Calcualte point estimates and their standard errors using bootstrap weights.
Source:R/calc.stError.R
calc.stError.RdCalculate point estimates as well as standard errors of variables in surveys. Standard errors are estimated using bootstrap weights (see draw.bootstrap and recalib). In addition the standard error of an estimate can be calcualted using the survey data for 3 or more consecutive periods, which results in a reduction of the standard error.
Usage
calc.stError(
dat,
weights = attr(dat, "weights"),
b.weights = attr(dat, "b.rep"),
period = attr(dat, "period"),
var = NULL,
fun = weightedRatio,
relative.share = FALSE,
group = NULL,
group.diff = FALSE,
fun.adjust.var = NULL,
adjust.var = NULL,
period.diff = NULL,
period.mean = NULL,
bias = FALSE,
size.limit = 20,
cv.limit = 10,
p = NULL,
add.arg = NULL,
national = FALSE,
new_method = TRUE
)Arguments
- dat
either data.frame or data.table containing the survey data. Surveys can be a panel survey or rotating panel survey, but does not need to be. For rotating panel survey bootstrap weights can be created using draw.bootstrap and recalib.
- weights
character specifying the name of the column in
datcontaining the original sample weights. Used to calculate point estimates.- b.weights
character vector specifying the names of the columns in
datcontaining bootstrap weights. Used to calculate standard errors.- period
character specifying the name of the column in
datcontaining the sample periods.- var
character vector containing variable names in
daton whichfunshall be applied for each sample period. Ifvar = NULLthe results will reflect the sum ofweights.- fun
function which will be applied on
varfor each sample period. Predefined functions are weightedRatio, weightedSum, but can also take any other function which returns a double or integer and uses weights as its second argument.boolean, if
TRUEpoint estimates resulting fromfunwill be divided by the point estimate at population level perperiod.- group
character vectors or list of character vectors containig variables in
dat. For each list entrydatwill be split in subgroups according to the containing variables as well asperiod. The pointestimates are then estimated for each subgroup seperately. Ifgroup=NULLthe data will split into sample periods by default.- group.diff
boolen, if
TRUEdifferences and the standard error between groups defined ingroupare calculated. See details for more explanations.- fun.adjust.var
can be either
NULLor a function. This argument can be used to apply a function for eachperiodand bootstrap weight to the data. The resulting estimates will be passed down tofun. See details for more explanations.- adjust.var
can be either
NULLor a character specifying the first argument infun.adjust.var.- period.diff
character vectors, defining periods for which the differences in the point estimate as well it's standard error is calculated. Each entry must have the form of
"period1 - period2". Can be NULL- period.mean
odd integer, defining the range of periods over which the sample mean of point estimates is additionally calcualted.
- bias
boolean, if
TRUEthe sample mean over the point estimates of the bootstrap weights is returned.- size.limit
integer defining a lower bound on the number of observations on
datin each group defined byperiodand the entries ingroup. Warnings are returned if the number of observations in a subgroup falls belowsize.limit. In addition the concerned groups are available in the function output.- cv.limit
non-negativ value defining a upper bound for the standard error in relation to the point estimate. If this relation exceed
cv.limit, for a point estimate, they are flagged and available in the function output.- p
numeric vector containing values between 0 and 1. Defines which quantiles for the distribution of
varare additionally estimated.- add.arg
additional arguments which will be passed to fun. Can be either a named list or vector. The names of the object correspond to the function arguments and the values to column names in dat, see also examples.
- national
DEPRECATED use
relative.shareinstead! boolean, if TRUE point estimates resulting from fun will be divided by the point estimate at the national level.- new_method
used for testing new implementation; will be removed with new release.
Value
Returns a list containing:
Estimates: data.table containing period differences and/or k period averages for estimates offunapplied tovaras well as the corresponding standard errors, which are calculated using the bootstrap weights. In addition the sample size,n, and poplutaion size for each group is added to the output.smallGroups: data.table containing groups for which the number of observation falls belowsize.limit.cvHigh: data.table containing a boolean variable which indicates for each estimate if the estimated standard error exceedscv.limit.stEDecrease: data.table indicating for each estimate the theoretical increase in sample size which is gained when averaging over k periods. Only returned ifperiod.meanis notNULL.
