Type:	Package
Title:	Methods and Data for Spatial Epidemiology
Version:	1.2.8
Maintainer:	Albert Y. Kim <albert.ys.kim@gmail.com>
Description:	Methods and data for cluster detection and disease mapping.
Depends:	R (≥ 3.0.2), sp
License:	GPL-2
LazyData:	true
URL:	https://github.com/rudeboybert/SpatialEpi
BugReports:	https://github.com/rudeboybert/SpatialEpi/issues
Imports:	Rcpp, MASS, spdep
LinkingTo:	Rcpp, RcppArmadillo
NeedsCompilation:	yes
RoxygenNote:	7.2.2
Suggests:	rmarkdown, markdown, knitr, testthat (≥ 3.0.0), ggplot2, dplyr
Config/testthat/edition:	3
Encoding:	UTF-8
Packaged:	2023-02-22 00:30:28 UTC; rudeboybert
Author:	Cici Chen [ctb], Albert Y. Kim [aut, cre], Michelle Ross [ctb], Jon Wakefield [aut], Mikael Moise [aut]
Repository:	CRAN
Date/Publication:	2023-02-22 00:50:04 UTC

Produce plots of empirical Bayes posterior densities when the data Y are Poisson with expected number E and relative risk theta, with the latter having a gamma distribution with known values alpha and beta, which are estimated using empirical Bayes.

Description

This function produces plots of empirical Bayes posterior densities which are gamma distributions with parameters (alpha+Y, (alpha+E*mu)/mu) where mu = exp(x beta). The SMRs are drawn on for comparison.

Usage

EBpostdens(
  Y,
  E,
  alpha,
  beta,
  Xrow = NULL,
  lower = NULL,
  upper = NULL,
  main = ""
)

Arguments

Value

A plot containing the gamma posterior distribution

Author(s)

Jon Wakefield

Examples

data(scotland)
Y <- scotland$data$cases
E <- scotland$data$expected
ebresults <- eBayes(Y,E)
EBpostdens(Y[1], E[1], ebresults$alpha, ebresults$beta, lower=0, upper=15,
          main="Area 1")

Produce the probabilities of exceeding a threshold given a posterior gamma distribution.

Description

This function produces the posterior probabilities of exceeding a threshold given a gamma distributions with parameters (alpha+Y, (alpha+E*mu)/mu) where mu = exp(x beta). This model arises from Y being Poisson with mean theta times E where theta is the relative risk and E are the expected numbers. The prior on theta is gamma with parameters alpha and beta. The parameters alpha and beta may be estimated using empirical Bayes.

Usage

EBpostthresh(Y, E, alpha, beta, Xrow = NULL, rrthresh)

Arguments

Value

Posterior probabilities of exceedence are returned.

Author(s)

Jon Wakefield

See Also

eBayes()

Examples

data(scotland)
Y <- scotland$data$cases
E <- scotland$data$expected
ebresults <- eBayes(Y,E)
#Find probabilities of exceedence of 3
thresh3 <- EBpostthresh(Y, E, alpha=ebresults$alpha, beta=ebresults$beta, rrthresh=3)
mapvariable(thresh3, scotland$spatial.polygon)

Compute Parameters to Calibrate a Gamma Distribution

Description

Compute parameters to calibrate the prior distribution of a relative risk that has a gamma distribution.

Usage

GammaPriorCh(theta, prob, d)

Arguments

Value

List containing

Author(s)

Jon Wakefield

See Also

LogNormalPriorCh

Examples

param <- GammaPriorCh(5, 0.975,1)
curve(dgamma(x,shape=param$a,rate=param$b),from=0,to=6,n=1000,ylab="density")

Compute Parameters to Calibrate a Log-normal Distribution

Description

Compute parameters to calibrate the prior distribution of a relative risk that has a log-normal distribution.

