Draw bootstrap replicates from survey data using either the rescaled bootstrap for stratified multistage sampling, presented by J. Preston (2009) or the Rao-Wu boostrap by J. N. K. Rao and C. F. J. Wu (1988)
Arguments
- dat
either data frame or data table containing the survey sample
- method
for bootstrap replicates, either
"Preston"or"Rao-Wu"- REP
integer indicating the number of bootstraps to be drawn
- strata
string specifying the column name in
datthat is used for stratification. For multistage sampling multiple column names can be specified bystrata=c("strata1","strata2","strata3")orstrata=c("strata1>strata2>strata3"). See Details for more information.- cluster
string specifying the column name in
datthat 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 bycluster=c("cluster1","cluster2","cluster3")orcluster=c("cluster1>cluster2>cluster3"). See Details for more information.- fpc
string specifying the column name in
datthat contains the number of PSUs at the first stage. For multistage sampling the number of PSUs at each stage must be specified bystrata=c("fpc1","fpc2","fpc3")orstrata=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. Forsingle.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", "replicates" and/or "selection" 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 asdata.table. For "selection" list of data.tables with length oflength(strata)is returned containing 1:REP0-1 columns indicating if a PSU was selected for each sampling stage.- run.input.checks
logical, if TRUE the input will be checked before applying the bootstrap procedure
- already.selected
list of data.tables or
NULLwhere each data.table contains columns incluster,strataand additionally 1:REPcolumns containing 0-1 values which indicate if a PSU was selected for each bootstrap replicate. Each of the data.tables corresponds to one of the sampling stages. First entry in the list corresponds to the first sampling stage and so on.- seed
integer specifying the seed for the random number generator.
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.
Details
For specifying multistage sampling designs the column names in
strata,cluster and fpc need to be seperated by ">".
For multistage sampling the strings are read from left to right meaning that
the first vector entry or column name before the first ">" is taken as the column for
stratification/clustering/number of PSUs at the first and the last vector entry
or 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") or
strata=c("strata1>I>strata3") if there was
no stratification at the second stage or
cluster=c("cluster1","cluster2","I") respectively
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!
If already.selected is supplied the sampling of bootstrap replicates
considers if speficif PSUs have already been selected by a previous survey wave.
For a specific strata and cluster this could lead to more than floor(n/2)
records selected. In that case records will be de-selected such that floor(n/2) records,
with n as the total number of records, are selected for each
strata and cluster. This parameter ist mostly used by draw.bootstrap in
order to consider the rotation of the sampling units over time.
References
Preston, J. (2009). Rescaled bootstrap for stratified multistage sampling. Survey Methodology. 35. 227-234. Rao, J. N. K., and C. F. J. Wu. (1988). Resampling Inference with Complex Survey Data. Journal of the American Statistical Association 83 (401): 231–41.
Examples
library(surveysd)
library(data.table)
setDTthreads(1)
set.seed(1234)
eusilc <- demo.eusilc(n = 1,prettyNames = TRUE)
eusilc[,N.households:=uniqueN(hid),by=region]
