Running examples in 'gustave-Ex.R' failed
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> ### Name: qvar
> ### Title: Quickly perform a variance estimation in common cases
> ### Aliases: qvar
> 
> ### ** Examples
> 
> ### Example from the Information and communication technologies (ICT) survey
> 
> # The (simulated) Information and communication technologies (ICT) survey 
> # has the following characteristics: 
> # - stratified one-stage sampling design
> # - non-response correction through reweighting in homogeneous response groups
> # - calibration on margins.
> 
> # The ict_survey data.frame is a (simulated) subset of the ICT 
> # survey file containing the variables of interest for the 612
> # responding firms.
> 
> # The ict_sample data.frame is the (simulated) sample of 650
> # firms corresponding to the ict_survey file. It contains all
> # technical information necessary to estimate a variance with
> # the qvar() function.
> 
> ## Methodological description of the survey
> 
> # Direct call of qvar()
> qvar(
+ 
+   # Sample file
+   data = ict_sample,
+   
+   # Dissemination and identification information
+   dissemination_dummy = "dissemination",
+   dissemination_weight = "w_calib",
+   id = "firm_id",
+   
+   # Scope
+   scope_dummy = "scope",
+   
+   # Sampling design
+   sampling_weight = "w_sample", 
+   strata = "strata",
+   
+   # Non-response correction
+   nrc_weight = "w_nrc", 
+   response_dummy = "resp", 
+   hrg = "hrg",
+   
+   # Calibration
+   calibration_weight = "w_calib",
+   calibration_var = c(paste0("N_", 58:63), paste0("turnover_", 58:63)),
+   
+   # Statistic(s) and variable(s) of interest
+   mean(employees)
+  
+ )
Survey variance estimation with the gustave package

The following features are taken into account:
  - stratified simple random sampling
  - out-of-scope units
  - non-response correction through reweighting
  - calibration on margins

Note: The strata variable (strata) is of type character. It is automatically coerced to factor.

                 call   n      est variance      std      cv    lower    upper
1 mean(y = employees) 506 89.09692 47.06522 6.860409 7.69994 75.65077 102.5431
> 
> # Definition of a variance estimation wrapper
> precision_ict <- qvar(
+ 
+   # As before
+   data = ict_sample,
+   dissemination_dummy = "dissemination",
+   dissemination_weight = "w_calib",
+   id = "firm_id",
+   scope_dummy = "scope",
+   sampling_weight = "w_sample", 
+   strata = "strata",
+   nrc_weight = "w_nrc", 
+   response_dummy = "resp", 
+   hrg = "hrg",
+   calibration_weight = "w_calib",
+   calibration_var = c(paste0("N_", 58:63), paste0("turnover_", 58:63)),
+   
+   # Replacing the variables of interest by define = TRUE
+   define = TRUE
+   
+ )
Survey variance estimation with the gustave package

The following features are taken into account:
  - stratified simple random sampling
  - out-of-scope units
  - non-response correction through reweighting
  - calibration on margins

Note: The strata variable (strata) is of type character. It is automatically coerced to factor.

Note: As define = TRUE, a ready-to-use variance wrapper is (invisibly) returned.

> 
> # Use of the variance estimation wrapper
> precision_ict(ict_sample, mean(employees))
Warning: 144 observations do not match any responding units of the survey. They are discarded.