Usage

LogNormalPriorCh(theta1, theta2, prob1, prob2)

Arguments

Value

A list containing

Author(s)

Jon Wakefield

Examples

# Calibrate the log-normal distribution s.t. the 95% confidence interval is [0.2, 5]
param <- LogNormalPriorCh(0.2, 5, 0.025, 0.975)
curve(dlnorm(x,param$mu,param$sigma), from=0, to=6, ylab="density")

Upstate New York Leukemia Data

Description

Census tract level (n=281) leukemia data for the 8 counties in upstate New York from 1978-1982, paired with population data from the 1980 census. Note that 4 census tracts were completely surrounded by another unique census tract; when applying the Bayesian cluster detection model in bayes_cluster(), we merge them with the surrounding census tracts yielding n=277 areas.

Usage

NYleukemia

Format

List with 5 items:

geo

table of the FIPS code, longitude, and latitude of the geographic centroid of each census tract

data

table of the FIPS code, number of cases, and population of each census tract

spatial.polygon

bject of class SpatialPolygons

surrounded

row IDs of the 4 census tracts that are completely surrounded by the

surrounding

census tracts

References

Turnbull, B. W. et al (1990) Monitoring for clusters of disease: application to leukemia incidence in upstate New York American Journal of Epidemiology, 132, 136–143

Examples

## Load data and convert coordinate system from latitude/longitude to grid
data(NYleukemia)
map <- NYleukemia$spatial.polygon
population <- NYleukemia$data$population
cases <- NYleukemia$data$cases
centroids <- latlong2grid(NYleukemia$geo[, 2:3])

## Identify the 4 census tract to be merged into their surrounding census tracts.  
remove <- NYleukemia$surrounded
add <- NYleukemia$surrounding

## Merge population and case counts
population[add] <- population[add] + population[remove]
population <- population[-remove]
cases[add] <- cases[add] + cases[remove]
cases <- cases[-remove]

## Modify geographical objects accordingly
map <- SpatialPolygons(map@polygons[-remove], proj4string=CRS("+proj=longlat +ellps=WGS84"))
centroids <- centroids[-remove, ]

## Plot incidence in latitude/longitude
plotmap(cases/population, map, log=TRUE, nclr=5)
points(grid2latlong(centroids), pch=4)

Upstate New York Leukemia

Description

Census tract level (n=281) leukemia data for the 8 counties in upstate New York from 1978-1982, paired with population data from the 1980 census. Note that 4 census tracts were completely surrounded by another unique census tract; when applying the Bayesian cluster detection model in bayes_cluster(), we merge them with the surrounding census tracts yielding n=277 areas.

Usage

NYleukemia_sf

Format

An sf 'POLYGON' data frame with 281 rows and 4 variables:

geometry

Geometric representation of 8 counties in upstate New York

cases

Number of cases per county

population

Population of each census tract

censustract.FIPS

11-digit Federal Information Processing System identification number for each county

Source

Turnbull, B. W. et al (1990) Monitoring for clusters of disease: application to leukemia incidence in upstate New York American Journal of Epidemiology, 132, 136–143

Examples

# Static map of NY Leukemia rate per county
library(ggplot2)
## Not run: 
ggplot(NYleukemia_sf) + 
  geom_sf(aes(fill= cases/population)) + 
  scale_fill_gradient(low = "white", high = "red")
  
## End(Not run)

Bayesian Cluster Detection Method

Description

Implementation of the Bayesian Cluster detection model of Wakefield and Kim (2013) for a study region with n areas. The prior and posterior probabilities of each of the n.zones single zones being a cluster/anti-cluster are estimated using Markov chain Monte Carlo. Furthermore, the posterior probability of k clusters/anti-clusters is computed.

