qvar  R Documentation 
qvar
(for "quick variance estimation") is a function
performing analytical variance estimation in most common cases, that is:
stratified simple random sampling
nonresponse correction (if any) through reweighting
calibration (if any)
Used with define = TRUE
, it defines a socalled variance wrapper, that
is a standalone readytouse function that can be applied to the survey dataset
without having to specify the methodological characteristics of the survey
(see define_variance_wrapper
).
qvar( data, ..., by = NULL, where = NULL, alpha = 0.05, display = TRUE, id, dissemination_dummy, dissemination_weight, sampling_weight, strata = NULL, scope_dummy = NULL, nrc_weight = NULL, response_dummy = NULL, nrc_dummy = NULL, calibration_weight = NULL, calibration_dummy = NULL, calibration_var = NULL, define = FALSE, envir = parent.frame() )
data 
The 
... 
One or more calls to a statistic wrapper (e.g. 
by 
A qualitative variable whose levels are used to define domains on which the variance estimation is performed. 
where 
A logical vector indicating a domain on which the variance estimation is to be performed. 
alpha 
A numeric vector of length 1 indicating the threshold
for confidence interval derivation ( 
display 
A logical verctor of length 1 indicating whether the result of the estimation should be displayed or not. 
id 
The identification variable of the units in 
dissemination_dummy 
A character vector of length 1, the name
of the logical variable in 
dissemination_weight 
A character vector of length 1, the name
of the numerical variable in 
sampling_weight 
A character vector of length 1, the name of the
numeric variable in 
strata 
A character vector of length 1, the name of the factor
variable in 
scope_dummy 
A character vector of length 1, the name of the logical
variable in 
nrc_weight 
A character vector of length 1, the name of the
numerical variable in 
response_dummy 
A character vector of length 1, the name of of the logical
variable in 
nrc_dummy 
A character vector of length 1, the name of
the logical variable in 
calibration_weight 
A character vector of length 1, the name of the
numerical variable in 
calibration_dummy 
A character vector of length 1, the name of of the logical
variable in 
calibration_var 
A character vector, the name of the variable(s) used in
the calibration process. Logical variables are coerced to numeric.
Character and factor variables are automatically discretized.

define 
Logical vector of lentgh 1. Should a variance wrapper be defined instead of performing a variance estimation (see details and examples)? 
envir 
An environment containing a binding to 
qvar
performs not only technical but also
methodological checks in order to ensure that the standard variance
estimation methodology does apply (e.g. equal probability of
inclusion within strata, number of units per stratum).
Used with define = TRUE
, the function returns a variance
estimation wrapper, that is a readytouse function that
implements the described variance estimation methodology and
contains all necessary data to do so (see examples).
Note: To some extent, qvar
is analogous to the qplot
function
in the ggplot2 package, as it is an easiertouse function for common
cases. More complex cases are to be handled by using the core functions of
the gustave package, e.g. define_variance_wrapper
.
define_variance_wrapper
, standard_statistic_wrapper
### Example from the Information and communication technologies (ICT) survey # The (simulated) Information and communication technologies (ICT) survey # has the following characteristics: #  stratified onestage sampling design #  nonresponse 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", # Nonresponse 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) ) # 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 ) # 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)) ## 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) ) # 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) ) # Onthefly 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(magrittr) library(dplyr) ict_survey %>% precision_ict("Internet speed above 100 Mbps" = mean(speed_quanti > 100)) %>% select(label, est, lower, upper)
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