Plot results of calc.stError()
Arguments
- x
object of class 'surveysd' output of function calc.stError
- variable
Name of the variable for which standard errors have been calcualated in
dat- type
can bei either
"summary"or"grouping", default value is"summary". For"summary"a barplot is created giving an overview of the number of estimates having the flagsmallGroup,cvHigh, both or none of them. For 'grouping' results for point estimate and standard error are plotted for pre defined groups.- groups
If
type='grouping'variables must be defined by which the data is grouped. Only 2 levels are supported as of right now. If only one group is defined the higher group will be the estimate over the whole period. Results are plotted for the first argument ingroupsas well as for the combination ofgroups[1]andgroups[2].- sd.type
can bei either
'ribbon'or'dot'and is only used iftype='grouping'. Default is"dot"Forsd.type='dot'point estimates are plotted and flagged if the corresponding standard error and/or the standard error using the mean over k-periods exceeded the valuecv.limit(see calc.stError). Forsd.type='ribbon'the point estimates including ribbons, defined by point estimate +- estimated standard error are plotted. The calculated standard errors using the mean over k periods are plotted using less transparency. Results for the higher level (~groups[1]) are coloured grey.- ...
additional arguments supplied to plot.
Examples
library(surveysd)
set.seed(1234)
eusilc <- demo.eusilc(n = 3, prettyNames = TRUE)
dat_boot <- draw.bootstrap(eusilc, REP = 3, hid = "hid", weights = "pWeight",
strata = "region", period = "year")
# calibrate weight for bootstrap replicates
dat_boot_calib <- recalib(dat_boot, conP.var = "gender", conH.var = "region")
#> Iteration stopped after 1 steps
#> Convergence reached
#> Iteration stopped after 1 steps
#> Convergence reached
#> 10:Not yet converged for P-Constraint1
#> 10:Not yet converged for H-Constraint1
#> 20:Not yet converged for P-Constraint1
#> 20:Not yet converged for H-Constraint1
#> 30:Not yet converged for P-Constraint1
#> 30:Not yet converged for H-Constraint1
#> 40:Not yet converged for P-Constraint1
#> 40:Not yet converged for H-Constraint1
#> 50:Not yet converged for P-Constraint1
#> 50:Not yet converged for H-Constraint1
#> 60:Not yet converged for P-Constraint1
#> 60:Not yet converged for H-Constraint1
#> 70:Not yet converged for P-Constraint1
#> 70:Not yet converged for H-Constraint1
#> 80:Not yet converged for P-Constraint1
#> 80:Not yet converged for H-Constraint1
#> 90:Not yet converged for P-Constraint1
#> 90:Not yet converged for H-Constraint1
#> 100:Not yet converged for P-Constraint1
#> year gender maxFac N epsP CalibMargin PopMargin
#> <fctr> <fctr> <num> <int> <num> <num> <num>
#> 1: 2012 male 0.02215357 7267 0.01 4067733 3979572
#> 2: 2012 female 0.02215357 7560 0.01 4295754 4202650
#> 3: 2011 female 0.01689261 7560 0.01 4273644 4202650
#> 4: 2011 male 0.01689261 7267 0.01 4046797 3979572
#> 5: 2010 female 0.01340012 7560 0.01 4258966 4202650
#> 6: 2010 male 0.01340012 7267 0.01 4032898 3979572
#> -----------------------------------------
#> 100:Not yet converged for H-Constraint1
#> year region maxFac N epsH sumCalibWeight PopMargin
#> <fctr> <fctr> <num> <int> <num> <num> <num>
#> 1: 2012 Salzburg 0.02167343 924 0.02 214917.8 219679
