as_survey_rep: Create a tbl_svy survey object using replicate weights

In gergness/srvyr: 'dplyr'-Like Syntax for Summary Statistics of Survey Data

Create a tbl_svy survey object using replicate weights

Description

Create a survey object with replicate weights.

Usage

as_survey_rep(.data, ...)

## S3 method for class 'data.frame'
as_survey_rep(
  .data,
  variables = NULL,
  repweights = NULL,
  weights = NULL,
  type = c("BRR", "Fay", "JK1", "JKn", "bootstrap", "successive-difference", "ACS",
    "other"),
  combined_weights = TRUE,
  rho = NULL,
  bootstrap_average = NULL,
  scale = NULL,
  rscales = NULL,
  fpc = NULL,
  fpctype = c("fraction", "correction"),
  mse = getOption("survey.replicates.mse"),
  ...
)

## S3 method for class 'tbl_lazy'
as_survey_rep(
  .data,
  variables = NULL,
  repweights = NULL,
  weights = NULL,
  type = c("BRR", "Fay", "JK1", "JKn", "bootstrap", "successive-difference", "ACS",
    "other"),
  combined_weights = TRUE,
  rho = NULL,
  bootstrap_average = NULL,
  scale = NULL,
  rscales = NULL,
  fpc = NULL,
  fpctype = c("fraction", "correction"),
  mse = getOption("survey.replicates.mse"),
  ...
)

## S3 method for class 'svyrep.design'
as_survey_rep(.data, ...)

## S3 method for class 'survey.design2'
as_survey_rep(
  .data,
  type = c("auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap", "mrbbootstrap",
    "Fay"),
  rho = 0,
  fpc = NULL,
  fpctype = NULL,
  ...,
  compress = TRUE,
  mse = getOption("survey.replicates.mse")
)

## S3 method for class 'tbl_svy'
as_survey_rep(
  .data,
  type = c("auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap", "mrbbootstrap",
    "Fay"),
  rho = 0,
  fpc = NULL,
  fpctype = NULL,
  ...,
  compress = TRUE,
  mse = getOption("survey.replicates.mse")
)

Arguments

.data	A data frame (which contains the variables specified below)
...	ignored
variables	Variables to include in the design (default is all)
repweights	Variables specifying the replication weight variables
weights	Variables specifying sampling weights
type	Type of replication weights
combined_weights	TRUE if the repweights already include the sampling weights. This is usually the case.
rho	Shrinkage factor for weights in Fay's method
bootstrap_average	For type = "bootstrap", if the bootstrap weights have been averaged, gives the number of iterations averaged over.
scale, rscales	Scaling constant for variance, see svrepdesign for more information.
fpc	Variables specifying a finite population correction, see svrepdesign for more details.
fpctype	Finite population correction information
mse	if TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates
compress	if TRUE, store replicate weights in compressed form (if converting from design)

Details

If provided a data.frame, it is a wrapper around svrepdesign. All survey variables must be included in the data.frame itself. Variables are selected by using bare column names, or convenience functions described in select.

If provided a svyrep.design object from the survey package, it will turn it into a srvyr object, so that srvyr functions will work with it

If provided a survey design (survey.design2 or tbl_svy), it is a wrapper around as.svrepdesign, and will convert from a survey design to replicate weights.

Value

An object of class tbl_svy

Examples

# Examples from ?survey::svrepdesign()
library(survey)
library(dplyr)
data(scd)
# use BRR replicate weights from Levy and Lemeshow
scd <- scd %>%
  mutate(rep1 = 2 * c(1, 0, 1, 0, 1, 0),
         rep2 = 2 * c(1, 0, 0, 1, 0, 1),
         rep3 = 2 * c(0, 1, 1, 0, 0, 1),
         rep4 = 2 * c(0, 1, 0, 1, 1, 0))

scdrep <- scd %>%
  as_survey_rep(type = "BRR", repweights = starts_with("rep"),
                combined_weights = FALSE)

# dplyr 0.7 introduced new style of NSE called quosures
# See `vignette("programming", package = "dplyr")` for details
repwts <- quo(starts_with("rep"))
scdrep <- scd %>%
  as_survey_rep(type = "BRR", repweights = !!repwts,
                combined_weights = FALSE)

gergness/srvyr documentation built on Oct. 23, 2023, 2:35 a.m.