Package 'diseasemapping' reference manual

Title:	Modelling Spatial Variation in Disease Risk for Areal Data
Description:	Formatting of population and case data, calculation of Standardized Incidence Ratios, and fitting the BYM model using 'INLA'. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.
Authors:	Patrick Brown [aut, cre, cph], Lutong Zhou [aut]
Maintainer:	Patrick Brown <[email protected]>
License:	GPL
Version:	2.0.6
Built:	2024-11-07 02:55:58 UTC
Source:	https://github.com/cran/diseasemapping

Disease Mapping

Description

Functions for calculating observed and expected counts by region, and manipulating posterior samples from Bayesian models produced by glmmBUGS.

Author(s)

Patrick Brown

Examples

# creating SMR's
data('kentucky')
kentucky = terra::unwrap(kentucky)


kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

if(require('mapmisc', quietly=TRUE)) {
	mycol = colourScale(kentucky2$SMR, breaks=9, 
	dec=-log10(0.5), style='equal', transform='sqrt')
	plot(kentucky2, col=mycol$plot)
	legendBreaks('topleft', mycol)
}

if(require("INLA", quietly=TRUE)) {

if(requireNamespace('INLA')) {
  INLA::inla.setOption(num.threads=2)
  # not all versions of INLA support blas.num.threads
  try(INLA::inla.setOption(blas.num.threads=2), silent=TRUE)
}

kBYM = bym(observed ~ offset(logExpected) + poverty, 
	data= kentucky2, 
	prior = list(sd=c(0.1, 0.5), propSpatial=c(0.5, 0.5))
)

kBYM$par$summary

}

# creating SMR's
data('kentucky')
kentucky = terra::unwrap(kentucky)


kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

if(require('mapmisc', quietly=TRUE)) {
	mycol = colourScale(kentucky2$SMR, breaks=9, 
	dec=-log10(0.5), style='equal', transform='sqrt')
	plot(kentucky2, col=mycol$plot)
	legendBreaks('topleft', mycol)
}

if(require("INLA", quietly=TRUE)) {

if(requireNamespace('INLA')) {
  INLA::inla.setOption(num.threads=2)
  # not all versions of INLA support blas.num.threads
  try(INLA::inla.setOption(blas.num.threads=2), silent=TRUE)
}

kBYM = bym(observed ~ offset(logExpected) + poverty, 
	data= kentucky2, 
	prior = list(sd=c(0.1, 0.5), propSpatial=c(0.5, 0.5))
)

kBYM$par$summary

}

Fit the BYM model

Description

Uses inla to fit a Besag, York and Mollie disease mapping model

Usage

## S4 method for signature 'formula,ANY,ANY,missing'
bym(formula,data,adjMat,region.id,...)
## S4 method for signature 'formula,ANY,missing,missing'
bym(formula,data,adjMat,region.id,...)
## S4 method for signature 'formula,SpatVector,NULL,character'
bym(formula, data, adjMat, region.id, ...)
## S4 method for signature 'formula,SpatVector,missing,character'
bym(formula, data, adjMat, region.id, ...)
## S4 method for signature 'formula,SpatVector,matrix,character'
bym(formula,data,adjMat,region.id,...)
## S4 method for signature 'formula,data.frame,matrix,character'
bym(
formula,data,adjMat,region.id,
prior=list(sd=c(0.1,0.5),propSpatial=c(0.5,0.5)),
family="poisson",formula.fitted=formula,...)
## S4 method for signature 'formula,ANY,ANY,missing'
bym(formula,data,adjMat,region.id,...)
## S4 method for signature 'formula,ANY,missing,missing'
bym(formula,data,adjMat,region.id,...)
## S4 method for signature 'formula,SpatVector,NULL,character'
bym(formula, data, adjMat, region.id, ...)
## S4 method for signature 'formula,SpatVector,missing,character'
bym(formula, data, adjMat, region.id, ...)
## S4 method for signature 'formula,SpatVector,matrix,character'
bym(formula,data,adjMat,region.id,...)
## S4 method for signature 'formula,data.frame,matrix,character'
bym(
formula,data,adjMat,region.id,
prior=list(sd=c(0.1,0.5),propSpatial=c(0.5,0.5)),
family="poisson",formula.fitted=formula,...)