#> 2: 2012 Vorarlberg 0.02167343 733 0.02 141852.5 144995
#> 3: 2012 Carinthia 0.02167343 1078 0.02 228679.9 233746
#> 4: 2012 Tyrol 0.02167343 1317 0.02 272969.7 279017
#> 5: 2012 Burgenland 0.02167343 549 0.02 107465.3 109846
#> 6: 2012 Styria 0.02167343 2295 0.02 479738.1 490366
#> 7: 2012 Lower Austria 0.02167343 2804 0.02 633330.5 647361
#> 8: 2012 Upper Austria 0.02167343 2805 0.02 554721.9 567011
#> 9: 2012 Vienna 0.02167343 2322 0.02 795500.8 813124
#> -----------------------------------------
#> 110:Not yet converged for P-Constraint1
#> 110:Not yet converged for H-Constraint1
#> 120:Not yet converged for P-Constraint1
#> 120:Not yet converged for H-Constraint1
#> 130:Not yet converged for P-Constraint1
#> 130:Not yet converged for H-Constraint1
#> 140:Not yet converged for P-Constraint1
#> 140:Not yet converged for H-Constraint1
#> 150:Not yet converged for P-Constraint1
#> 150:Not yet converged for H-Constraint1
#> 160:Not yet converged for P-Constraint1
#> 160:Not yet converged for H-Constraint1
#> 170:Not yet converged for P-Constraint1
#> 170:Not yet converged for H-Constraint1
#> 180:Not yet converged for P-Constraint1
#> 180:Not yet converged for H-Constraint1
#> 190:Not yet converged for P-Constraint1
#> 190:Not yet converged for H-Constraint1
#> 200:Not yet converged for P-Constraint1
#> year gender maxFac N epsP CalibMargin PopMargin
#> <fctr> <fctr> <num> <int> <num> <num> <num>
#> 1: 2012 male 0.02215357 7267 0.01 4067733 3979572
#> 2: 2012 female 0.02215357 7560 0.01 4295754 4202650
#> 3: 2011 male 0.01689261 7267 0.01 4046797 3979572
#> 4: 2011 female 0.01689261 7560 0.01 4273644 4202650
#> 5: 2010 male 0.01340012 7267 0.01 4032898 3979572
#> 6: 2010 female 0.01340012 7560 0.01 4258966 4202650
#> -----------------------------------------
#> 200:Not yet converged for H-Constraint1
#> year region maxFac N epsH sumCalibWeight PopMargin
#> <fctr> <fctr> <num> <int> <num> <num> <num>
#> 1: 2012 Vienna 0.02167343 2322 0.02 795500.8 813124
#> 2: 2012 Tyrol 0.02167343 1317 0.02 272969.7 279017
#> 3: 2012 Burgenland 0.02167343 549 0.02 107465.3 109846
#> 4: 2012 Upper Austria 0.02167343 2805 0.02 554721.9 567011
#> 5: 2012 Styria 0.02167343 2295 0.02 479738.1 490366
#> 6: 2012 Vorarlberg 0.02167343 733 0.02 141852.5 144995
#> 7: 2012 Salzburg 0.02167343 924 0.02 214917.8 219679
#> 8: 2012 Carinthia 0.02167343 1078 0.02 228679.9 233746
#> 9: 2012 Lower Austria 0.02167343 2804 0.02 633330.5 647361
#> -----------------------------------------
#> Warning: Not converged in 200 steps
#> No convergence reached
#> Calibration failed for bootstrap replicates w3
#> Corresponding bootstrap replicates will be discarded
#> Returning 2 calibrated bootstrap weights
# estimate weightedRatio for povmd60 per period
group <- list("gender", "region", c("gender", "region"))
err.est <- calc.stError(dat_boot_calib, var = "povertyRisk",
fun = weightedRatio,
group = group , period.mean = NULL)
plot(err.est)
# plot results for gender
# dotted line is the result on the national level
plot(err.est, type = "grouping", groups = "gender")
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's shape values.
# plot results for rb090 in each db040
# with standard errors as ribbons
plot(err.est, type = "grouping", groups = c("gender", "region"), sd.type = "ribbon")
