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"),
degf = NULL,
...
)
# 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"),
degf = NULL,
...
)
# 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 therepweights
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- degf
Design degrees of freedom: a single number, or
NULL
, in which case a value will be computed automatically, which can be slow for very large data sets. Seesvrepdesign
for more details.- 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.
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)
#> Warning: No sampling weights provided: equal probability assumed
# 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)
#> Warning: No sampling weights provided: equal probability assumed