Usage

bayes_cluster(
  y,
  E,
  population,
  sp.obj,
  centroids,
  max.prop,
  shape,
  rate,
  J,
  pi0,
  n.sim.lambda,
  n.sim.prior,
  n.sim.post,
  burnin.prop = 0.1,
  theta.init = vector(mode = "numeric", length = 0)
)

Arguments

Value

List containing return(list( prior.map=prior.map, post.map=post.map, pk.y=pk.y))

Author(s)

Albert Y. Kim

References

Wakefield J. and Kim A.Y. (2013) A Bayesian model for cluster detection.

Examples

## Note for the NYleukemia example, 4 census tracts were completely surrounded 
## by another unique census tract; when applying the Bayesian cluster detection 
## model in [bayes_cluster()], we merge them with the surrounding 
## census tracts yielding `n=277` areas.

## Load data and convert coordinate system from latitude/longitude to grid
data(NYleukemia)
sp.obj <- NYleukemia$spatial.polygon
population <- NYleukemia$data$population
cases <- NYleukemia$data$cases
centroids <- latlong2grid(NYleukemia$geo[, 2:3])

## Identify the 4 census tract to be merged into their surrounding census tracts 
remove <- NYleukemia$surrounded
add <- NYleukemia$surrounding

## Merge population and case counts and geographical objects accordingly
population[add] <- population[add] + population[remove]
population <- population[-remove]
cases[add] <- cases[add] + cases[remove]
cases <- cases[-remove]
sp.obj <-
  SpatialPolygons(sp.obj@polygons[-remove], proj4string=CRS("+proj=longlat +ellps=WGS84"))
centroids <- centroids[-remove, ]

## Set parameters
y <- cases
E <- expected(population, cases, 1)
max.prop <- 0.15
shape <- c(2976.3, 2.31)
rate <- c(2977.3, 1.31)
J <- 7
pi0 <- 0.95
n.sim.lambda <- 10^4
n.sim.prior <- 10^5
n.sim.post <- 10^5

## (Uncomment first) Compute output
#output <- bayes_cluster(y, E, population, sp.obj, centroids, max.prop, 
#  shape, rate, J, pi0, n.sim.lambda, n.sim.prior, n.sim.post)
#plotmap(output$prior.map$high.area, sp.obj)
#plotmap(output$post.map$high.area, sp.obj)
#plotmap(output$post.map$RR.est.area, sp.obj, log=TRUE)
#barplot(output$pk.y, names.arg=0:J, xlab="k", ylab="P(k|y)")    

Besag-Newell Cluster Detection Method

Description

Besag-Newell cluster detection method. There are differences with the original paper and our implementation:

• we base our analysis on k cases, rather than k other cases as prescribed in the paper.

• we do not subtract 1 from the accumulated numbers of other cases and accumulated numbers of others at risk, as was prescribed in the paper to discount selection bias

• M is the total number of areas included, not the number of additional areas included. i.e. M starts at 1, not 0.

• p-values are not based on the original value of k, rather the actual number of cases observed until we view k or more cases. Ex: if k = 10, but as we consider neighbors we encounter 1, 2, 9 then 12 cases, we base our p-values on k=12

• we do not provide a Monte-Carlo simulated R: the number of tests that attain significance at a fixed level \alpha

The first two and last differences are because we view the testing on an area-by-area level, rather than a case-by-case level.

Usage

besag_newell(geo, population, cases, expected.cases = NULL, k, alpha.level)

Arguments

Details

For the population and cases tables, the rows are bunched by areas first, and then for each area, the counts for each strata are listed. It is important that the tables are balanced: the strata information are in the same order for each area, and counts for each area/strata combination appear exactly once (even if zero).