Arguments

`formula`	model formula, defaults to intercept-only model suitable for output from `getSMR` if `data` is a `SpatialPolygonsDataFrame`.
`data`	The observations and covariates for the model, can be output from `getSMR`.
`adjMat`	An object of class `nb` containing the adjacency matrix. If not supplied it will be computed from `data`, but is required if `data` is a `SpatialPolygonDataFrame`
`region.id`	Variable in `data` giving identifiers for the spatial regions. If not supplied, row numbers will be used.
`prior`	named list of vectors specifying priors, see Details
`family`	distribution of the observations, defaults to `poisson`
`formula.fitted`	formula to use to compute the fitted values, defaults to the model formula but may, for example, exclude individual-level covariates.
`...`	Additional arguments passed to `inla`in the `INLA`package, such as `control.inla`

Details

The Besag, York and Mollie model for Poisson distributed case counts is:

$Y_i \sim Poisson(O_i \lambda_i)$

$\log(\mu_i) = X_i \beta + U_i$

$U_i \sim BYM(\sigma_1^2 , \sigma_2^2)$

$Y_i$ is the response variable for region $i$ , on the left side of the formula argument.
$O_i$ is the 'baseline' expected count, which is specified in formula on the log scale with $\log(O_i)$ an offset variable.
$X_i$ are covariates, on the right side of formula
$U_i$ is a spatial random effect, with a spatially structured variance parameter $\sigma_1^2$ and a spatially independent variance $\sigma_2^2$ .

The prior has elements named sd and propSpatial, which specifies model="bym2" should be used with penalized complexity priors. The sd element gives a prior for the marginal standard deviation $\sigma_0 =\sqrt{\sigma_1^2+\sigma_2^2}$ . This prior is approximately exponential, and prior$sd = c(1, 0.01) specifies a prior probability $pr(\sigma_0 > 1) = 0.01$ . The propSpatial element gives the prior for the ratio $\phi = \sigma_1/\sigma_0$ . Having prior$propSpatial = c(0.5, 0.9) implies $pr(\phi < 0.5) = 0.9$ .

Value

A list containing

`inla`	results from the call to `inla`. Two additional elements are added: `marginals.bym` for the marginal distributions of the spatial random effects, and `marginals.fitted.bym` for the marginals of the fitted values.
`data`	A `data.frame` or `SpatialPolygonsDataFrame` containing posterior means and quantiles of the spatial random effect and fitted values.
`parameters`	Prior and posterior distributions of the two covariance parameters, and a table summary with posterior quantiles of all model parameters.

Author(s)

Patrick Brown

Examples


data('kentucky')
kentucky = terra::unwrap(kentucky)

# get rid of under 10s
larynxRates = larynxRates[-grep("_(0|5)$",names(larynxRates))]

kentucky = getSMR(kentucky, larynxRates, larynx, regionCode="County")

if(requireNamespace('INLA')) {
  INLA::inla.setOption(num.threads=2)
  # not all versions of INLA support blas.num.threads
  try(INLA::inla.setOption(blas.num.threads=2), silent=TRUE)
}

kBYM <- try(
  bym(
    observed ~ offset(logExpected) + poverty, kentucky,
	  prior= list(sd=c(0.1, 0.5), propSpatial=c(0.5, 0.5))
    ), silent=TRUE)

if(length(grep("parameters", names(kBYM)))) {
  kBYM$parameters$summary
}



if( require("mapmisc", quietly=TRUE) && length(grep("parameters", names(kBYM))) ) {
  thecol = colourScale(kBYM$data$fitted.exp, breaks=5, dec=1, style="equal")
  terra::plot(kBYM$data, col=thecol$plot)
  legendBreaks("topleft", thecol)
}


data('kentucky')
kentucky = terra::unwrap(kentucky)

# get rid of under 10s
larynxRates = larynxRates[-grep("_(0|5)$",names(larynxRates))]

kentucky = getSMR(kentucky, larynxRates, larynx, regionCode="County")

if(requireNamespace('INLA')) {
  INLA::inla.setOption(num.threads=2)
  # not all versions of INLA support blas.num.threads
  try(INLA::inla.setOption(blas.num.threads=2), silent=TRUE)
}

kBYM <- try(
  bym(
    observed ~ offset(logExpected) + poverty, kentucky,
	  prior= list(sd=c(0.1, 0.5), propSpatial=c(0.5, 0.5))
    ), silent=TRUE)

if(length(grep("parameters", names(kBYM)))) {
  kBYM$parameters$summary
}



if( require("mapmisc", quietly=TRUE) && length(grep("parameters", names(kBYM))) ) {
  thecol = colourScale(kBYM$data$fitted.exp, breaks=5, dec=1, style="equal")
  terra::plot(kBYM$data, col=thecol$plot)
  legendBreaks("topleft", thecol)
}

Download cancer incidence rates from the International Agency for Research on Cancer (IARC)

Description

Rates by age and sex group are retreived from http://ci5.iarc.fr/CI5plus/ci5plus.htm

Usage

cancerRates(area = "canada", year=2000, sex=c("M", "F"), site="Lung") 
cancerRates(area = "canada", year=2000, sex=c("M", "F"), site="Lung")

Arguments

`area`	Region to retrieve rates from,
`year`	year or sequence of years to retrieve data from, within the period 1978 to 2002
`site`	a vector of cancer sites, see details
`sex`	`"M"` or `"F"` for male or female rates only, `c("M","F")` (the default) for both sexes.

Details

area must be one of Canada, Norway, Latvia, Lithuania, Iceland, Finland, Estonia, Denmark, "Czech Republic", "Costa Rica", USA, Iowa, "New Mexico" or the Canadian provinces of Newfoundland, Prince Edward Island, Nova Scotia, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia. Alternately an integer specifying a registry code from http://ci5.iarc.fr.

site must be one or more of All Sites, Oral Cavity and Pharynx, Oesophagus. Stomach, Colon, Rectum and Anus, Liver, Gallbladder, Pancreas, Larynx, Lung, Bone, Melanoma of skin, Prostate (Males only), Testis (Males only), Breast (Females only), Cervix uteri (Females only), Corpus uteri (Females only), Ovary and other uterine adnexa (Females only), Kidney, Bladder, Eye, Brain and Central Nervous System, Thyroid, Non-Hodgkin Lymphoma, Hodgkin Lymphoma, Multiple myeloma, Leukaemia.

Value

vector of cancer rates, by age and sex group

Examples


# won't work if offline or if the iarc web site is down
qcLungF=try(cancerRates(area="canada", 
  	year=2001:2002, site="lung", sex="F"), silent=TRUE)

if(length(grep("try-error", class(qcLungF)))) {
	qcLungF = structure(c(0, 5e-06, 0, 0, 5e-06, 1e-05, 0, 3.4e-05, 9.6e-05, 
0.000211, 0.000559, 0.001289, 0.002003, 0.002508, 0.002728, 0.003189, 
0.002792, 0.001905), .Names = c("F_0", "F_5", "F_10", "F_15", 
"F_20", "F_25", "F_30", "F_35", "F_40", "F_45", "F_50", "F_55", 
"F_60", "F_65", "F_70", "F_75", "F_80", "F_85"), site = "Lung", 
area = "Canada", year = "2001-2002")
}
qcLungF

data('popdata')
popdata = terra::unwrap(popdata)

qcLungExp = getSMR(popdata, qcLungF)

names(qcLungExp)

if(require('mapmisc', quietly=TRUE)) {

	mycol = colourScale(qcLungExp$expected, 
		breaks=12, dec=0, style='quantile')
	plot(popdata[1:400,])
	plot(qcLungExp, col=mycol$plot, border='#00000040',add=TRUE)
	legendBreaks('topright', mycol)
}

