Support
Usage
Load the package and the georgia county mortality data set:
library(geostan)
-data(georgia)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 to protect privacy.
The sp_diag function provides visual summaries of spatial data, including a histogram, Moran scatter plot, and map. The Moran scatter plot displays the values against a summary of their neighboring values, so that the slope of the regression line gives a measure of their degree of autocorrelation.
Here is a quick visual summary of crude female mortality rates (as deaths per 10,000):
@@ -662,8 +664,8 @@Usage
# quick spatial diagnostics sp_diag(mortality_rate, georgia, w = C, name = "Mortality") #> 3 NA values found in x will be dropped from data x and from matrix w (nb: this disrupts row-standardization of w) -#> Warning: Removed 3 rows containing non-finite outside the scale range -#> (`stat_bin()`). +#> Warning: Removed 3 rows containing non-finite outside the scale +#> range (`stat_bin()`).
Mortality rates and other health statistics for counties are, in many cases, highly unstable estimates that cannot be relied upon for public advisories or inference (due to small population sizes). Hence, we need models to make inferences from small area data.
@@ -682,13 +684,16 @@Usage
iter = 1e3, # no. MCMC samples quiet = TRUE) # to silence printing #> 3 NA values identified in the outcome variable -#> Found in rows: 55, 126, 157 +#> Found in rows: 55, 126, 157 +#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. +#> Running the chains for more iterations may help. See +#> https://mc-stan.org/misc/warnings.html#tail-essPassing a fitted model to the sp_diag function will return a set of diagnostics for spatial models:
sp_diag(fit, georgia)
#> 3 NA values found in x will be dropped from data x and from matrix w (nb: this disrupts row-standardization of w)
-#> Warning: Removed 3 rows containing missing values or values outside the
-#> scale range (`geom_pointrange()`).
+#> 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)
@@ -697,7 +702,7 @@ Usage
#> Likelihood: poisson
#> Link: log
#> Spatial method: CAR
-#> Residual Moran Coefficient: -0.0031375
+#> Residual Moran Coefficient: 0.0018845
#> Observations: 156
#>
#> Inference for Stan model: foundation.
@@ -705,11 +710,11 @@ Usage
#> post-warmup draws per chain=500, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 20% 50% 80% 97.5% n_eff Rhat
-#> intercept -4.677 0.003 0.092 -4.871 -4.734 -4.677 -4.619 -4.513 861 1.004
-#> car_rho 0.924 0.001 0.058 0.784 0.883 0.936 0.973 0.996 1606 0.999
-#> car_scale 0.456 0.001 0.036 0.391 0.424 0.454 0.486 0.532 1899 1.002
+#> intercept -4.660 0.010 0.124 -4.844 -4.728 -4.671 -4.614 -4.415 152 1.026
+#> car_rho 0.926 0.002 0.057 0.788 0.884 0.938 0.974 0.997 982 1.004
+#> car_scale 0.457 0.001 0.035 0.392 0.427 0.454 0.485 0.533 1869 1.001
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Nov 13 16:09:50 2024.