Value

List containing

Note

The clusters list elements are themselves lists reporting:

Author(s)

Albert Y. Kim

References

Besag J. and Newell J. (1991) The Detection of Clusters in Rare Diseases Journal of the Royal Statistical Society. Series A (Statistics in Society), 154, 143–155

Examples

## Load Pennsylvania Lung Cancer Data
data(pennLC)
data <- pennLC$data

## Process geographical information and convert to grid
geo <- pennLC$geo[,2:3]
geo <- latlong2grid(geo)

## Get aggregated counts of population and cases for each county
population <- tapply(data$population,data$county,sum)
cases <- tapply(data$cases,data$county,sum)

## Based on the 16 strata levels, computed expected numbers of disease
n.strata <- 16
expected.cases <- expected(data$population, data$cases, n.strata)

## Set Parameters
k <- 1250
alpha.level <- 0.05

# not controlling for stratas
results <- besag_newell(geo, population, cases, expected.cases=NULL, k, 
                       alpha.level)

# controlling for stratas
results <- besag_newell(geo, population, cases, expected.cases, k, alpha.level)

Compute cartesian coordinates of a cluster center and radius

Description

This function is used for plotting purposes

Usage

circle(geo, cluster.center, cluster.end)

Arguments

Value

Author(s)

Albert Y. Kim

Examples

data(pennLC)
geo <- pennLC$geo[,2:3]
plot(geo,type='n')
text(geo,labels=1:nrow(geo))
lines( circle(geo, 23, 46), col = "red" )

Create geographical objects to be used in Bayesian Cluster Detection Method

Description

This internal function creates the geographical objects needed to run the Bayesian cluster detection method in bayes_cluster(). Specifically it creates all single zones based data objects, where single zones are the zones defined by Kulldorff (1997).

Usage

create_geo_objects(max.prop, population, centroids, sp.obj)

Arguments

Value

Author(s)

Albert Y. Kim

References

Wakefield J. and Kim A.Y. (2013) A Bayesian model for cluster detection.Biostatistics, 14, 752–765.

Examples

data(pennLC)
max.prop <- 0.15
population <- tapply(pennLC$data$population, pennLC$data$county, sum)
centroids <- latlong2grid(pennLC$geo[, 2:3])
sp.obj <- pennLC$spatial.polygon
output <- create_geo_objects(max.prop, population, centroids, sp.obj)
## number of single zones
nrow(output$cluster.coords)

Empirical Bayes Estimates of Relative Risk

Description

The computes empirical Bayes estimates of relative risk of study region with n areas, given observed and expected numbers of counts of disease and covariate information.

Usage

eBayes(Y, E, Xmat = NULL)

Arguments

Value

A list with 5 elements:

References

Clayton D. and Kaldor J. (1987) Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43, 671–681

Examples

data(scotland)
data <- scotland$data
x <- data$AFF
Xmat <- cbind(x,x^2)
results <- eBayes(data$cases,data$expected,Xmat)
scotland.map <- scotland$spatial.polygon
mapvariable(results$RR, scotland.map)

Estimate lambda values

Description

Internal function to estimate values of lambda needed for MCMC_simulation and prior probability of k clusters/anti-clusters for k=0,...,J

Usage

estimate_lambda(n.sim, J, prior.z, overlap, pi0)

Arguments

Value

estimates of lambda and prior.j

References

Wakefield J. and Kim A.Y. (2013) A Bayesian model for cluster detection. Biostatistics, 14, 752–765.

Compute Expected Numbers of Disease

Description

Compute the internally indirect standardized expected numbers of disease.

Usage

expected(population, cases, n.strata)

Arguments

Details

The population and cases vectors must be balanced: all counts are sorted by area first, and then within each area the counts for all strata are listed (even if 0 count) in the same order.

Value

Author(s)

Albert Y. Kim

References

Elliot, P. et al. (2000) Spatial Epidemiology: Methods and Applications. Oxford Medical Publications.

Examples

data(pennLC)
population <- pennLC$data$population
cases <- pennLC$data$cases
## In each county in Pennsylvania, there are 2 races, gender and 4 age bands 
## considered = 16 strata levels
pennLC$data[1:16,]
expected(population, cases, 16)

Convert Coordinates from Grid to Latitude/Longitude

Description

Convert geographic coordinates from Universal Transverse Mercator system to Latitude/Longitude.