# won't work if offline or if the iarc web site is down
qcLungF=try(cancerRates(area="canada", 
  	year=2001:2002, site="lung", sex="F"), silent=TRUE)

if(length(grep("try-error", class(qcLungF)))) {
	qcLungF = structure(c(0, 5e-06, 0, 0, 5e-06, 1e-05, 0, 3.4e-05, 9.6e-05, 
0.000211, 0.000559, 0.001289, 0.002003, 0.002508, 0.002728, 0.003189, 
0.002792, 0.001905), .Names = c("F_0", "F_5", "F_10", "F_15", 
"F_20", "F_25", "F_30", "F_35", "F_40", "F_45", "F_50", "F_55", 
"F_60", "F_65", "F_70", "F_75", "F_80", "F_85"), site = "Lung", 
area = "Canada", year = "2001-2002")
}
qcLungF

data('popdata')
popdata = terra::unwrap(popdata)

qcLungExp = getSMR(popdata, qcLungF)

names(qcLungExp)

if(require('mapmisc', quietly=TRUE)) {

	mycol = colourScale(qcLungExp$expected, 
		breaks=12, dec=0, style='quantile')
	plot(popdata[1:400,])
	plot(qcLungExp, col=mycol$plot, border='#00000040',add=TRUE)
	legendBreaks('topright', mycol)
}

Data set contains the number of cases information

Description

Cases of Hepatitis Z in Ontario.

Usage

data(casedata)data(casedata)

Format

data frame

Details

This dataset refers to cases of Hepatitis Z in Ontario for the years 1916 to 1918, giving the number of cases in each census subdivision by age, sex and year. For reasons of privacy, any counts between 1 and 5 have been changed to 1.

Examples

data(casedata)
head(casedata)
table(casedata$cases)
tapply(casedata$cases, casedata$age, sum)

## maybe str(casedata) ; plot(casedata) ...
data(casedata)
head(casedata)
table(casedata$cases)
tapply(casedata$cases, casedata$age, sum)

## maybe str(casedata) ; plot(casedata) ...

Format the disease case data set

Description

The formatCases funtion formats the case data set. Changes other formats of age and sex group to three columns: age, ageNumeric and sex.

Usage

formatCases(casedata, ageBreaks = NULL, years = NULL, aggregate.by = NULL)
formatCases(casedata, ageBreaks = NULL, years = NULL, aggregate.by = NULL)

Arguments

`casedata`	disease cases data set, usually a data.frame which contains age and sex and number of cases.
`ageBreaks`	results from `getBreaks` function.
`years`	if it contains multiple years, define which years will be included in.
`aggregate.by`	if want to view the data set from a macro way, could aggregate the data set by age or sex or other variables.

Details

After using formatCases function, the age columns will change to a "character" column contains ages in cut format, i.e [50,55), denotes age 50. The cut breaks can be found from the breaks of the population data set or defined by user. The original "age" column will changed to "ageNumeric" columns as factors. The sex column will have two levels "M" and "F" as factors. If "aggregate.by" is not NULL, the number of cases will be sum up by the groups defined in aggregate.by argument.

Value

formatCases function will return a data frame.

Author(s)

Patrick Brown

Examples

data('casedata')
data('popdata')
head(casedata)
caseformat <- formatCases(casedata, ageBreaks = getBreaks(names(popdata)))
head(caseformat)
caseformatagg <- formatCases(casedata, ageBreaks = getBreaks(names(popdata)), 
  aggregate.by=c("age", "sex"))
head(caseformatagg)

data('casedata')
data('popdata')
head(casedata)
caseformat <- formatCases(casedata, ageBreaks = getBreaks(names(popdata)))
head(caseformat)
caseformatagg <- formatCases(casedata, ageBreaks = getBreaks(names(popdata)), 
  aggregate.by=c("age", "sex"))
head(caseformatagg)

Format a population data set

Description

The formatCases funtion formats the population data set. Reshape the population data set to "long" format, add in 4 columns : GROUP, POPULATION, sex and age.

Usage

## S4 method for signature 'data.frame'
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL, ...
)
## S4 method for signature 'SpatVector'
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL, ...
)
## S4 method for signature 'list'
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL,  
	years=as.integer(names(popdata)), year.range=NULL, 
	 time="YEAR", 
        personYears=TRUE,...
)
## S4 method for signature 'data.frame'
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL, ...
)
## S4 method for signature 'SpatVector'
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL, ...
)
## S4 method for signature 'list'
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL,  
	years=as.integer(names(popdata)), year.range=NULL, 
	 time="YEAR", 
        personYears=TRUE,...
)