Details
calc.stError takes survey data (dat) and returns point estimates
as well as their standard Errors defined by fun and var for each sample
period in dat. dat must be household data where household members
correspond to multiple rows with the same household identifier. The data
should at least contain the following columns:
Column indicating the sample period;
Column indicating the household ID;
Column containing the household sample weights;
Columns which contain the bootstrap weights (see output of recalib);
Columns listed in
varas well as ingroup
For each variable in var as well as sample period the function fun is
applied using the original as well as the bootstrap sample weights.
The point estimate is then selected as the result of fun when using the
original sample weights and it's standard error is estimated with the result
of fun using the bootstrap sample weights. fun can be any function which returns a double or integer and uses sample
weights as it's second argument. The predifined options are weightedRatio
and weightedSum.
For the option weightedRatio a weighted ratio (in \
calculated for var equal to 1, e.g
sum(weight[var==1])/sum(weight[!is.na(var)])*100.
Additionally using the option national=TRUE the weighted ratio (in \
divided by the weighted ratio at the national level for each period.
If group is not NULL but a vector of variables from dat then fun is
applied on each subset of dat defined by all combinations of values in
group.
For instance if group = "sex" with "sex" having the values "Male" and
"Female" in dat the point estimate and standard error is calculated on the
subsets of dat with only "Male" or "Female" value for "sex". This is done
for each value of period. For variables in group which have NAs in
dat the rows containing the missings will be discarded.
When group is a list of character vectors, subsets of dat and the
following estimation of the point estimate, including the estimate for the
standard error, are calculated for each list entry.
If group.diff = TRUE difference between groups definded by group are calculated.
Differences are only calculated within each variables of group,
e.g group = c("gender", "region") will calcualate estimates of each group and
also differences within "gender" and "region" seperately.
If grouping is done with multiple variables e.g group = list(c("gender","region"))
(~ grouping by "gender" x "region") differences are calculated
only between groups where one of the grouping variables is different.
For instance the difference between gender = "female" & region = "Vienna" and
gender = "male" & region = "Vienna" OR gender = "female" & region = "Vienna" and
gender = "female" & region = "Salzburg" will be calculated.
The difference between gender = "female" & region = "Vienna" and
gender = "male" & region = "Salzburg" will not be calculated. The order of difference
is determined by order of value (alpha-numerical order) or
if grouping contains factor variables the factor levels determin the order.
The optional parameters fun.adjust.var and adjust.var can be used if the
values in var are dependent on the weights. As is for instance the case
for the poverty thershhold calculated from EU-SILC.
In such a case an additional function can be supplied using fun.adjust.var
as well as its first argument adjust.var, which needs to be part of the
data set dat. Then, before applying fun on variable var
for all period and groups, the function fun.adjust.var is applied to
adjust.var using each of the bootstrap weights seperately (NOTE: weight is
used as the second argument of fun.adjust.var).
Thus creating i=1,...,length(b.weights) additional variables.
For applying fun on var the estimates for the bootstrap replicate will
now use each of the corresponding new additional variables. So instead of
$$fun(var,weights,...),fun(var,b.weights[1],...),
fun(var,b.weights[2],...),...$$
the function fun will be applied in the way
$$fun(var,weights,...),fun(var.1,b.weights[1],...),fun(var.2,
b.weights[2],...),...$$
where var.1, var.2, ... correspond to the estimates resulting from
fun.adjust.var and adjust.var.
NOTE: This procedure is especially usefull if the var is dependent on
weights and fun is applied on subgroups of the data set. Then it is not
possible to capture this procedure with fun and var, see examples for a
more hands on explanation.
When defining period.diff the difference of point estimates between periods
as well their standard errors are calculated.