Usage

grid2latlong(input)

Arguments

Details

Longitude/latitudes are not a grid-based coordinate system: latitudes are equidistant but the distance between longitudes varies.

Value

Either a data frame with the corresponding longitude and latitude, or a SpatialPolygons object with the coordinates changed.

Note

Rough conversion of US lat/long to km (used by GeoBUGS): (see also forum.swarthmore.edu/dr.math/problems/longandlat.html). Radius of earth: r = 3963.34 (equatorial) or 3949.99 (polar) mi = 6378.2 or 6356.7 km, which implies: km per mile = 1.609299 or 1.609295 a change of 1 degree of latitude corresponds to the same number of km, regardless of longitude. arclength=rtheta, so the multiplier for coord y should probably be just the radius of earth. On the other hand, a change of 1 degree in longitude corresponds to a different distance, depending on latitude. (at N pole, the change is essentially 0. at the equator, use equatorial radius. Perhaps for U.S., might use an "average" latitude, 30 deg is roughly Houston, 49deg is most of N bdry of continental 48 states. 0.5(30+49)=39.5 deg. so use r approx 6378.2sin(51.5)

Author(s)

Lance A. Waller

Examples

coord <- data.frame(rbind(
# Montreal, QC
c(-6414.30, 5052.849),
# Vancouver, BC
c(-122.6042, 45.6605)
))

grid2latlong(coord)

Kulldorff Cluster Detection Method

Description

Kulldorff spatial cluster detection method for a study region with n areas. The method constructs zones by consecutively aggregating nearest-neighboring areas until a proportion of the total study population is included. Given the observed number of cases, the likelihood of each zone is computed using either binomial or poisson likelihoods. The procedure reports the zone that is the most likely cluster and generates significance measures via Monte Carlo sampling. Further, secondary clusters, whose Monte Carlo p-values are below the \alpha-threshold, are reported as well.

Usage

kulldorff(
  geo,
  cases,
  population,
  expected.cases = NULL,
  pop.upper.bound,
  n.simulations,
  alpha.level,
  plot = TRUE
)

Arguments

Details

If expected.cases is specified to be NULL, then the binomial likelihood is used. Otherwise, a Poisson model is assumed. Typical values of n.simulations are 99, 999, 9999

Value

List containing:

Note

The most.likely.cluster and secondary.clusters list elements are themselves lists reporting:

Author(s)

Albert Y. Kim

References

SatScan: Software for the spatial, temporal, and space-time scan statistics https://www.satscan.org/ Kulldorff, M. (1997) A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481–1496. Kulldorff M. and Nagarwalla N. (1995) Spatial disease clusters: Detection and Inference. Statistics in Medicine, 14, 799–810.

Examples

## Load Pennsylvania Lung Cancer Data
data(pennLC)
data <- pennLC$data

## Process geographical information and convert to grid
geo <- pennLC$geo[,2:3]
geo <- latlong2grid(geo)

## Get aggregated counts of population and cases for each county
population <- tapply(data$population,data$county,sum)
cases <- tapply(data$cases,data$county,sum)

## Based on the 16 strata levels, computed expected numbers of disease
n.strata <- 16
expected.cases <- expected(data$population, data$cases, n.strata)

## Set Parameters
pop.upper.bound <- 0.5
n.simulations <- 999
alpha.level <- 0.05
plot <- TRUE

## Kulldorff using Binomial likelihoods
binomial <- kulldorff(geo, cases, population, NULL, pop.upper.bound, n.simulations, 
                     alpha.level, plot)
cluster <- binomial$most.likely.cluster$location.IDs.included

## plot
plot(pennLC$spatial.polygon,axes=TRUE)
plot(pennLC$spatial.polygon[cluster],add=TRUE,col="red")
title("Most Likely Cluster")

## Kulldorff using Poisson likelihoods
poisson <- kulldorff(geo, cases, population, expected.cases, pop.upper.bound, 
                    n.simulations, alpha.level, plot)
cluster <- poisson$most.likely.cluster$location.IDs.included

## plot
plot(pennLC$spatial.polygon,axes=TRUE)
plot(pennLC$spatial.polygon[cluster],add=TRUE,col="red")
title("Most Likely Cluster Controlling for Strata")

Convert Coordinates from Latitude/Longitude to Grid

Description

Convert geographic latitude/longitude coordinates to kilometer-based grid coordinates.

