surveysd (version 1.3.1)

rescaled.bootstrap: Draw bootstrap replicates

Description

Draw bootstrap replicates from survey data using the rescaled bootstrap for stratified multistage sampling, presented by Preston, J. (2009).

Usage

rescaled.bootstrap(
  dat,
  REP = 1000,
  strata = "DB050>1",
  cluster = "DB060>DB030",
  fpc = "N.cluster>N.households",
  single.PSU = c("merge", "mean"),
  return.value = c("data", "replicates"),
  check.input = TRUE,
  new.method = FALSE
)

Value

returns the complete data set including the bootstrap replicates or just the bootstrap replicates, depending on return.value="data" or return.value="replicates" respectively.

Arguments

dat

either data frame or data table containing the survey sample

REP

integer indicating the number of bootstraps to be drawn

strata

string specifying the column name in dat that is used for stratification. For multistage sampling multiple column names can be specified by strata=c("strata1>strata2>strata3"). See Details for more information.

cluster

string specifying the column name in dat that is used for clustering. For instance given a household sample the column containing the household ID should be supplied. For multistage sampling multiple column names can be specified by cluster=c("cluster1>cluster2>cluster3"). See Details for more information.

fpc

string specifying the column name in dat that contains the number of PSUs at the first stage. For multistage sampling the number of PSUs at each stage must be specified by strata=c("fpc1>fpc2>fpc3").

single.PSU

either "merge" or "mean" defining how single PSUs need to be dealt with. For single.PSU="merge" single PSUs at each stage are merged with the strata or cluster with the next least number of PSUs. If multiple of those exist one will be select via random draw. For single.PSU="mean" single PSUs will get the mean over all bootstrap replicates at the stage which did not contain single PSUs.

return.value

either "data" or "replicates" specifying the return value of the function. For "data" the survey data is returned as class data.table, for "replicates" only the bootstrap replicates are returned as data.table.

check.input

logical, if TRUE the input will be checked before applying the bootstrap procedure

new.method

logical, if TRUE bootstrap replicates will never be negative even if in some strata the whole population is in the sample. WARNING: This is still experimental and resulting standard errors might be underestimated! Use this if for some strata the whole population is in the sample!

Author

Johannes Gussenbauer, Statistics Austria

Details

For specifying multistage sampling designs the column names in strata,cluster and fpc need to seperated by ">".
For multistage sampling the strings are read from left to right meaning that the column name before the first ">" is taken as the column for stratification/clustering/number of PSUs at the first and the column after the last ">" is taken as the column for stratification/clustering/number of PSUs at the last stage. If for some stages the sample was not stratified or clustered one must specify this by "1" or "I", e.g. strata=c("strata1>I>strata3") if there was no stratification at the second stage or cluster=c("cluster1>cluster2>I") if there were no clusters at the last stage.
The number of PSUs at each stage is not calculated internally and must be specified for any sampling design. For single stage sampling using stratification this can usually be done by adding over all sample weights of each PSU by each strata-code.
Spaces in each of the strings will be removed, so if column names contain spaces they should be renamed before calling this procedure!

References

Preston, J. (2009). Rescaled bootstrap for stratified multistage sampling. Survey Methodology. 35. 227-234.

Examples

library(surveysd)
library(data.table)
set.seed(1234)
eusilc <- demo.eusilc(n = 1,prettyNames = TRUE)

eusilc[,N.households:=uniqueN(hid),by=region]
eusilc.bootstrap <- rescaled.bootstrap(eusilc,REP=10,strata="region",
                                       cluster="hid",fpc="N.households")

eusilc[,new_strata:=paste(region,hsize,sep="_")]
eusilc[,N.housholds:=uniqueN(hid),by=new_strata]
eusilc.bootstrap <- rescaled.bootstrap(eusilc,REP=10,strata=c("new_strata"),
                                       cluster="hid",fpc="N.households")