The entries in period.diff must have the form of "period1 - period2"
which means that the results of the point estimates for period2 will be
substracted from the results of the point estimates for period1.
Specifying period.mean leads to an improvement in standard error by
averaging the results for the point estimates, using the bootstrap weights,
over period.mean periods.
Setting, for instance, period.mean = 3 the results in averaging these
results over each consecutive set of 3 periods.
Estimating the standard error over these averages gives an improved estimate
of the standard error for the central period, which was used for
averaging.
The averaging of the results is also applied in differences of point
estimates. For instance defining period.diff = "2015-2009" and
period.mean = 3
the differences in point estimates of 2015 and 2009, 2016 and 2010 as well as
2014 and 2008 are calcualated and finally the average over these 3
differences is calculated.
The periods set in period.diff are always used as the middle periods around
which the mean over period.mean years is build.
Setting bias to TRUE returns the calculation of a mean over the results
from the bootstrap replicates. In the output the corresponding columns is
labeled _mean at the end.
If fun needs more arguments they can be supplied in add.arg. This can
either be a named list or vector.
The parameter size.limit indicates a lower bound of the sample size for
subsets in dat created by group. If the sample size of a subset falls
below size.limit a warning will be displayed.
In addition all subsets for which this is the case can be selected from the
output of calc.stError with $smallGroups.
With the parameter cv.limit one can set an upper bound on the coefficient
of variantion. Estimates which exceed this bound are flagged with TRUE and
are available in the function output with $cvHigh.
cv.limit must be a positive integer and is treated internally as \
for cv.limit=1 the estimate will be flagged if the coefficient of
variantion exceeds 1\
When specifying period.mean, the decrease in standard error for choosing
this method is internally calcualted and a rough estimate for an implied
increase in sample size is available in the output with $stEDecrease.
The rough estimate for the increase in sample size uses the fact that for a
sample of size \(n\) the sample estimate for the standard error of most
point estimates converges with a factor \(1/\sqrt{n}\) against the true
standard error \(\sigma\).
Examples
# Import data and calibrate
library(surveysd)
library(data.table)
setDTthreads(1)
set.seed(1234)
eusilc <- demo.eusilc(n = 4,prettyNames = TRUE)
dat_boot <- draw.bootstrap(eusilc, REP = 3, hid = "hid", weights = "pWeight",
strata = "region", period = "year")
dat_boot_calib <- recalib(dat_boot, conP.var = "gender", conH.var = "region")
#> Iteration stopped after 3 steps
#> Convergence reached
#> Iteration stopped after 2 steps
#> Convergence reached
#> Iteration stopped after 3 steps
