrix w
#> Warning: Removed 3 rows containing missing values or values outside the
#> scale range (`geom_pointrange()`).
The print
method returns a summary of the probability distributions for model parameters, as well as Markov chain Monte Carlo (MCMC) diagnostics from Stan (Monte Carlo standard errors of the mean se_mean
, effective sample size n_eff
, and the R-hat statistic Rhat
):
print(fit)
#> Spatial Model Results
#> Formula: deaths.female ~ offset(log(pop.at.risk.female))
#> Spatial method (outcome): CAR
#> Likelihood function: poisson
#> Link function: log
#> Residual Moran Coefficient: 0.0016665
#> WAIC: 1225.41
#> Observations: 156
#> Data models (ME): none
#> Inference for Stan model: foundation.
#> 4 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#>
#> mean se_mean sd 2.5% 20% 50% 80% 97.5% n_eff Rhat
#> intercept -4.672 0.002 0.088 -4.836 -4.728 -4.674 -4.618 -4.494 2497 1
#> car_rho 0.924 0.001 0.057 0.785 0.883 0.936 0.971 0.995 3289 1
#> car_scale 0.457 0.001 0.035 0.393 0.427 0.455 0.485 0.528 3657 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Aug 20 05:58:10 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
Applying the fitted
method to the fitted model will return the fitted values from the model - in this case, the fitted values are the estimates of the county mortality rates. Multiplying them by 10,000 gives mortality rate per 10,000 at risk:
mortality_est <- fitted(fit) * 10e3
county_name <- georgia$NAME
head( cbind(county_name, mortality_est) )
#> county_name mean sd 2.5% 20% 50%
#> fitted[1] Crisp 101.49822 9.422313 84.36103 93.25680 101.12252
#> fitted[2] Candler 137.25530 15.792946 108.87856 123.56765 136.61021
#> fitted[3] Barrow 94.26635 6.194287 82.31271 89.08029 94.15654
#> fitted[4] DeKalb 59.76593 1.560082 56.76403 58.42762 59.74499
#> fitted[5] Columbia 53.31762 3.271309 47.03397 50.46420 53.27029
#> fitted[6] Cobb 54.13543 1.506894 51.22763 52.89381 54.09214
#> 80% 97.5%
#> fitted[1] 109.39261 120.59846
#> fitted[2] 150.26569 169.52541
#> fitted[3] 99.37990 106.98671
#> fitted[4] 61.11074 62.79957
#> fitted[5] 56.05511 59.77822
#> fitted[6] 55.37597 57.19144
The mortality estimates are stored in the column named “mean”, and the limits of the 95% credible interval are found in the columns “2.5%” and “97.5%”.
Details and demonstrations can be found in the package help pages and vignettes.
If you use geostan
in published work, please include a citation.
Donegan, Connor (2022) “geostan: An R package for Bayesian spatial analysis” The Journal of Open Source Software. 7, no. 79: 4716. https://doi.org/10.21105/joss.04716.
@Article{,
{{geostan}: An {R} package for {B}ayesian spatial analysis},
title = {Connor Donegan},
author = {The Journal of Open Source Software},
journal = {2022},
year = {7},
volume = {79},
number = {4716},
pages = {10.21105/joss.04716},
doi = }