Arguments

`popdata`	population data set. It can be a data frame, list, database connection, or spatial polygon data frame
`aggregate.by`	if want to view the data set from a macro way, could aggregate the data set by age or sex or other variables
`breaks`	age breaks the user want to use. i.e breaks = c(10, 20, 30 ,40, 60, Inf).
`time`	the time variable, i.e years
`personYears`	convert populations to person-years
`years`	a vector with the year of each dataset
`year.range`	two dimensional vector with first and last year
`...`	additional arguments

Details

After using the formatPopulation function, it will return the population data set in the same class as the original data set. i.e if a spatial polygon data frame has been put into the formatPopulation function, it will return a spatial polygon data frame. If aggregate.by is not NULL, the number of cases will be sum up by the groups define in aggregate.by. The "Group" column contains information of sex and age groups,in the format of M.55, denotes male, year 55. The "POPULATION" column is a numeric column, denotes the size of population for the particular age and sex group. The "age" column will be a "character" column contains ages in a cut format. i.e [50,55), denotes age 50. The cut breaks will get from the breaks of population data set or define by user. The sex column will have two levels "M" and "F" as factors.

Value

A data frame or spatial object, matching the input.

Note

If breaks is not specified, the function will aggregate by "age" as default.

Author(s)

Patrick Brown

Examples

data('kentucky')
kentucky = terra::unwrap(kentucky)
head(terra::values(kentucky))
poptry <- formatPopulation(kentucky, breaks = c(seq(0, 80, by=10), Inf))
head(poptry)
poptryagg <- formatPopulation(kentucky, 
  breaks = c(seq(0, 80, by=10), Inf), 
  aggregate.by=c("sex", "age"))
head(poptryagg)
data('kentucky')
kentucky = terra::unwrap(kentucky)
head(terra::values(kentucky))
poptry <- formatPopulation(kentucky, breaks = c(seq(0, 80, by=10), Inf))
head(poptry)
poptryagg <- formatPopulation(kentucky, 
  breaks = c(seq(0, 80, by=10), Inf), 
  aggregate.by=c("sex", "age"))
head(poptryagg)

Age Breaks

Description

An internal function to return a list contains age breaks, ages in the population data set, sex in the population data set, and age sex groups will be used in the formatPopulation function.

Usage

getBreaks(colNames, breaks = NULL)
getBreaks(colNames, breaks = NULL)

Arguments

`colNames`	names from the population data set
`breaks`	the age breaks, i.e breaks =seq(0, 80, by= 10)

Value

vector of ages

Examples

data('kentucky')
ageBreaks = getBreaks(names(kentucky), breaks=c(seq(0, 80, by=10), Inf))
ageBreaks
data('kentucky')
ageBreaks = getBreaks(names(kentucky), breaks=c(seq(0, 80, by=10), Inf))
ageBreaks

Calculate the estimated coefficients of age and sex group from the glm model

Description

The getRates function calculates the estimated coefficient of the age and sex group from the case and population data set. It fits a glm model with Poisson distribution by default.

Usage

getRates(casedata, popdata, formula, family = 'poisson', minimumAge = 0, 
    maximumAge = 100, S = c("M", "F"), years = NULL, year.range = NULL, 
    case.years = grep("^year$", names(casedata), ignore.case = TRUE, 
        value = TRUE), fit.numeric=NULL, breaks = NULL) 
getRates(casedata, popdata, formula, family = 'poisson', minimumAge = 0, 
    maximumAge = 100, S = c("M", "F"), years = NULL, year.range = NULL, 
    case.years = grep("^year$", names(casedata), ignore.case = TRUE, 
        value = TRUE), fit.numeric=NULL, breaks = NULL)

Arguments

`casedata`	A data frame of case data, with columns corresponding to variables in `formula`. Assumed to be one row per case, unless a column called `y` or `cases` or `count` is included, in which case this column gives the number of cases per row.
`popdata`	population data set
`formula`	the glm model you want to fit. ie. `~age*sex`
`family`	the distribution to fit the model
`minimumAge`	the lower boundary of the age, default is 0
`maximumAge`	the higher boundary of the age, default is 100
`S`	vector of sexes to include in the analysis. Defaults to both "M" and "F"
`years`	a vector of census years
`year.range`	study period: a vector of two elements, starting dates and ending dates
`case.years`	variable name in the case data which contains time
`fit.numeric`	the variables which needed to be changed from factor to numeric
`breaks`	the age breaks

Details

It fits a glm model with Poisson or binomial distribution over case and population data sets. If there is no data set in some age and sex group, an NA will show there.