+#> Samples were drawn using NUTS(diag_e) at Mon Dec 16 09:54:18 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).Usage
county_name <- georgia$NAME head( cbind(county_name, mortality_est) ) #> county_name mean sd 2.5% 20% 50% -#> fitted[1] Crisp 101.89147 9.784748 83.87184 93.37621 101.37227 -#> fitted[2] Candler 137.13841 15.643722 108.50033 123.57906 136.51359 -#> fitted[3] Barrow 94.35971 6.364805 82.62612 88.73920 94.14574 -#> fitted[4] DeKalb 59.74315 1.575741 56.77068 58.33325 59.71677 -#> fitted[5] Columbia 53.34581 3.207432 47.33439 50.61504 53.26339 -#> fitted[6] Cobb 54.12259 1.495041 51.24262 52.86109 54.12912 +#> fitted[1] Crisp 101.50235 9.768276 83.48211 93.29802 101.13725 +#> fitted[2] Candler 137.44182 16.379395 109.04185 123.43893 136.64743 +#> fitted[3] Barrow 94.08506 5.891425 82.33040 89.33693 93.98641 +#> fitted[4] DeKalb 59.79313 1.558891 56.90628 58.48399 59.77942 +#> fitted[5] Columbia 53.28957 3.303941 46.98875 50.43938 53.23976 +#> fitted[6] Cobb 54.14148 1.534015 51.08186 52.88618 54.14383 #> 80% 97.5% -#> fitted[1] 110.47617 122.04006 -#> fitted[2] 150.26114 169.29720 -#> fitted[3] 99.74677 107.51136 -#> fitted[4] 61.13469 62.84502 -#> fitted[5] 56.05293 59.83064 -#> fitted[6] 55.38574 57.00559 +#> fitted[1] 109.73107 121.20084 +#> fitted[2] 151.70773 170.97029 +#> fitted[3] 99.08237 105.55138 +#> fitted[4] 61.05865 62.84313 +#> fitted[5] 55.97967 60.21512 +#> fitted[6] 55.40963 57.25577The mortality estimates are stored in the column named “mean”, and the limits of the 95% credible interval are found in the columns “2.5%” and “97.5%”. Here we create a map of estimates (with some help from sf package):
library(sf)
-
-# put estimates into bins for map colors
-x <- mortality_est$mean
-brks <- quantile(x, probs = c(0, 0.2, 0.4, 0.6, 0.8, 1))
-est_cut <- cut(x, breaks = brks, include.lowest = TRUE)
-
-# assign colors to values
-rank <- as.numeric( est_cut )
-pal_fun <- colorRampPalette( c("#5D74A5FF", "gray90", "#A8554EFF") )
-pal <- pal_fun( max(rank) )
-colors <- pal[ rank ]
-
-# set plot margins
-og=par(mar=rep(1, 4))
-
-# get boundaries
-geom <- sf::st_geometry(georgia)
-
-# map estimates
-plot(geom,
- lwd = 0.2,
- col = colors)
-
-# legend
-legend("right",
- fill = pal,
- title = 'Mortality per 10,000',
- legend = levels(est_cut),
- bty = 'n'
-)
-
-mtext('County mortality rates per 10,000, Georgia women ages 55-64', side = 3, font = 2)
+#> Linking to GEOS 3.10.2, GDAL 3.4.3, PROJ 8.2.1; sf_use_s2() is TRUE
+
+# put estimates into bins for map colors
+x <- mortality_est$mean
+brks <- quantile(x, probs = c(0, 0.2, 0.4, 0.6, 0.8, 1))
+est_cut <- cut(x, breaks = brks, include.lowest = TRUE)
+
+# assign colors to values
+rank <- as.numeric( est_cut )
+pal_fun <- colorRampPalette( c("#5D74A5FF", "gray90", "#A8554EFF") )
+pal <- pal_fun( max(rank) )
+colors <- pal[ rank ]
+
+# set plot margins
+og=par(mar=rep(1, 4))
+
+# get boundaries
+geom <- sf::st_geometry(georgia)
+
+# map estimates
+plot(geom,
+ lwd = 0.2,
+ col = colors)
+
+# legend
+legend("right",
+ fill = pal,
+ title = 'Mortality per 10,000',
+ legend = levels(est_cut),
+ bty = 'n'
+)
+
+mtext('County mortality rates per 10,000, Georgia women ages 55-64', side = 3, font = 2)
+
# reset margins
par(og)Usage
# x axis axis(1, at = seq(x_lim[1], x_lim[2], by = 20)) mtext('County mortality rates per 10,000, Georgia women ages 55-64', side = 1, line = 2) -
+
par(og)More details and demonstrations can be found in the package help pages and vignettes.
diff --git a/README.md b/README.md index 1cddde9..d867357 100644 --- a/README.md +++ b/README.md @@ -55,18 +55,22 @@ Using your R console, you can install geostan from CRAN: install.packages("geostan") ``` +For most users, installing from CRAN is the recommended way to obtain +geostan. + ### From github -You can install geostan from github: +You can also install geostan from github: ``` r 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](https://cran.r-project.org/bin/windows/Rtools/) -first (this is not needed when installing from CRAN). To install Rtools: +This requires compilation of the Stan models. If you are using Windows +and installing with `install_github`, you may need to install +[Rtools](https://cran.r-project.org/bin/windows/Rtools/) first (this is +not needed when installing from CRAN). To install Rtools: 1. Visit the Rtools site:
@@ -181,6 +186,9 @@ fit <- stan_car(deaths.female ~ offset(log(pop.at.risk.female)),
quiet = TRUE) # to silence printing
#> 3 NA values identified in the outcome variable
#> Found in rows: 55, 126, 157
+#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
+#> Running the chains for more iterations may help. See
+#> https://mc-stan.org/misc/warnings.html#tail-ess
```
Passing a fitted model to the `sp_diag` function will return a set of
@@ -189,8 +197,8 @@ diagnostics for spatial models:
``` r
sp_diag(fit, georgia)
#> 3 NA values found in x will be dropped from data x and from matrix w (nb: this disrupts row-standardization of w)
-#> Warning: Removed 3 rows containing missing values or values outside the
-#> scale range (`geom_pointrange()`).