Usage

latlong2grid(input)

Arguments

Details

Longitude/latitudes are not a grid-based coordinate system: latitudes are equidistant but the distance between longitudes varies.

Value

Either a data frame with the corresponding (x,y) kilometer-based grid coordinates, or a SpatialPolygons object with the coordinates changed.

Note

Rough conversion of US lat/long to km (used by GeoBUGS): (see also forum.swarthmore.edu/dr.math/problems/longandlat.html). Radius of earth: r = 3963.34 (equatorial) or 3949.99 (polar) mi = 6378.2 or 6356.7 km, which implies: km per mile = 1.609299 or 1.609295 a change of 1 degree of latitude corresponds to the same number of km, regardless of longitude. arclength=r*theta, so the multiplier for coord y should probably be just the radius of earth. On the other hand, a change of 1 degree in longitude corresponds to a different distance, depending on latitude. (at N pole, the change is essentially 0. at the equator, use equatorial radius.

Author(s)

Lance A. Waller

Examples

## Convert coordinates
coord <- data.frame(rbind(
 # Montreal, QC:  Latitude: 45deg 28' 0" N (deg min sec), Longitude: 73deg 45' 0" W
 c(-73.7500, 45.4667),
 # Vancouver, BC:  Latitude: 45deg 39' 38" N (deg min sec), Longitude: 122deg 36' 15" W
 c(-122.6042, 45.6605)
))
latlong2grid(coord)
## Convert SpatialPolygon
data(pennLC)
new <- latlong2grid(pennLC$spatial.polygon)
par(mfrow=c(1,2))
plot(pennLC$spatial.polygon,axes=TRUE)
title("Lat/Long")
plot(new,axes=TRUE)
title("Grid (in km)")

Make legend labels

Description

leglabs makes character strings from the same break points. This function was copied from the soon-to-be deprecated maptools package with permission from author Roger Bivand

Usage

leglabs(vec, under = "under", over = "over", between = "-", reverse = FALSE)

Arguments

Author(s)

Roger Bivand, Nick Bearman, Nicholas Lewin-Koh

Plot Levels of a Variable in a Colour-Coded Map

Description

Plot levels of a variable in a colour-coded map along with a legend.

Usage

mapvariable(
  y,
  spatial.polygon,
  ncut = 1000,
  nlevels = 10,
  lower = NULL,
  upper = NULL,
  main = NULL,
  xlab = NULL,
  ylab = NULL
)

Arguments

Value

A map colour-coded to indicate the different levels of y

Author(s)

Jon Wakefield, Nicky Best, Sebastien Haneuse, and Albert Y. Kim

References

Bivand, R. S., Pebesma E. J., and Gomez-Rubio V. (2008) Applied Spatial Data Analysis with R. Springer Series in Statistics. E. J. Pebesma and R. S. Bivand. (2005) Classes and methods for spatial data in R. R News, 5, 9–13.

Examples

data(scotland)
map <- scotland$spatial.polygon
y <- scotland$data$cases
E <- scotland$data$expected
SMR <- y/E
mapvariable(SMR,map,main="Scotland",xlab="Eastings (km)",ylab="Northings (km)")

Pennsylvania Lung Cancer

Description

County-level (n=67) population/case data for lung cancer in Pennsylvania in 2002, stratified on race (white vs non-white), gender and age (Under 40, 40-59, 60-69 and 70+). Additionally, county-specific smoking rates.