Value

A summary of the glm model contains set of estimated coefficients for different age and sex groups.

Author(s)

Patrick Brown

Examples


data('casedata')
data('popdata')
popdata = terra::unwrap(popdata)
therates = getRates(casedata, popdata, ~sex*age,
	breaks=c(seq(0, 80, by=10), Inf))
therates

data('casedata')
data('popdata')
popdata = terra::unwrap(popdata)
therates = getRates(casedata, popdata, ~sex*age,
	breaks=c(seq(0, 80, by=10), Inf))
therates

Calculate the standardized mortality/morbidity ratios

Description

Calculates the rate of observe value over expected value. It will also merge back the observed value, expected value and the ratio back to the population data set.

Usage

## S4 method for signature 'SpatVector,ANY,ANY,ANY,ANY'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)
## S4 method for signature 'list,ANY,ANY,ANY,ANY'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale=1, sex=c('m','f'), ...
)
## S4 method for signature 'data.frame,ANY,missing,missing,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)

## S4 method for signature 'data.frame,ANY,data.frame,missing,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)
## S4 method for signature 'data.frame,ANY,data.frame,character,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)
## S4 method for signature 'data.frame,ANY,missing,character,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)

## S4 method for signature 'data.frame,ANY,data.frame,character,character'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)

## S4 method for signature 'SpatVector,ANY,ANY,ANY,ANY'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)
## S4 method for signature 'list,ANY,ANY,ANY,ANY'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale=1, sex=c('m','f'), ...
)
## S4 method for signature 'data.frame,ANY,missing,missing,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)

## S4 method for signature 'data.frame,ANY,data.frame,missing,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)
## S4 method for signature 'data.frame,ANY,data.frame,character,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)
## S4 method for signature 'data.frame,ANY,missing,character,missing'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)

## S4 method for signature 'data.frame,ANY,data.frame,character,character'
getSMR(
popdata, model, casedata, regionCode , regionCodeCases , 
area.scale = 1,  sex=c('m','f'),...
)

Arguments

`popdata`	the name of population data set
`model`	rates, either fitted model (usually a `glm` object), or a vector of rates.
`casedata`	the name of case data set
`regionCode`	the name of district area column in population data set
`regionCodeCases`	the name of district area column in case data set
`area.scale`	scale the unit of area. e.g $10^6$: if your spatial coordinates are metres and you want intensity in cases per km2
`sex`	possible subsetting of cases and population, set `sex='f'` for females only.
`...`	additional arguments. When `popdata` is a `list`, arguments can be `personYears` (logical, convert rates to person years), `years` (a vector with the year of each dataset), or `year.range` (two dimensional vector with first and last year)

Details

If model is numeric, it's assumed to be a vector of rates, with the names of the elements corresponding to columns of the population data set. Names do not need to match exactly (can have M in one set of names, male in another for instance).

Otherwise, model is passed to the predict function.

Value

Returns a new population data set contains expected number of cases, observed number of cases and SMR. It has the same format as the population data set which put into the function.

Examples

data(kentucky)
kentucky = terra::unwrap(kentucky)

kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

terra::values(kentucky2)[1:10,grep("^F|^M", names(kentucky2), invert=TRUE)]

theBreaks = signif(seq(0, max(kentucky2$SMR, na.rm=TRUE), len=9),1)
theCol = heat.colors(length(theBreaks)-1)
terra::plot(kentucky2, col=theCol, breaks = theBreaks)
legend('left', fill=theCol, legend = paste(theBreaks[-length(theBreaks)], ' - ', theBreaks[-1]))


data(kentucky)
kentucky = terra::unwrap(kentucky)

kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

terra::values(kentucky2)[1:10,grep("^F|^M", names(kentucky2), invert=TRUE)]

theBreaks = signif(seq(0, max(kentucky2$SMR, na.rm=TRUE), len=9),1)
theCol = heat.colors(length(theBreaks)-1)
terra::plot(kentucky2, col=theCol, breaks = theBreaks)
legend('left', fill=theCol, legend = paste(theBreaks[-length(theBreaks)], ' - ', theBreaks[-1]))

Calculate the standardized rate

Description

A function to calculate the standard rate according to the Canadian standard population data set from year 1991.

Usage

getStdRate(relativeRate, model, referencePopulation, scale = 1e+05)
getStdRate(relativeRate, model, referencePopulation, scale = 1e+05)

Arguments

`relativeRate`	the relative cancer rate calculated by glmmBUGS of different sex and age group of people from ontario .
`model`	Model to standardize to, either `glm` model output or a vector of rates by age and sex group
`referencePopulation`	population to standardize to
`scale`	compute the expected rate per ‘scale’ number of people.

Value

numeric value, incidence rate in reference population.

Author(s)

Lutong Zhou

Examples

data(kentucky)
kentucky = terra::unwrap(kentucky)

kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

data(referencepop)
newpop <- getStdRate(kentucky2$SMR, larynxRates, referencepop, scale=100000)

newpop[1:10]
data(kentucky)
kentucky = terra::unwrap(kentucky)

kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

data(referencepop)
newpop <- getStdRate(kentucky2$SMR, larynxRates, referencepop, scale=100000)

newpop[1:10]

Valid models in INLA

Description

calls the function of the same name in INLA

Usage

inla.models()
inla.models()

Value

a list

Larynx cancer cases and population in Kentucky

Description

Data set contains the information of population, by age, sex, and census subdivision.

Usage

data('kentucky')data('kentucky')

Format

A SpatialPolygonsDataFrame of Kentucky boundaries and populations, case numbers for each county, and a vector of cancer rates by age and sex group.

Details

larynx is a data.frame of cancer case counts by county, obtained from http://www.cancer-rates.info and are for a single deliberately unspecified year.

kentucky contains country boundaries and populations.

kentuckyTract contains census tract boundaries and populations.

Examples

library('terra')
data('kentucky')
kentucky = terra::unwrap(kentucky)

head(larynx)
10^5*larynxRates[paste(c("M","F"), 50, sep="_")]



kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

names(kentucky2)
length(kentucky2)

data('kentuckyTract')
kentuckyTract = unwrap(kentuckyTract)

length(kentuckyTract)


if(require('mapmisc', quietly=TRUE)) {
	mycol = colourScale(kentucky2$SMR, 
		breaks=10, dec=-log10(0.5), style='quantile')
	plot(kentucky2, col=mycol$plot, border='#00000040')
	legendBreaks('topright', mycol)
} else {
	terra::plot(kentucky2)
}

breaks = c(0,1,seq(2, ceiling(max(kentucky2$SMR,na.rm=TRUE)),by=2))
thecol = terrain.colors(length(breaks)-1)

plot(kentucky2, col = thecol[cut(kentucky2$SMR, 
	breaks,include.lowest=TRUE)] )

legend("topleft", pch=15, pt.cex=2.5, adj=c(0,15), 
  legend=rev(breaks), col=c(NA, rev(thecol)))

library('terra')
data('kentucky')
kentucky = terra::unwrap(kentucky)

head(larynx)
10^5*larynxRates[paste(c("M","F"), 50, sep="_")]



kentucky2 = getSMR(kentucky, larynxRates, larynx, 
		regionCode="County")

names(kentucky2)
length(kentucky2)

data('kentuckyTract')
kentuckyTract = unwrap(kentuckyTract)

length(kentuckyTract)


if(require('mapmisc', quietly=TRUE)) {
	mycol = colourScale(kentucky2$SMR, 
		breaks=10, dec=-log10(0.5), style='quantile')
	plot(kentucky2, col=mycol$plot, border='#00000040')
	legendBreaks('topright', mycol)
} else {
	terra::plot(kentucky2)
}

breaks = c(0,1,seq(2, ceiling(max(kentucky2$SMR,na.rm=TRUE)),by=2))
thecol = terrain.colors(length(breaks)-1)

plot(kentucky2, col = thecol[cut(kentucky2$SMR, 
	breaks,include.lowest=TRUE)] )

legend("topleft", pch=15, pt.cex=2.5, adj=c(0,15), 
  legend=rev(breaks), col=c(NA, rev(thecol)))

Write a graph file for INLA

Description

Writes a graph file from an adjacency matrix suitable for use with INLA.