+#> Warning: Removed 3 rows containing missing values or values
+#> outside the scale range (`geom_pointrange()`).
```
@@ -208,7 +216,7 @@ print(fit)
#> Likelihood: poisson
#> Link: log
#> Spatial method: CAR
-#> Residual Moran Coefficient: -0.0031375
+#> Residual Moran Coefficient: 0.0018845
#> Observations: 156
#>
#> Inference for Stan model: foundation.
@@ -216,11 +224,11 @@ print(fit)
#> post-warmup draws per chain=500, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 20% 50% 80% 97.5% n_eff Rhat
-#> intercept -4.677 0.003 0.092 -4.871 -4.734 -4.677 -4.619 -4.513 861 1.004
-#> car_rho 0.924 0.001 0.058 0.784 0.883 0.936 0.973 0.996 1606 0.999
-#> car_scale 0.456 0.001 0.036 0.391 0.424 0.454 0.486 0.532 1899 1.002
+#> intercept -4.660 0.010 0.124 -4.844 -4.728 -4.671 -4.614 -4.415 152 1.026
+#> car_rho 0.926 0.002 0.057 0.788 0.884 0.938 0.974 0.997 982 1.004
+#> car_scale 0.457 0.001 0.035 0.392 0.427 0.454 0.485 0.533 1869 1.001
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Nov 13 16:09:50 2024.
+#> Samples were drawn using NUTS(diag_e) at Mon Dec 16 09:54:18 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).
@@ -239,19 +247,19 @@ 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 101.89147 9.784748 83.87184 93.37621 101.37227
-#> fitted[2] Candler 137.13841 15.643722 108.50033 123.57906 136.51359
-#> fitted[3] Barrow 94.35971 6.364805 82.62612 88.73920 94.14574
-#> fitted[4] DeKalb 59.74315 1.575741 56.77068 58.33325 59.71677
-#> fitted[5] Columbia 53.34581 3.207432 47.33439 50.61504 53.26339
-#> fitted[6] Cobb 54.12259 1.495041 51.24262 52.86109 54.12912
+#> fitted[1] Crisp 101.50235 9.768276 83.48211 93.29802 101.13725
+#> fitted[2] Candler 137.44182 16.379395 109.04185 123.43893 136.64743
+#> fitted[3] Barrow 94.08506 5.891425 82.33040 89.33693 93.98641
+#> fitted[4] DeKalb 59.79313 1.558891 56.90628 58.48399 59.77942
+#> fitted[5] Columbia 53.28957 3.303941 46.98875 50.43938 53.23976
+#> fitted[6] Cobb 54.14148 1.534015 51.08186 52.88618 54.14383
#> 80% 97.5%
-#> fitted[1] 110.47617 122.04006
-#> fitted[2] 150.26114 169.29720
-#> fitted[3] 99.74677 107.51136
-#> fitted[4] 61.13469 62.84502
-#> fitted[5] 56.05293 59.83064
-#> fitted[6] 55.38574 57.00559
+#> fitted[1] 109.73107 121.20084
+#> fitted[2] 151.70773 170.97029
+#> fitted[3] 99.08237 105.55138
+#> fitted[4] 61.05865 62.84313
+#> fitted[5] 55.97967 60.21512
+#> fitted[6] 55.40963 57.25577
```
The mortality estimates are stored in the column named “mean”, and the
@@ -261,6 +269,7 @@ package):
``` r
library(sf)
+#> Linking to GEOS 3.10.2, GDAL 3.4.3, PROJ 8.2.1; sf_use_s2() is TRUE
# put estimates into bins for map colors
x <- mortality_est$mean
diff --git a/docs/index.html b/docs/index.html
index 477a99f..fa084cf 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -110,15 +110,16 @@ From CRAN
install.packages("geostan")
+For most users, installing from CRAN is the recommended way to obtain geostan.
