tcpPlot {SUMMER}

Discrete-color maps based on the True Classification Probabilities

Description

Discrete-color maps based on the True Classification Probabilities

Usage

tcpPlot(
  draws,
  geo,
  by.geo = NULL,
  year_plot = NULL,
  ncol = 4,
  per1000 = FALSE,
  thresholds = NULL,
  intervals = 3,
  size.title = 0.7,
  legend.label = NULL,
  border = "gray20",
  size = 0.5
)

Arguments

draws

a posterior draw object from getSmoothed

geo

SpatialPolygonsDataFrame object for the map

by.geo

variable name specifying region names in geo

year_plot

vector of year string vector to be plotted.

ncol

number of columns in the output figure.

per1000

logical indicator to multiply results by 1000.

thresholds

a vector of thresholds (on the mortality scale) defining the discrete color scale of the maps.

intervals

number of quantile intervals defining the discrete color scale of the maps. Required when thresholds are not specified.

size.title

a numerical value giving the amount by which the plot title should be magnified relative to the default.

legend.label

Label for the color legend.

border

color of the border

size

size of the border

Value

a list of True Classification Probability (TCP) tables, a list of individual spplot maps, and a gridded array of all maps.

Author(s)

Tracy Qi Dong, Zehang Richard Li

References

Tracy Qi Dong, and Jon Wakefield. (2020) Modeling and presentation of vaccination coverage estimates using data from household surveys. arXiv preprint arXiv:2004.03127.

Examples

## Not run: 
library(dplyr)
data(DemoData)
# Create dataset of counts, unstratified
counts.all <- NULL
for(i in 1:length(DemoData)){
  counts <- getCounts(DemoData[[i]][, c("clustid", "time", "age", "died",
                                        "region")],
            variables = 'died', by = c("age", "clustid", "region", 
                                         "time"))
  counts <- counts %>% mutate(cluster = clustid, years = time, Y=died)
  counts$strata <- NA
  counts$survey <- names(DemoData)[i] 
  counts.all <- rbind(counts.all, counts)
}

# fit cluster-level model on the periods
periods <- levels(DemoData[[1]]$time)
fit <- smoothCluster(data = counts.all, 
      Amat = DemoMap$Amat, 
      time.model = "rw2", 
      st.time.model = "rw1",
      strata.time.effect =  TRUE, 
      survey.effect = TRUE,
      family = "betabinomial",
      year_label = c(periods, "15-19"))
est <- getSmoothed(fit, nsim = 1000, save.draws=TRUE)

tcp <- tcpPlot(est, DemoMap$geo, by.geo = "REGNAME", interval = 3, year_plot = periods) 
tcp$g

## End(Not run)