srvyr: as_survey_twophase – R documentation

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as_survey_twophase

Create a tbl_svy survey object using two phase design

Description

Create a survey object by specifying the survey's two phase design. It is a wrapper around twophase. 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.

Usage

as_survey_twophase(.data, ...)

## S3 method for class 'data.frame'
as_survey_twophase(
  .data,
  id,
  strata = NULL,
  probs = NULL,
  weights = NULL,
  fpc = NULL,
  subset,
  method = c("full", "approx", "simple"),
  ...
)

## S3 method for class 'twophase2'
as_survey_twophase(.data, ...)

Arguments

`.data`	A data frame (which contains the variables specified below)
`...`	ignored
`id`	list of two sets of variable names for sampling unit identifiers
`strata`	list of two sets of variable names (or `NULLs`) for stratum identifiers
`probs`	list of two sets of variable names (or `NULLs`) for sampling probabilities
`weights`	Only for method = "approx", list of two sets of variable names (or `NULLs`) for sampling weights
`fpc`	list of two sets of variables (or `NULLs` for finite population corrections
`subset`	bare name of a variable which specifies which observations are selected in phase 2
`method`	"full" requires (much) more memory, but gives unbiased variance estimates for general multistage designs at both phases. "simple" or "approx" use less memory, and is correct for designs with simple random sampling at phase one and stratified randoms sampling at phase two. See `twophase` for more details.

Value

An object of class tbl_svy

Examples

# Examples from ?survey::twophase
# two-phase simple random sampling.
data(pbc, package="survival")
library(dplyr)

pbc <- pbc %>%
  mutate(randomized = !is.na(trt) & trt > 0,
         id = row_number())
d2pbc <- pbc %>%
  as_survey_twophase(id = list(id, id), subset = randomized)

d2pbc %>% summarize(mean = survey_mean(bili))

# two-stage sampling as two-phase
library(survey)
data(mu284)

mu284_1 <- mu284 %>%
  dplyr::slice(c(1:15, rep(1:5, n2[1:5] - 3))) %>%
  mutate(id = row_number(),
         sub = rep(c(TRUE, FALSE), c(15, 34-15)))

dmu284 <- mu284 %>%
  as_survey_design(ids = c(id1, id2), fpc = c(n1, n2))
# first phase cluster sample, second phase stratified within cluster
d2mu284 <- mu284_1 %>%
  as_survey_twophase(id = list(id1, id), strata = list(NULL, id1),
                  fpc = list(n1, NULL), subset = sub)
dmu284 %>%
  summarize(total = survey_total(y1),
            mean = survey_mean(y1))
d2mu284 %>%
  summarize(total = survey_total(y1),
            mean = survey_mean(y1))

# dplyr 0.7 introduced new style of NSE called quosures
# See `vignette("programming", package = "dplyr")` for details
ids <- quo(list(id, id))
d2pbc <- pbc %>%
  as_survey_twophase(id = !!ids, subset = "randomized")

srvyr

'dplyr'-Like Syntax for Summary Statistics of Survey Data

v1.0.1

GPL-2 | GPL-3

Authors

Greg Freedman Ellis [aut, cre], Thomas Lumley [ctb], Tomasz Żółtak [ctb], Ben Schneider [aut, ctb], Pavel N. Krivitsky [ctb]

Initial release

2021-03-28