srvyr has updated it's standard evaluation semantics to match dplyr 0.7, so these underscore functions are no longer required (but are still supported for backward compatibility reasons). See se-deprecated or the dplyr vignette on programming (vignette("programming", package = "dplyr")) for more details.

as_survey_(.data, ...)

as_survey_design_(
.data,
ids = NULL,
probs = NULL,
strata = NULL,
variables = NULL,
fpc = NULL,
nest = FALSE,
check_strata = !nest,
weights = NULL,
pps = FALSE,
variance = c("HT", "YG")
)

as_survey_rep_(
.data,
variables = NULL,
repweights = NULL,
weights = NULL,
type = c("BRR", "Fay", "JK1", "JKn", "bootstrap", "other"),
combined_weights = TRUE,
rho = NULL,
bootstrap_average = NULL,
scale = NULL,
rscales = NULL,
fpc = NULL,
fpctype = c("fraction", "correction"),
mse = getOption("survey.replicates.mse")
)

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

cascade_(.data, ..., .dots, .fill = NA)

## Arguments

.data a data.frame or an object from the survey package other arguments, see other functions for details Variables specifying cluster ids from largest level to smallest level (leaving the argument empty, NULL, 1, or 0 indicate no clusters). Variables specifying cluster sampling probabilities. Variables specifying strata. Variables specifying variables to be included in survey. Defaults to all variables in .data Variables specifying a finite population correct, see svydesign for more details. If TRUE, relabel cluster ids to enforce nesting within strata. If TRUE, check that clusters are nested in strata. Variables specifying weights (inverse of probability). "brewer" to use Brewer's approximation for PPS sampling without replacement. "overton" to use Overton's approximation. An object of class HR to use the Hartley-Rao approximation. An object of class ppsmat to use the Horvitz-Thompson estimator. For pps without replacement, use variance="YG" for the Yates-Grundy estimator instead of the Horvitz-Thompson estimator Variables specifying the replication weight variables Type of replication weights TRUE if the repweights already include the sampling weights. This is usually the case. