# Compare survey statistics calculated separately from different sets of replicate weights A modified version of the `svyby()` function from the `survey` package. Whereas `svyby()` calculates statistics separately for each subset formed by a specified grouping variable, `svyby_repwts()` calculates statistics separately for each replicate design, in addition to any additional user-specified grouping variables. ```r svyby_repwts( rep_designs, formula, by, FUN, ..., deff = FALSE, keep.var = TRUE, keep.names = TRUE, verbose = FALSE, vartype = c("se", "ci", "ci", "cv", "cvpct", "var"), drop.empty.groups = TRUE, return.replicates = FALSE, na.rm.by = FALSE, na.rm.all = FALSE, multicore = getOption("survey.multicore") ) ``` ## Arguments - `rep_designs`: The replicate-weights survey designs to be compared. Supplied either as: * A named list of replicate-weights survey design objects, for example `list('nr' = nr_adjusted_design, 'ue' = ue_adjusted_design)`. * A 'stacked' replicate-weights survey design object created by `stack_replicate_designs()`. The designs must all have the same number of columns of replicate weights, of the same type (bootstrap, JKn, etc.) - `formula`: A formula specifying the variables to pass to `FUN` - `by`: A formula specifying factors that define subsets - `FUN`: A function taking a formula and survey design object as its first two arguments. Usually a function from the `survey` package, such as `svytotal` or `svymean`. - `...`: Other arguments to `FUN` - `deff`: A value of `TRUE` or `FALSE`, indicating whether design effects should be estimated if possible. - `keep.var`: A value of `TRUE` or `FALSE`. If `FUN` returns a `svystat` object, indicates whether to extract standard errors from it. - `keep.names`: Define row names based on the subsets - `verbose`: If `TRUE`, print a label for each subset as it is processed. - `vartype`: Report variability as one or more of standard error, confidence interval, coefficient of variation, percent coefficient of variation, or variance - `drop.empty.groups`: If `FALSE`, report `NA` for empty groups, if `TRUE` drop them from the output - `return.replicates`: If `TRUE`, return all the replicates as the "replicates" attribute of the result. This can be useful if you want to produce custom summaries of the estimates from each replicate. - `na.rm.by`: If true, omit groups defined by `NA` values of the `by` variables - `na.rm.all`: If true, check for groups with no non-missing observations for variables defined by `formula` and treat these groups as empty - `multicore`: Use `multicore` package to distribute subsets over multiple processors? ## Returns An object of class `"svyby"`: a data frame showing the grouping factors and results of `FUN` for each combination of the grouping factors. The first grouping factor always consists of indicators for which replicate design was used for an estimate. ## Examples ```r ## Not run: suppressPackageStartupMessages(library(survey)) data(api) dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) dclus1$variables$response_status <- sample(x = c("Respondent", "Nonrespondent", "Ineligible", "Unknown eligibility"), size = nrow(dclus1), replace = TRUE) orig_rep_design <- as.svrepdesign(dclus1) # Adjust weights for cases with unknown eligibility ue_adjusted_design <- redistribute_weights( design = orig_rep_design, reduce_if = response_status %in% c("Unknown eligibility"), increase_if = !response_status %in% c("Unknown eligibility"), by = c("stype") ) # Adjust weights for nonresponse nr_adjusted_design <- redistribute_weights( design = ue_adjusted_design, reduce_if = response_status %in% c("Nonrespondent"), increase_if = response_status == "Respondent", by = c("stype") ) # Compare estimates from the three sets of replicate weights list_of_designs <- list('original' = orig_rep_design, 'unknown eligibility adjusted' = ue_adjusted_design, 'nonresponse adjusted' = nr_adjusted_design) ##_ First compare overall means for two variables means_by_design <- svyby_repwts(formula = ~ api00 + api99, FUN = svymean, rep_design = list_of_designs) print(means_by_design) ##_ Next compare domain means for two variables domain_means_by_design <- svyby_repwts(formula = ~ api00 + api99, by = ~ stype, FUN = svymean, rep_design = list_of_designs) print(domain_means_by_design) # Calculate confidence interval for difference between estimates ests_by_design <- svyby_repwts(rep_designs = list('NR-adjusted' = nr_adjusted_design, 'Original' = orig_rep_design), FUN = svymean, formula = ~ api00 + api99) differences_in_estimates <- svycontrast(stat = ests_by_design, contrasts = list( 'Mean of api00: NR-adjusted vs. Original' = c(1,-1,0,0), 'Mean of api99: NR-adjusted vs. Original' = c(0,0,1,-1) )) print(differences_in_estimates) confint(differences_in_estimates, level = 0.95) ## End(Not run) ```

svyby_repwts function