Usage

nbToInlaGraph(adjMat, graphFile="graph.dat", region.id = attributes(adjMat)$region.id)
nbToInlaGraph(adjMat, graphFile="graph.dat", region.id = attributes(adjMat)$region.id)

Arguments

`adjMat`	An object of class `nb` containing the adjacency matrix.
`graphFile`	name of file to save adjacency matrix to.
`region.id`	names of regions

Details

This function correctly handles regions which have zero neighbours.

Value

A vector of names and indices

Author(s)

Patrick Brown

Examples


data('kentucky')
kentucky = terra::unwrap(kentucky)
# remove all the neighbours Ballard county
kSub = kentucky[-c(2,20,79),]


adjMat = terra::adjacent(kSub)
attributes(adjMat)$region.id = kSub$County

nFile = tempfile()
nbRes = nbToInlaGraph(adjMat, nFile)

# Ballard is region 3
nbRes['Ballard']
# note that Ballard has no neighbours
table(adjMat[,'from']==3)

cat(readLines(nFile, n=5), sep='\n')


# there will be a warning about zero neighbours
junk = bym(poverty ~ 1, data=kSub, family='gaussian', num.threads=3)



data('kentucky')
kentucky = terra::unwrap(kentucky)
# remove all the neighbours Ballard county
kSub = kentucky[-c(2,20,79),]


adjMat = terra::adjacent(kSub)
attributes(adjMat)$region.id = kSub$County

nFile = tempfile()
nbRes = nbToInlaGraph(adjMat, nFile)

# Ballard is region 3
nbRes['Ballard']
# note that Ballard has no neighbours
table(adjMat[,'from']==3)

cat(readLines(nFile, n=5), sep='\n')


# there will be a warning about zero neighbours
junk = bym(poverty ~ 1, data=kSub, family='gaussian', num.threads=3)

Ontario 2006 population by census subdivision

Description

Data set contains the information of population, by age, sex, and census subdivision.

Usage

data(popdata)data(popdata)

Format

A SpatialPolygonsDataFrame object, which needs the sp package for full functionality.

Details

This data is from the 2006 Census of canada offering by Statistics Canada web site, www12.statcan.gc.ca/english/census06/data/highlights/agesex/Index_PR.cfm?Lang=E&Geo=CSD&Table=1

Examples

data('popdata')
popdata = terra::unwrap(popdata)
head(terra::values(popdata))

terra::plot(popdata)
data('popdata')
popdata = terra::unwrap(popdata)
head(terra::values(popdata))

terra::plot(popdata)

Standard Canadian population data set from year 1991.

Description

A data set contains population and age sex group from year 1991.

Usage

data(referencepop)data(referencepop)

Format

Data frame with columns POPULATION, sex, and age for the Canada 1991 population.

Details

data frame with rows representing age-sex groups, first column giving proportion of Canada 1991 population in that group, and subsequent columns giving sex and start of age range for each group

Examples

data(referencepop)
head(referencepop)
sum(referencepop$POPULATION)
data(referencepop)
head(referencepop)
sum(referencepop$POPULATION)

Package 'diseasemapping'

Help Index

Disease Mapping

Description

Author(s)

Examples

Fit the BYM model

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Download cancer incidence rates from the International Agency for Research on Cancer (IARC)

Description

Usage

Arguments

Details

Value

Examples

Data set contains the number of cases information

Description

Usage

Format

Details

Examples

Format the disease case data set

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Format a population data set

Description

Usage

Arguments

Details

Value

Note

Author(s)

Examples

Age Breaks

Description

Usage

Arguments

Value

Examples

Calculate the estimated coefficients of age and sex group from the glm model

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Calculate the standardized mortality/morbidity ratios

Description

Usage

Arguments

Details

Value

Examples

Calculate the standardized rate

Description

Usage

Arguments

Value

Author(s)

Examples

Valid models in INLA

Description

Usage

Value

See Also

Larynx cancer cases and population in Kentucky

Description