Usage

pennLC

Format

List of 3 items

geo

a table of county IDs, longitude/latitude of the geographic centroid of each county

data

a table of county IDs, number of cases, population and strata information

smoking

a table of county IDs and proportion of smokers

spatial.polygon

an object of class SpatialPolygons

Source

Population data was obtained from the 2000 decennial census, lung cancer and smoking data were obtained from the Pennsylvania Department of Health website: https://www.health.pa.gov/Pages/default.aspx

Examples

data(pennLC)
pennLC$geo
pennLC$data
pennLC$smoking
# Map smoking rates in Pennsylvania
mapvariable(pennLC$smoking[,2], pennLC$spatial.polygon)

Pennsylvania Lung Cancer

Description

County-level (n=67) population/case data for lung cancer in Pennsylvania in 2002, stratified on race (white vs non-white), gender and age (Under 40, 40-59, 60-69 and 70+). Additionally, county-specific smoking rates.

Usage

pennLC_sf

Format

An sf POLYGON data frame with 1072 rows = 67 counties x 2 race x 2 gender x 4 age bands

county

Pennsylvania county

cases

Number of cases per county split by strata

population

Population per county split by strata

race

Race (w = white and o = non-white)

gender

Gender (f = female and m = male)

age

Age (4 bands)

smoking

Overall county smoking rate (not broken down by strata)

geometry

Geometric representation of counties in Pennsylvania

Source

Population data was obtained from the 2000 decennial census, lung cancer and smoking data were obtained from the Pennsylvania Department of Health website:https://www.health.pa.gov/Pages/default.aspx.

Examples

library(ggplot2)
library(dplyr)
# Sum cases & population for each county
lung_cancer_rate <- pennLC_sf %>% 
  group_by(county) %>% 
  summarize(cases = sum(cases), population = sum(population)) %>% 
  mutate(rate = cases/population)

# Static map of Pennsylvania lung cancer rates for each county
## Not run: 
ggplot() +
  geom_sf(data = lung_cancer_rate, aes(fill = rate))
  
## End(Not run)

Plot Levels of a Variable in a Colour-Coded Map

Description

Plot levels of a variable in a colour-coded map.

Usage

plotmap(
  values,
  map,
  log = FALSE,
  nclr = 7,
  include.legend = TRUE,
  lwd = 0.5,
  round = 3,
  brks = NULL,
  legend = NULL,
  location = "topright",
  rev = FALSE
)

Arguments

Value

A map colour-coded to indicate the different levels of values.

Author(s)

Albert Y. Kim

Examples

## Load data
data(scotland)
map <- scotland$spatial.polygon
y <- scotland$data$cases
E <- scotland$data$expected
SMR <- y/E
## Plot SMR
plotmap(SMR, map, nclr=9, location="topleft")

Convert a Polygon to a Spatial Polygons Object

Description

Converts a polygon (a matrix of coordinates with NA values to separate subpolygons) into a Spatial Polygons object.

Usage

polygon2spatial_polygon(
  poly,
  coordinate.system,
  area.names = NULL,
  nrepeats = NULL
)

Arguments

Details

Just as when plotting with the graphics::polygon() function, it is assumed that each subpolygon is to be closed by joining the last point to the first point. In the matrix poly, NA values separate complete subpolygons. In the case with an area consists of more than one separate closed polygon, nrepeats specifies the number of closed polygons associated with each area.

Value

An object of class SpatialPolygons (See SpatialPolygons-class from the sp package).

Author(s)

Albert Y. Kim

References

Bivand, R. S., Pebesma E. J., and Gomez-Rubio V. (2008) Applied Spatial Data Analysis with R. Springer Series in Statistics. E. J. Pebesma and R. S. Bivand. (2005) Classes and methods for spatial data in R. R News, 5, 9–13.