From github
-You can install geostan from github:
+You can also 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:
+This requires compilation of the Stan models. 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:
- Visit the Rtools site: https://cran.r-project.org/bin/windows/Rtools/
@@ -141,6 +142,7 @@ Usage
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 to protect privacy.
The sp_diag function provides visual summaries of spatial data, including a histogram, Moran scatter plot, and map. The Moran scatter plot displays the values against a summary of their neighboring values, so that the slope of the regression line gives a measure of their degree of autocorrelation.
@@ -163,8 +165,8 @@ Usage
# quick spatial diagnostics
sp_diag(mortality_rate, georgia, w = C, name = "Mortality")
#> 3 NA values found in x will be dropped from data x and from matrix w (nb: this disrupts row-standardization of w)
-#> Warning: Removed 3 rows containing non-finite outside the scale range
-#> (`stat_bin()`).
+#> Warning: Removed 3 rows containing non-finite outside the scale
+#> range (`stat_bin()`).
Mortality rates and other health statistics for counties are, in many cases, highly unstable estimates that cannot be relied upon for public advisories or inference (due to small population sizes). Hence, we need models to make inferences from small area data.
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 variables. 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.
@@ -183,13 +185,16 @@ Usage
iter = 1e3, # no. MCMC samples
quiet = TRUE) # to silence printing
#> 3 NA values identified in the outcome variable
-#> Found in rows: 55, 126, 157
+#> Found in rows: 55, 126, 157
+#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
+#> Running the chains for more iterations may help. See
+#> https://mc-stan.org/misc/warnings.html#tail-ess
Passing a fitted model to the sp_diag function will return a set of diagnostics for spatial models:
sp_diag(fit, georgia)
#> 3 NA values found in x will be dropped from data x and from matrix w (nb: this disrupts row-standardization of w)
-#> Warning: Removed 3 rows containing missing values or values outside the
-#> scale range (`geom_pointrange()`).
+#> 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):
@@ -199,7 +204,7 @@ Usage
#> Likelihood: poisson
#> Link: log
#> Spatial method: CAR
-#> Residual Moran Coefficient: -0.0031375
+#> Residual Moran Coefficient: 0.0018845
#> Observations: 156
#>
#> Inference for Stan model: foundation.
@@ -207,11 +212,11 @@ Usage
#> post-warmup draws per chain=500, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 20% 50% 80% 97.5% n_eff Rhat
-#> intercept -4.677 0.003 0.092 -4.871 -4.734 -4.677 -4.619 -4.513 861 1.004
-#> car_rho 0.924 0.001 0.058 0.784 0.883 0.936 0.973 0.996 1606 0.999
-#> car_scale 0.456 0.001 0.036 0.391 0.424 0.454 0.486 0.532 1899 1.002
+#> intercept -4.660 0.010 0.124 -4.844 -4.728 -4.671 -4.614 -4.415 152 1.026
+#> car_rho 0.926 0.002 0.057 0.788 0.884 0.938 0.974 0.997 982 1.004
+#> car_scale 0.457 0.001 0.035 0.392 0.427 0.454 0.485 0.533 1869 1.001
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Nov 13 16:09:50 2024.
+#> Samples were drawn using NUTS(diag_e) at Mon Dec 16 09:54:18 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).
