# as_survey_twophase

##### 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`

##### 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*