Examples

data(scotland)

polygon <- scotland$polygon$polygon
coord.system <- "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 "
coord.system <- paste(coord.system, "+ellps=WGS84 +datum=WGS84 +units=m +no_defs", sep = "")
names <- scotland$data$county.names
nrepeats <- scotland$polygon$nrepeats

spatial.polygon <- polygon2spatial_polygon(polygon,coord.system,names,nrepeats)

par(mfrow=c(1,2))
# plot using polygon function
plot(polygon,type='n',xlab="Eastings (km)",ylab="Northings (km)",main="Polygon File")
polygon(polygon)

# plot as spatial polygon object
plot(spatial.polygon,axes=TRUE)
title(xlab="Eastings (km)",ylab="Northings (km)",main="Spatial Polygon")

# Note that area 23 (argyll-bute) consists of 8 separate polygons
nrepeats[23]
plot(spatial.polygon[23],add=TRUE,col="red")

Process MCMC Sample

Description

Take the output of sampled configurations from MCMC_simulation and produce area-by-area summaries

Usage

process_MCMC_sample(sample, param, RR.area, cluster.list, cutoffs)

Arguments

Value

References

Wakefield J. and Kim A.Y. (2013) A Bayesian model for cluster detection. Biostatistics, 14, 752–765.

Lip Cancer in Scotland

Description

County-level (n=56) data for lip cancer among males in Scotland between 1975-1980

Usage

scotland

Format

List containing:

geo

a table of county IDs, x-coordinates (eastings) and y-coordinates (northings) of the geographic centroid of each county.

data

a table of county IDs, number of cases, population and strata information

spatial.polygon

a Spatial Polygons class (See SpatialPolygons-class) map of Scotland

polygon

a polygon map of Scotland (See polygon2spatial_polygon()

Source

Kemp I., Boyle P., Smans M. and Muir C. (1985) Atlas of cancer in Scotland, 1975-1980, incidence and epidemiologic perspective International Agency for Research on Cancer 72.

References

Clayton D. and Kaldor J. (1987) Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43, 671–681.

Examples

data(scotland)
data <- scotland$data
scotland.map <- scotland$spatial.polygon
SMR <- data$cases/data$expected
mapvariable(SMR,scotland.map)

Lip Cancer in Scotland

Description

County-level (n=56) data for lip cancer among males in Scotland between 1975-1980

Usage

scotland_sf

Format

A data frame with 56 rows representing counties and 5 variables:

geometry

Geometric representation of counties in Scotland

cases

Number of Lip Cancer cases per county

county.names

Scotland County name

AFF

Proportion of the population who work in agricultural fishing and farming

expected

Expected number of lip cancer cases

Source

Kemp I., Boyle P., Smans M. and Muir C. (1985) Atlas of cancer in Scotland, 1975-1980, incidence and epidemiologic perspective International Agency for Research on Cancer 72.

References

Clayton D. and Kaldor J. (1987) Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43, 671–681.

Examples

library(ggplot2)
## Not run: 
ggplot() +
geom_sf(data = scotland_sf, aes(fill= cases))

## End(Not run)

Create set of all single zones and output geographical information

Description

Based on the population counts and centroid coordinates of each of n areas, output the set of n.zones single zones as defined by Kulldorff and other geographical information.

Usage

zones(geo, population, pop.upper.bound)

Arguments

Value

A list containing

Author(s)

Albert Y. Kim

References

Kulldorff, M. (1997) A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481–1496. Kulldorff M. and Nagarwalla N. (1995) Spatial disease clusters: Detection and Inference. Statistics in Medicine, 14, 799–810.

Examples

data(pennLC)
geo <- pennLC$geo[,2:3]
geo <- latlong2grid(geo)
population <- tapply(pennLC$data$population, pennLC$data$county, sum)
pop.upper.bound <- 0.5
geo.info <- zones(geo, population, pop.upper.bound)