#> hid hsize region pid age gender ecoStat citizenship
#> <int> <fctr> <fctr> <int> <fctr> <fctr> <fctr> <fctr>
#> 1: 1 3 Tyrol 101 (25,45] female part time AT
#> 2: 1 3 Tyrol 102 (25,45] male full time Other
#> 3: 1 3 Tyrol 103 (-Inf,16] male <NA> <NA>
#> 4: 2 4 Tyrol 201 (25,45] female domestic AT
#> 5: 2 4 Tyrol 202 (25,45] male full time AT
#> ---
#> 14823: 5997 4 Lower Austria 599704 (-Inf,16] female education AT
#> 14824: 5998 1 Upper Austria 599801 (25,45] female full time AT
#> 14825: 5999 1 Tyrol 599901 (25,45] male full time AT
#> 14826: 6000 2 Tyrol 600001 (45,65] male full time AT
#> 14827: 6000 2 Tyrol 600002 (45,65] female disabled AT
#> py010n py050n py090n py100n py110n py120n py130n py140n hy040n
#> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 9756.25 0 0.00 0 0 0 0 0 4273.9
#> 2: 12471.60 0 0.00 0 0 0 0 0 4273.9
#> 3: NA NA NA NA NA NA NA NA 4273.9
#> 4: 12487.03 0 0.00 0 0 0 0 0 0.0
#> 5: 42821.23 0 0.00 0 0 0 0 0 0.0
#> ---
#> 14823: 0.00 0 0.00 0 0 0 0 0 0.0
#> 14824: 13962.56 0 0.00 0 0 0 0 0 0.0
#> 14825: 14685.18 0 0.00 0 0 0 0 0 0.0
#> 14826: 20606.82 0 0.00 0 0 0 0 0 0.0
#> 14827: 0.00 0 3825.63 0 0 0 0 0 0.0
#> hy050n hy070n hy080n hy090n hy110n hy130n hy145n eqSS eqIncome
#> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 2: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 3: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 4: 1549.72 0 0 2.13 0 0 0 2.1 27076.24
#> 5: 1549.72 0 0 2.13 0 0 0 2.1 27076.24
#> ---
#> 14823: 1955.19 0 0 0.00 0 0 0 2.5 26508.20
#> 14824: 0.00 0 0 424.85 0 0 0 1.0 14387.41
#> 14825: 0.00 0 0 120.65 0 0 0 1.0 14805.83
#> 14826: 0.00 0 0 0.00 0 0 0 1.5 16288.30
#> 14827: 0.00 0 0 0.00 0 0 0 1.5 16288.30
#> db090 pWeight year povertyRisk N.households
#> <num> <num> <num> <lgcl> <int>
#> 1: 504.5696 504.5696 2010 FALSE 496
#> 2: 504.5696 504.5696 2010 FALSE 496
#> 3: 504.5696 504.5696 2010 FALSE 496
#> 4: 493.3824 493.3824 2010 FALSE 496
#> 5: 493.3824 493.3824 2010 FALSE 496
#> ---
#> 14823: 556.4260 556.4260 2010 FALSE 1131
#> 14824: 643.2557 643.2557 2010 FALSE 1068
#> 14825: 679.7288 679.7288 2010 FALSE 496
#> 14826: 567.1544 567.1544 2010 FALSE 496
#> 14827: 567.1544 567.1544 2010 FALSE 496
eusilc.bootstrap <- rescaled.bootstrap(eusilc,REP=10,strata="region",
cluster="hid",fpc="N.households")
eusilc[,new_strata:=paste(region,hsize,sep="_")]
#> hid hsize region pid age gender ecoStat citizenship
#> <int> <fctr> <fctr> <int> <fctr> <fctr> <fctr> <fctr>
#> 1: 1 3 Tyrol 101 (25,45] female part time AT
#> 2: 1 3 Tyrol 102 (25,45] male full time Other
#> 3: 1 3 Tyrol 103 (-Inf,16] male <NA> <NA>
#> 4: 2 4 Tyrol 201 (25,45] female domestic AT
#> 5: 2 4 Tyrol 202 (25,45] male full time AT
#> ---
#> 14823: 5997 4 Lower Austria 599704 (-Inf,16] female education AT
#> 14824: 5998 1 Upper Austria 599801 (25,45] female full time AT
#> 14825: 5999 1 Tyrol 599901 (25,45] male full time AT
#> 14826: 6000 2 Tyrol 600001 (45,65] male full time AT
#> 14827: 6000 2 Tyrol 600002 (45,65] female disabled AT
#> py010n py050n py090n py100n py110n py120n py130n py140n hy040n
#> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 9756.25 0 0.00 0 0 0 0 0 4273.9
#> 2: 12471.60 0 0.00 0 0 0 0 0 4273.9
#> 3: NA NA NA NA NA NA NA NA 4273.9
#> 4: 12487.03 0 0.00 0 0 0 0 0 0.0
#> 5: 42821.23 0 0.00 0 0 0 0 0 0.0
#> ---
#> 14823: 0.00 0 0.00 0 0 0 0 0 0.0
#> 14824: 13962.56 0 0.00 0 0 0 0 0 0.0
#> 14825: 14685.18 0 0.00 0 0 0 0 0 0.0
#> 14826: 20606.82 0 0.00 0 0 0 0 0 0.0
#> 14827: 0.00 0 3825.63 0 0 0 0 0 0.0
#> hy050n hy070n hy080n hy090n hy110n hy130n hy145n eqSS eqIncome
#> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 2: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 3: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 4: 1549.72 0 0 2.13 0 0 0 2.1 27076.24
#> 5: 1549.72 0 0 2.13 0 0 0 2.1 27076.24
#> ---
#> 14823: 1955.19 0 0 0.00 0 0 0 2.5 26508.20
#> 14824: 0.00 0 0 424.85 0 0 0 1.0 14387.41
#> 14825: 0.00 0 0 120.65 0 0 0 1.0 14805.83
#> 14826: 0.00 0 0 0.00 0 0 0 1.5 16288.30
#> 14827: 0.00 0 0 0.00 0 0 0 1.5 16288.30
#> db090 pWeight year povertyRisk N.households new_strata
#> <num> <num> <num> <lgcl> <int> <char>
#> 1: 504.5696 504.5696 2010 FALSE 496 Tyrol_3
#> 2: 504.5696 504.5696 2010 FALSE 496 Tyrol_3
#> 3: 504.5696 504.5696 2010 FALSE 496 Tyrol_3
#> 4: 493.3824 493.3824 2010 FALSE 496 Tyrol_4
#> 5: 493.3824 493.3824 2010 FALSE 496 Tyrol_4
#> ---
#> 14823: 556.4260 556.4260 2010 FALSE 1131 Lower Austria_4
#> 14824: 643.2557 643.2557 2010 FALSE 1068 Upper Austria_1
#> 14825: 679.7288 679.7288 2010 FALSE 496 Tyrol_1
#> 14826: 567.1544 567.1544 2010 FALSE 496 Tyrol_2
#> 14827: 567.1544 567.1544 2010 FALSE 496 Tyrol_2
eusilc[,N.housholds:=uniqueN(hid),by=new_strata]
#> hid hsize region pid age gender ecoStat citizenship
#> <int> <fctr> <fctr> <int> <fctr> <fctr> <fctr> <fctr>
#> 1: 1 3 Tyrol 101 (25,45] female part time AT
#> 2: 1 3 Tyrol 102 (25,45] male full time Other
#> 3: 1 3 Tyrol 103 (-Inf,16] male <NA> <NA>
#> 4: 2 4 Tyrol 201 (25,45] female domestic AT
#> 5: 2 4 Tyrol 202 (25,45] male full time AT
#> ---
#> 14823: 5997 4 Lower Austria 599704 (-Inf,16] female education AT
#> 14824: 5998 1 Upper Austria 599801 (25,45] female full time AT
#> 14825: 5999 1 Tyrol 599901 (25,45] male full time AT
#> 14826: 6000 2 Tyrol 600001 (45,65] male full time AT
#> 14827: 6000 2 Tyrol 600002 (45,65] female disabled AT
#> py010n py050n py090n py100n py110n py120n py130n py140n hy040n
#> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 9756.25 0 0.00 0 0 0 0 0 4273.9
#> 2: 12471.60 0 0.00 0 0 0 0 0 4273.9
#> 3: NA NA NA NA NA NA NA NA 4273.9
#> 4: 12487.03 0 0.00 0 0 0 0 0 0.0
#> 5: 42821.23 0 0.00 0 0 0 0 0 0.0
#> ---
#> 14823: 0.00 0 0.00 0 0 0 0 0 0.0
#> 14824: 13962.56 0 0.00 0 0 0 0 0 0.0
#> 14825: 14685.18 0 0.00 0 0 0 0 0 0.0
#> 14826: 20606.82 0 0.00 0 0 0 0 0 0.0
#> 14827: 0.00 0 3825.63 0 0 0 0 0 0.0
#> hy050n hy070n hy080n hy090n hy110n hy130n hy145n eqSS eqIncome
#> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 2: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 3: 2428.11 0 0 33.39 0 0 0 1.8 16090.69
#> 4: 1549.72 0 0 2.13 0 0 0 2.1 27076.24
#> 5: 1549.72 0 0 2.13 0 0 0 2.1 27076.24
#> ---
#> 14823: 1955.19 0 0 0.00 0 0 0 2.5 26508.20
#> 14824: 0.00 0 0 424.85 0 0 0 1.0 14387.41
#> 14825: 0.00 0 0 120.65 0 0 0 1.0 14805.83
#> 14826: 0.00 0 0 0.00 0 0 0 1.5 16288.30
#> 14827: 0.00 0 0 0.00 0 0 0 1.5 16288.30
#> db090 pWeight year povertyRisk N.households new_strata
#> <num> <num> <num> <lgcl> <int> <char>
#> 1: 504.5696 504.5696 2010 FALSE 496 Tyrol_3
#> 2: 504.5696 504.5696 2010 FALSE 496 Tyrol_3
#> 3: 504.5696 504.5696 2010 FALSE 496 Tyrol_3
#> 4: 493.3824 493.3824 2010 FALSE 496 Tyrol_4
#> 5: 493.3824 493.3824 2010 FALSE 496 Tyrol_4
#> ---
#> 14823: 556.4260 556.4260 2010 FALSE 1131 Lower Austria_4
#> 14824: 643.2557 643.2557 2010 FALSE 1068 Upper Austria_1
#> 14825: 679.7288 679.7288 2010 FALSE 496 Tyrol_1
#> 14826: 567.1544 567.1544 2010 FALSE 496 Tyrol_2
#> 14827: 567.1544 567.1544 2010 FALSE 496 Tyrol_2
#> N.housholds
#> <int>
#> 1: 79
#> 2: 79
#> 3: 79
#> 4: 102
#> 5: 102
#> ---
#> 14823: 169
#> 14824: 262
#> 14825: 118
#> 14826: 149
#> 14827: 149
eusilc.bootstrap <- rescaled.bootstrap(eusilc,REP=10,strata=c("new_strata"),
cluster="hid",fpc="N.households")