#> Convergence reached
# estimate weightedRatio for povertyRisk per period
err.est <- calc.stError(dat_boot_calib, var = "povertyRisk",
fun = weightedRatio)
err.est$Estimates
#> Key: <year, n, N, estimate_type>
#> year n N estimate_type val_povertyRisk stE_povertyRisk
#> <num> <int> <num> <char> <num> <num>
#> 1: 2010 14827 8182222 direct 14.44422 0.6910876
#> 2: 2011 14827 8182222 direct 14.77393 0.6186808
#> 3: 2012 14827 8182222 direct 15.04515 0.5684274
#> 4: 2013 14827 8182222 direct 14.89013 0.1781831
# calculate weightedRatio for povertyRisk and fraction of one-person
# households per period
dat_boot_calib[, onePerson := .N == 1, by = .(year, hid)]
#> hid hsize region pid age gender ecoStat
#> <int> <fctr> <fctr> <int> <fctr> <fctr> <fctr>
#> 1: 1 3 Tyrol 101 (25,45] female part time
#> 2: 1 3 Tyrol 102 (25,45] male full time
#> 3: 1 3 Tyrol 103 (-Inf,16] male <NA>
#> 4: 2 4 Tyrol 201 (25,45] female domestic
#> 5: 2 4 Tyrol 202 (25,45] male full time
#> ---
#> 59304: 8999 4 Lower Austria 899904 (-Inf,16] female education
#> 59305: 10500 1 Upper Austria 1050001 (25,45] female full time
#> 59306: 5999 1 Tyrol 599901 (25,45] male full time
#> 59307: 7500 2 Tyrol 750001 (45,65] male full time
#> 59308: 7500 2 Tyrol 750002 (45,65] female disabled
#> citizenship py010n py050n py090n py100n py110n py120n py130n py140n
#> <fctr> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: AT 9756.25 0 0.00 0 0 0 0 0
#> 2: Other 12471.60 0 0.00 0 0 0 0 0
#> 3: <NA> NA NA NA NA NA NA NA NA
#> 4: AT 12487.03 0 0.00 0 0 0 0 0
#> 5: AT 42821.23 0 0.00 0 0 0 0 0
#> ---
#> 59304: AT 0.00 0 0.00 0 0 0 0 0
#> 59305: AT 13962.56 0 0.00 0 0 0 0 0
#> 59306: AT 14685.18 0 0.00 0 0 0 0 0
#> 59307: AT 20606.82 0 0.00 0 0 0 0 0
#> 59308: AT 0.00 0 3825.63 0 0 0 0 0
#> hy040n hy050n hy070n hy080n hy090n hy110n hy130n hy145n eqSS eqIncome
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 4273.9 2428.11 0 0 33.39 0 0 0 1.8 16090.694
#> 2: 4273.9 2428.11 0 0 33.39 0 0 0 1.8 16090.694
#> 3: 4273.9 2428.11 0 0 33.39 0 0 0 1.8 16090.694
#> 4: 0.0 1549.72 0 0 2.13 0 0 0 2.1 27076.243
#> 5: 0.0 1549.72 0 0 2.13 0 0 0 2.1 27076.243
#> ---
#> 59304: 0.0 1955.19 0 0 0.00 0 0 0 2.5 26970.023
#> 59305: 0.0 0.00 0 0 424.85 0 0 0 1.0 6923.625
#> 59306: 0.0 0.00 0 0 120.65 0 0 0 1.0 14805.830
#> 59307: 0.0 0.00 0 0 0.00 0 0 0 1.5 24680.877
#> 59308: 0.0 0.00 0 0 0.00 0 0 0 1.5 24680.877
#> db090 pWeight year povertyRisk w1 w2
#> <num> <num> <num> <lgcl> <num> <num>
#> 1: 504.5696 504.5696 2010 FALSE 1006.2332589 0.4427778
#> 2: 504.5696 504.5696 2010 FALSE 1006.2332589 0.4427778
#> 3: 504.5696 504.5696 2010 FALSE 1006.2332589 0.4427778
#> 4: 493.3824 493.3824 2010 FALSE 980.8756252 0.4321565
#> 5: 493.3824 493.3824 2010 FALSE 980.8756252 0.4321565
#> ---
#> 59304: 556.4260 556.4260 2013 FALSE 1103.0230839 1106.7748059
#> 59305: 643.2557 643.2557 2013 TRUE 0.6113836 1282.4281820
#> 59306: 679.7288 679.7288 2013 FALSE 0.5885659 0.5994622
#> 59307: 567.1544 567.1544 2013 FALSE 0.4998739 0.5015741
