# Compare survey statistics calculated separately from different sets of replicate weights

Source:`R/svyby_repwts.R`

`svyby_repwts.Rd`

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.

## Usage

```
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?

## Value

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

```
if (FALSE) {
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)
}
```