Provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.
Gustave (Gustave: a User-oriented Statistical Toolkit for Analytical Variance Estimation) is an R package that provides a toolkit for analytical variance estimation in survey sampling.
Apart from the implementation of standard variance estimators (Sen-Yates-Grundy, Deville-Tillé), its main feature is to help he methodologist produce easy-to-use variance estimation wrappers, where systematic operations (statistic linearization, domain estimation) are handled in a consistent and transparent way.
The ready-to-use variance estimation wrapper
qvar(), adapted for common cases (e.g. stratified simple random sampling, non-response correction through reweighting in homogeneous response groups, calibration), is also included. The core functions of the package (e.g.
define_variance_wrapper()) are to be used for more complex cases.
gustave is available on CRAN and can therefore be installed with the
However, if you wish to install the latest version of gustave, you can use
devtools::install_github() to install it directly from the github.com repository:
In this example, we aim at estimating the variance of estimators computed using simulated data inspired from the Information and communication technology (ICT) survey. This survey has the following characteristics:
The ICT simulated data files are shipped with the gustave package:
library(gustave) data(package = "gustave") ? ict_survey
A variance estimation can be perform in a single call of
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) )
The survey methodology description is however cumbersome when several variance estimations are to be conducted. As it does not change from one estimation to another, it could be defined once and for all and then re-used for all variance estimations.
qvar() allows for this by defining a so-called variance wrapper, that is an easy-to-use function where the variance estimation methodology for the given survey is implemented and all the technical data used to do so included.
# Definition of the variance estimation wrapper precision_ict 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 ) # Use of the variance estimation wrapper precision_ict(ict_sample, mean(employees)) # The variance estimation wrapper can also be used on the survey file precision_ict(ict_survey, mean(speed_quanti))
The variance estimation wrapper is much easier-to-use than a standard variance estimation function:
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) )
domain estimation with where and by arguments
precision_ict(ict_survey, mean(speed_quanti), where = employees >= 50 ) precision_ict(ict_survey, mean(speed_quanti), by = division ) # 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) )
handy variable evaluation
# On-the-fly evaluation (e.g. discretization) precision_ict(ict_survey, mean(speed_quanti > 100)) # Automatic discretization for qualitative (character or factor) variables precision_ict(ict_survey, mean(speed_quali)) # Standard evaluation capabilities variables_of_interest <- c("speed_quanti", "speed_quali") precision_ict(ict_survey, mean(variables_of_interest))
Integration with %>% and dplyr
library(dplyr) ict_survey %>% precision_ict("Internet speed above 100 Mbps" = mean(speed_quanti > 100)) %>% select(label, est, lower, upper)
From the methodological point of view, this package is related to the Poulpe SAS macro (in French) developed at the French statistical institute. From the implementation point of view, some inspiration was found in the ggplot2 package. The idea of developing an R package on this specific topic was stimulated by the icarus package and its author.
Breaking: Heavy remanufacturing of
technical_dataargument offers a more consistent way to include technical data within the enclosing environment of the wrapper.
objects_to_includeis kept for non-data objects (such as additional statistic wrappers) or advanced customization.
technical_paramargument offers a more convenient way to specify default values for parameters used by the variance function.
default$weight. This means that the reference weight used for point estimation and linearization is set while defining the variance wrapper and not at run-time.
stat, which was a remain of an early implementation of linearization functions, is not a parameter of the variance wrappers anymore. Its purpose (to apply a given variance wrapper to several variables without having to type the name of the linearization wrapper) is now covered by the standard evaluation capabilities of statistic wrappers (see below).
defaultis replaced by
default$statare no longer needed. As for
default$alpha, its value is set to 0.05 and cannot be changed anymore while defining the variance wrapper (as this can easily be done afterwards using
Breaking: Rebranding and heavy remanufacturing of
define_statistic_wrapper (previously known as
define_linearization_wrapper), added support for standard evaluation (see
qvar function allows for a straigthforward variance estimation in common cases (stratified simple random sampling with non-response through reweighting and calibration) and performs both technical and methodological checks.
Some normalization in function names:
Example data: calibration variables in ict_sample instead of ict_survey, new LFS example data
Significant increase of unit tests