                 call   n      est variance      std      cv    lower    upper
1 mean(y = employees) 506 89.09692 47.06522 6.860409 7.69994 75.65077 102.5431
> 
> # The variance estimation wrapper can also be used on the survey file
> precision_ict(ict_survey, mean(speed_quanti))
                    call   n      est variance      std       cv    lower
1 mean(y = speed_quanti) 506 36.81271 2.517231 1.586579 4.309866 33.70307
     upper
1 39.92234
> 
> ## Features of the variance estimation wrapper
> 
> # Several statistics in one call (with optional labels)
> precision_ict(ict_survey, 
+   "Mean internet speed in Mbps" = mean(speed_quanti), 
+   "Turnover per employee" = ratio(turnover, employees)
+ )
                        label                                     call   n
1 Mean internet speed in Mbps                   mean(y = speed_quanti) 506
2       Turnover per employee ratio(num = turnover, denom = employees) 506
        est   variance       std       cv     lower     upper
1  36.81271   2.517231  1.586579 4.309866  33.70307  39.92234
2 260.39641 402.017218 20.050367 7.699940 221.09842 299.69441
> 
> # Domain estimation with where and by arguments
> precision_ict(ict_survey, 
+   mean(speed_quanti), 
+   where = employees >= 50
+ )
                                             call   n      est variance
1 mean(y = speed_quanti, where = employees >= 50) 193 41.78957 11.98235
       std       cv    lower    upper
1 3.461553 8.283293 35.00505 48.57409
> precision_ict(ict_survey, 
+   mean(speed_quanti), 
+   by = division
+ )
                                   call by   n      est  variance      std
1 mean(y = speed_quanti, by = division) 58 152 36.22628  6.672118 2.583044
2 mean(y = speed_quanti, by = division) 59  69 24.11386  7.106194 2.665745
3 mean(y = speed_quanti, by = division) 60  35 53.24218 32.706150 5.718929
4 mean(y = speed_quanti, by = division) 61  41 85.04560 95.745360 9.784956
5 mean(y = speed_quanti, by = division) 62 170 34.55942  6.219674 2.493927
6 mean(y = speed_quanti, by = division) 63  39 35.07277 28.897546 5.375644
         cv    lower     upper
1  7.130305 31.16361  41.28895
2 11.054824 18.88909  29.33862
3 10.741351 42.03328  64.45107
4 11.505540 65.86744 104.22376
5  7.216345 29.67142  39.44743
6 15.327115 24.53670  45.60884
> 
> # Domain may differ from one estimator to another
> precision_ict(ict_survey, 
+   "Mean turnover, firms with 50 employees or more" = mean(turnover, where = employees >= 50),
+   "Mean turnover, firms with 100 employees or more" = mean(turnover, where = employees >= 100)
+ )
                                            label
1  Mean turnover, firms with 50 employees or more
2 Mean turnover, firms with 100 employees or more
                                          call   n      est variance      std
1  mean(y = turnover, where = employees >= 50) 193 29888.95 15978626 3997.327
2 mean(y = turnover, where = employees >= 100) 137 35825.06 33641742 5800.150
        cv    lower    upper
1 13.37393 22054.33 37723.56
2 16.19020 24456.97 47193.14
> 
> # On-the-fly evaluation (e.g. discretization)
> precision_ict(ict_survey, mean(speed_quanti > 100))
                          call   n       est     variance        std       cv
1 mean(y = speed_quanti > 100) 506 0.1606019 0.0002552473 0.01597646 9.947865
      lower     upper
1 0.1292886 0.1919152
> 
> # Automatic discretization for qualitative (character or factor) variables
> precision_ict(ict_survey, mean(speed_quali))
                   call                     mod   n        est     variance
1 mean(y = speed_quali)        Less than 2 Mbps 506 0.03311273 7.381933e-05
2 mean(y = speed_quali)   Between 2 and 10 Mbps 506 0.34282291 5.140507e-04
3 mean(y = speed_quali)  Between 10 and 30 Mbps 506 0.32560461 5.040504e-04
4 mean(y = speed_quali) Between 30 and 100 Mbps 506 0.13785785 2.681454e-04
5 mean(y = speed_quali)          Above 100 Mbps 506 0.16060190 2.552473e-04
          std        cv      lower      upper
1 0.008591818 25.947175 0.01627308 0.04995238
2 0.022672687  6.613528 0.29838526 0.38726056
3 0.022451068  6.895193 0.28160133 0.36960790
4 0.016375144 11.878282 0.10576316 0.16995255
5 0.015976460  9.947865 0.12928861 0.19191519
> 
> # Standard evaluation capabilities
> variables_of_interest <- c("speed_quanti", "speed_quali")
> precision_ict(ict_survey, mean(variables_of_interest))
                    call                     mod   n         est     variance
1 mean(y = speed_quanti)                    <NA> 506 36.81270668 2.517231e+00
2  mean(y = speed_quali)        Less than 2 Mbps 506  0.03311273 7.381933e-05
3  mean(y = speed_quali)   Between 2 and 10 Mbps 506  0.34282291 5.140507e-04
4  mean(y = speed_quali)  Between 10 and 30 Mbps 506  0.32560461 5.040504e-04
5  mean(y = speed_quali) Between 30 and 100 Mbps 506  0.13785785 2.681454e-04
6  mean(y = speed_quali)          Above 100 Mbps 506  0.16060190 2.552473e-04
          std        cv       lower       upper
1 1.586578504  4.309866 33.70306995 39.92234341
2 0.008591818 25.947175  0.01627308  0.04995238
3 0.022672687  6.613528  0.29838526  0.38726056
4 0.022451068  6.895193  0.28160133  0.36960790
5 0.016375144 11.878282  0.10576316  0.16995255
6 0.015976460  9.947865  0.12928861  0.19191519
> 
> # Integration with %>% and dplyr
> library(magrittr)
> library(dplyr)
Error: package or namespace load failed for 'dplyr' in library.dynam(lib, package, package.lib):
 DLL 'dplyr' not found: maybe not installed for this architecture?
Execution halted