#> 59308: 567.1544 567.1544 2013 FALSE 0.4998739 0.5015741
#> w3 onePerson
#> <num> <lgcl>
#> 1: 0.4466294 FALSE
#> 2: 0.4466294 FALSE
#> 3: 0.4466294 FALSE
#> 4: 981.5229187 FALSE
#> 5: 981.5229187 FALSE
#> ---
#> 59304: 1125.1069971 FALSE
#> 59305: 0.6060360 TRUE
#> 59306: 1386.4651735 TRUE
#> 59307: 1138.8643405 FALSE
#> 59308: 1138.8643405 FALSE
err.est <- calc.stError(dat_boot_calib, var = c("povertyRisk", "onePerson"),
fun = weightedRatio)
err.est$Estimates
#> Key: <year, n, N, estimate_type>
#> year n N estimate_type val_povertyRisk stE_povertyRisk
#> <num> <int> <num> <char> <num> <num>
#> 1: 2010 14827 8182222 direct 14.44422 0.5335276
#> 2: 2011 14827 8182222 direct 14.77393 0.5417570
#> 3: 2012 14827 8182222 direct 15.04515 0.5502787
#> 4: 2013 14827 8182222 direct 14.89013 0.3622258
#> val_onePerson stE_onePerson
#> <num> <num>
#> 1: 14.85737 0.5335276
#> 2: 14.85737 0.5417570
#> 3: 14.85737 0.5502787
#> 4: 14.85737 0.3622258
# estimate weightedRatio for povertyRisk per period and gender and
# period x region x gender
group <- list("gender", c("gender", "region"))
err.est <- calc.stError(dat_boot_calib, var = "povertyRisk",
fun = weightedRatio, group = group)
err.est$Estimates
#> Key: <year, n, N, gender, region, estimate_type>
#> year n N gender region estimate_type val_povertyRisk
#> <num> <int> <num> <fctr> <fctr> <char> <num>
#> 1: 2010 261 122741.8 male Burgenland direct 17.414524
#> 2: 2010 288 137822.2 female Burgenland direct 21.432598
#> 3: 2010 359 182732.9 male Vorarlberg direct 12.973259
#> 4: 2010 374 194622.1 female Vorarlberg direct 19.883637
#> 5: 2010 440 253143.7 male Salzburg direct 9.156964
#> 6: 2010 484 282307.3 female Salzburg direct 17.939382
#> 7: 2010 517 268581.4 male Carinthia direct 10.552149
#> 8: 2010 561 295066.6 female Carinthia direct 15.392924
#> 9: 2010 650 339566.5 male Tyrol direct 12.857542
#> 10: 2010 667 362332.5 female Tyrol direct 17.604861
#> 11: 2010 1128 571011.7 male Styria direct 11.671247
#> 12: 2010 1132 774405.4 male Vienna direct 15.590616
#> 13: 2010 1167 596033.3 female Styria direct 16.964539
#> 14: 2010 1190 824525.6 female Vienna direct 18.778813
#> 15: 2010 1363 684272.5 male Upper Austria direct 9.074690
#> 16: 2010 1387 772593.2 female Lower Austria direct 16.372949
#> 17: 2010 1417 783115.8 male Lower Austria direct 11.348283
#> 18: 2010 1442 737347.5 female Upper Austria direct 12.574206
#> 19: 2010 7267 3979571.7 male <NA> direct 12.026600
#> 20: 2010 7560 4202650.3 female <NA> direct 16.733508
#> 21: 2010 14827 8182222.0 <NA> <NA> direct 14.444218
#> 22: 2011 261 122741.8 male Burgenland direct 16.465679
#> 23: 2011 288 137822.2 female Burgenland direct 21.245704
#> 24: 2011 359 182732.9 male Vorarlberg direct 12.590109
#> 25: 2011 374 194622.1 female Vorarlberg direct 17.125259
#> 26: 2011 440 253143.7 male Salzburg direct 11.189644
#> 27: 2011 484 282307.3 female Salzburg direct 18.213248
#> 28: 2011 517 268581.4 male Carinthia direct 12.789630
