as_survey_twophase

0th

Percentile

Create a tbl_svy survey object using two phase design

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

# S3 method for 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 identifers

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 correect for designs with simple random sampling at phase one and stratifed randoms sampling at phase two. See twophase for more details.

Value

An object of class tbl_svy

Aliases
  • as_survey_twophase
  • as_survey_twophase.data.frame
  • as_survey_twophase.twophase2
Examples
# NOT RUN {
# 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")

# }
Documentation reproduced from package srvyr, version 0.3.5, License: GPL-2 | GPL-3

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