surveyPrev (version 1.0.0)

directEST: Calculate direct estimates

Description

This function calculate direct estimates at given admin level.

Usage

directEST(
  data,
  cluster.info,
  admin,
  strata = "all",
  CI = 0.95,
  weight = c("population", "survey")[1],
  admin.info = NULL,
  aggregation = FALSE,
  ...
)

Value

This function returns the dataset that contain district name and population for given tiff files and polygons of admin level,

Arguments

data

dataframe that contains the indicator of interests, output of getDHSindicator function

cluster.info

list contains data and wrong.points. data contains admin 1 and admin 2 information and coordinates for each cluster. wrong.points. contains cluster id for cluster without coordinates or admin 1 information. Output of getDHSindicator function

admin

admin level for the model.

strata

use only urban or rural data, only for national level model

CI

Credible interval to be used. Default to 0.95.

weight

the weight used for aggregating result, "population" or "survey"

admin.info

list contains data and mat, data contains population and urban/rural proportion at specific admin level and mat is the adjacency matrix, output of adminInfo function

aggregation

whether or not report aggregation results.

...

Additional arguments passed on to the `smoothSurvey` function

Author

Qianyu Dong

Examples

if (FALSE) {
geo <- getDHSgeo(country = "Zambia", year = 2018)
data(ZambiaAdm1)
data(ZambiaAdm2)
data(ZambiaPopWomen)
cluster.info<-clusterInfo(geo=geo, poly.adm1=poly.adm1, poly.adm2=poly.adm2,
by.adm1 = "NAME_1",by.adm2 = "NAME_2")
dhsData <- getDHSdata(country = "Zambia",
                                 indicator = "ancvisit4+",
                                 year = 2018)

data <- getDHSindicator(dhsData, indicator = "ancvisit4+")
res_ad1 <- directEST(data = data,
                  cluster.info = cluster.info,
                  admin = 1,
                  aggregation = FALSE)
res_ad1
# compare with the DHS direct estimates
dhs_table <- get_api_table(country = "ZM",
                           survey = "ZM2018DHS",
                           indicator = "RH_ANCN_W_N4P",
                           simplify = TRUE)
subset(dhs_table, ByVariableLabel == "Five years preceding the survey")

}