#> 29: 2011 561 295066.6 female Carinthia direct 16.745958
#> 30: 2011 650 339566.5 male Tyrol direct 12.531181
#> 31: 2011 667 362332.5 female Tyrol direct 16.522466
#> 32: 2011 1128 571011.7 male Styria direct 10.163611
#> 33: 2011 1132 774405.4 male Vienna direct 16.616484
#> 34: 2011 1167 596033.3 female Styria direct 15.322461
#> 35: 2011 1190 824525.6 female Vienna direct 18.582643
#> 36: 2011 1363 684272.5 male Upper Austria direct 11.360200
#> 37: 2011 1387 772593.2 female Lower Austria direct 16.109340
#> 38: 2011 1417 783115.8 male Lower Austria direct 12.419104
#> 39: 2011 1442 737347.5 female Upper Austria direct 14.426605
#> 40: 2011 7267 3979571.7 male <NA> direct 12.819212
#> 41: 2011 7560 4202650.3 female <NA> direct 16.624882
#> 42: 2011 14827 8182222.0 <NA> <NA> direct 14.773925
#> 43: 2012 261 122741.8 male Burgenland direct 16.005251
#> 44: 2012 288 137822.2 female Burgenland direct 19.646025
#> 45: 2012 359 182732.9 male Vorarlberg direct 12.650134
#> 46: 2012 374 194622.1 female Vorarlberg direct 16.089942
#> 47: 2012 440 253143.7 male Salzburg direct 11.305057
#> 48: 2012 484 282307.3 female Salzburg direct 18.150559
#> 49: 2012 517 268581.4 male Carinthia direct 14.157852
#> 50: 2012 561 295066.6 female Carinthia direct 15.926182
#> 51: 2012 650 339566.5 male Tyrol direct 15.104689
#> 52: 2012 667 362332.5 female Tyrol direct 17.449297
#> 53: 2012 1128 571011.7 male Styria direct 11.019424
#> 54: 2012 1132 774405.4 male Vienna direct 16.099733
#> 55: 2012 1167 596033.3 female Styria direct 13.902894
#> 56: 2012 1190 824525.6 female Vienna direct 16.796126
#> 57: 2012 1363 684272.5 male Upper Austria direct 13.557275
#> 58: 2012 1387 772593.2 female Lower Austria direct 16.563011
#> 59: 2012 1417 783115.8 male Lower Austria direct 13.606128
#> 60: 2012 1442 737347.5 female Upper Austria direct 15.494037
#> 61: 2012 7267 3979571.7 male <NA> direct 13.760647
#> 62: 2012 7560 4202650.3 female <NA> direct 16.261469
#> 63: 2012 14827 8182222.0 <NA> <NA> direct 15.045149
#> 64: 2013 261 122741.8 male Burgenland direct 20.636575
#> 65: 2013 288 137822.2 female Burgenland direct 21.223485
#> 66: 2013 359 182732.9 male Vorarlberg direct 12.311667
#> 67: 2013 374 194622.1 female Vorarlberg direct 15.716327
#> 68: 2013 440 253143.7 male Salzburg direct 11.427862
#> 69: 2013 484 282307.3 female Salzburg direct 17.234804
#> 70: 2013 517 268581.4 male Carinthia direct 12.594461
#> 71: 2013 561 295066.6 female Carinthia direct 13.368498
#> 72: 2013 650 339566.5 male Tyrol direct 14.564544
#> 73: 2013 667 362332.5 female Tyrol direct 16.559763
#> 74: 2013 1128 571011.7 male Styria direct 11.942973
#> 75: 2013 1132 774405.4 male Vienna direct 16.144697
#> 76: 2013 1167 596033.3 female Styria direct 14.377224
#> 77: 2013 1190 824525.6 female Vienna direct 17.419665
#> 78: 2013 1363 684272.5 male Upper Austria direct 13.461762
#> 79: 2013 1387 772593.2 female Lower Austria direct 14.978617
