plot_data: Create spatial pixels data frame

In realTimeSurv: Real Time Disease Surveillance Using Geospatial Statistical Modelling

Description

Output spatial pixels data frame of lgcp predictions

Usage

plot_data(
  lg,
  covariates,
  per.days = 10000,
  change.lag = NULL,
  relative = TRUE,
  msq = 10000,
  rr_lim = NULL
)

Arguments

lg	An lgcpReal object, output from a call to lgcp
covariates	A spatialPolygonsDataFrame covering the area of interest and containing the covariate and population density data. Typically the same object as specified in the covariates argument in the call to lgcp.
per.days	Integer, the number of person-days to use for incidence, default is 10,000.
change.lag	If not NULL, then plots are created of the change in outputs compared to this number of periods prior.
relative	A logical value indicating whether the comparisons (if change.lag set) should be relative (default), i.e. incidence rate ratios and ratios of relative risks, or absolute.
msq	Integer, the denominator of the population density, default is hectares (population per 10,000m^2)
rr_lim	Integer, for plotting the relative risk, the maximum value of the colour scale. Useful when comparing multiple plots to put colour gradient on same scale.

Details

This function generates a spatial pixels data frame with the output from lgcp.

Value

A codespatialPolygonsDataFrame

See Also

aggregator_data, plot_hotspot

Examples

data(dat,square,square_pop)
lg1 <- lgcp(data=dat,
            pop.var = c("popdens"),
            boundary=square,
            covariates=square_pop,
            cellwidth=0.1,
            laglength = 7,
            mala.pars=c(200,100,1),
            nchains=2)
plot_data(lg1,square_pop)

realTimeSurv documentation built on May 18, 2021, 9:07 a.m.