tcpPlot: Discrete-color maps based on the True Classification...

In SUMMER: Small-Area-Estimation Unit/Area Models and Methods for Estimation in R

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)

SUMMER documentation built on July 8, 2022, 9:05 a.m.