#> 80: 2013 1417 783115.8 male Lower Austria direct 13.710926
#> 81: 2013 1442 737347.5 female Upper Austria direct 15.272207
#> 82: 2013 7267 3979571.7 male <NA> direct 13.889623
#> 83: 2013 7560 4202650.3 female <NA> direct 15.837536
#> 84: 2013 14827 8182222.0 <NA> <NA> direct 14.890134
#> year n N gender region estimate_type val_povertyRisk
#> stE_povertyRisk
#> <num>
#> 1: 1.4431618
#> 2: 3.6735767
#> 3: 1.4934327
#> 4: 1.9559294
#> 5: 3.3468129
#> 6: 3.2160653
#> 7: 1.0833181
#> 8: 2.4398838
#> 9: 1.9952530
#> 10: 2.6276076
#> 11: 0.9946241
#> 12: 2.1249192
#> 13: 1.5379803
#> 14: 3.2649315
#> 15: 0.8511495
#> 16: 0.3267907
#> 17: 0.4956941
#> 18: 0.7527641
#> 19: 0.6303579
#> 20: 0.7697657
#> 21: 0.6910876
#> 22: 0.2422727
#> 23: 2.8328214
#> 24: 1.5469103
#> 25: 2.7661600
#> 26: 3.0055654
#> 27: 4.1314584
#> 28: 1.2450208
#> 29: 1.9472010
#> 30: 2.0003031
#> 31: 1.8694584
#> 32: 0.8604726
#> 33: 2.6417920
#> 34: 0.8508027
#> 35: 3.5113719
#> 36: 1.5951153
#> 37: 0.4865375
#> 38: 0.5181370
#> 39: 1.1713676
#> 40: 0.5190553
#> 41: 0.7141305
#> 42: 0.6186808
#> 43: 1.1196609
#> 44: 3.1025139
#> 45: 0.7009807
#> 46: 3.3653762
#> 47: 2.6601005
#> 48: 4.4074740
#> 49: 1.9924914
#> 50: 2.0065407
#> 51: 1.9054850
#> 52: 0.8418672
#> 53: 1.4551381
#> 54: 2.0495089
#> 55: 0.8344010
#> 56: 2.9610239
#> 57: 2.0961807
#> 58: 0.2274939
#> 59: 1.0362823
#> 60: 2.1069955
#> 61: 0.4681727
#> 62: 0.6522503
#> 63: 0.5684274
#> 64: 0.8181103
#> 65: 2.3455299
#> 66: 1.0540283
#> 67: 1.9742950
#> 68: 2.5023987
#> 69: 2.7559770
#> 70: 2.6805977
#> 71: 1.8236565
#> 72: 2.5781278
#> 73: 2.7238161
#> 74: 1.7085255
#> 75: 0.6896327
#> 76: 0.5959627
#> 77: 1.4252120
#> 78: 1.7905396
#> 79: 0.5841637
#> 80: 1.3225249
#> 81: 1.8833088
#> 82: 0.3592972
#> 83: 0.1157311
#> 84: 0.1781831
#> stE_povertyRisk
# use average over 3 periods for standard error estimation
# and calculate estimate for difference of
# period 2011 and 2012 inclulding standard errors
period.diff <- c("2012-2011")
err.est <- calc.stError(
dat_boot_calib, var = "povertyRisk", fun = weightedRatio,
period.diff = period.diff, # <- take difference of periods 2012 and 2011
period.mean = 3) # <- average over 3 periods
err.est$Estimates
#> Key: <year, n, N, estimate_type>
#> year n N estimate_type
#> <char> <num> <num> <char>
#> 1: 2010 14827 8182222 direct
#> 2: 2010_2011_2012 14827 8182222 period average
#> 3: 2011 14827 8182222 direct
#> 4: 2011_2012_2013 14827 8182222 period average
#> 5: 2012 14827 8182222 direct
#> 6: 2012-2011 14827 8182222 period difference
#> 7: 2012-2011_mean 14827 8182222 difference between period averages
#> 8: 2013 14827 8182222 direct
#> val_povertyRisk stE_povertyRisk
#> <num> <num>
#> 1: 14.4442182 0.6910876
#> 2: 14.7544308 0.5625162
#> 3: 14.7739255 0.6186808
#> 4: 14.9030692 0.4339878
#> 5: 15.0451487 0.5684274
#> 6: 0.2712233 0.2851669
#> 7: 0.1486385 0.2330347
#> 8: 14.8901335 0.1781831
# for more examples see https://statistikat.github.io/surveysd/articles/error_estimation.html