@@ -224,22 +229,23 @@ Usage
county_name <- georgia$NAME
head( cbind(county_name, mortality_est) )
#> county_name mean sd 2.5% 20% 50%
-#> fitted[1] Crisp 101.89147 9.784748 83.87184 93.37621 101.37227
-#> fitted[2] Candler 137.13841 15.643722 108.50033 123.57906 136.51359
-#> fitted[3] Barrow 94.35971 6.364805 82.62612 88.73920 94.14574
-#> fitted[4] DeKalb 59.74315 1.575741 56.77068 58.33325 59.71677
-#> fitted[5] Columbia 53.34581 3.207432 47.33439 50.61504 53.26339
-#> fitted[6] Cobb 54.12259 1.495041 51.24262 52.86109 54.12912
+#> fitted[1] Crisp 101.50235 9.768276 83.48211 93.29802 101.13725
+#> fitted[2] Candler 137.44182 16.379395 109.04185 123.43893 136.64743
+#> fitted[3] Barrow 94.08506 5.891425 82.33040 89.33693 93.98641
+#> fitted[4] DeKalb 59.79313 1.558891 56.90628 58.48399 59.77942
+#> fitted[5] Columbia 53.28957 3.303941 46.98875 50.43938 53.23976
+#> fitted[6] Cobb 54.14148 1.534015 51.08186 52.88618 54.14383
#> 80% 97.5%
-#> fitted[1] 110.47617 122.04006
-#> fitted[2] 150.26114 169.29720
-#> fitted[3] 99.74677 107.51136
-#> fitted[4] 61.13469 62.84502
-#> fitted[5] 56.05293 59.83064
-#> fitted[6] 55.38574 57.00559
+#> fitted[1] 109.73107 121.20084
+#> fitted[2] 151.70773 170.97029
+#> fitted[3] 99.08237 105.55138
+#> fitted[4] 61.05865 62.84313
+#> fitted[5] 55.97967 60.21512
+#> fitted[6] 55.40963 57.25577
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%”. Here we create a map of estimates (with some help from sf package):
library(sf)
+#> Linking to GEOS 3.10.2, GDAL 3.4.3, PROJ 8.2.1; sf_use_s2() is TRUE
# put estimates into bins for map colors
x <- mortality_est$mean
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install.packages("geostan")From github
-You can install geostan from github:
+You can also 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:
This requires compilation of the Stan models. 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:
- Visit the Rtools site: https://cran.r-project.org/bin/windows/Rtools/ @@ -141,6 +142,7 @@
Usage
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 to protect privacy.
The sp_diag function provides visual summaries of spatial data, including a histogram, Moran scatter plot, and map. The Moran scatter plot displays the values against a summary of their neighboring values, so that the slope of the regression line gives a measure of their degree of autocorrelation.
Usage # quick spatial diagnostics sp_diag(mortality_rate, georgia, w = C, name = "Mortality") #> 3 NA values found in x will be dropped from data x and from matrix w (nb: this disrupts row-standardization of w) -#> Warning: Removed 3 rows containing non-finite outside the scale range -#> (`stat_bin()`).
lm or glm. We provide the population at risk (the denominator for mortality rates) as an offset term, using the log-transform.sp_diag function will return a set of diagnostics for spatial models:
sp_diag(fit, georgia)
#> 3 NA values found in x will be dropped from data x and from matrix w (nb: this disrupts row-standardization of w)
-#> Warning: Removed 3 rows containing missing values or values outside the
-#> scale range (`geom_pointrange()`).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):@@ -199,7 +204,7 @@Usage #> Likelihood: poisson #> Link: log #> Spatial method: CAR -#> Residual Moran Coefficient: -0.0031375 +#> Residual Moran Coefficient: 0.0018845 #> Observations: 156 #> #> Inference for Stan model: foundation. @@ -207,11 +212,11 @@
Usage #> post-warmup draws per chain=500, total post-warmup draws=2000. #> #> mean se_mean sd 2.5% 20% 50% 80% 97.5% n_eff Rhat -#> intercept -4.677 0.003 0.092 -4.871 -4.734 -4.677 -4.619 -4.513 861 1.004 -#> car_rho 0.924 0.001 0.058 0.784 0.883 0.936 0.973 0.996 1606 0.999 -#> car_scale 0.456 0.001 0.036 0.391 0.424 0.454 0.486 0.532 1899 1.002 +#> intercept -4.660 0.010 0.124 -4.844 -4.728 -4.671 -4.614 -4.415 152 1.026 +#> car_rho 0.926 0.002 0.057 0.788 0.884 0.938 0.974 0.997 982 1.004 +#> car_scale 0.457 0.001 0.035 0.392 0.427 0.454 0.485 0.533 1869 1.001 #> -#> Samples were drawn using NUTS(diag_e) at Wed Nov 13 16:09:50 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Dec 16 09:54:18 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).
sf package):
library(sf)
+#> Linking to GEOS 3.10.2, GDAL 3.4.3, PROJ 8.2.1; sf_use_s2() is TRUE
# put estimates into bins for map colors
x <- mortality_est$mean
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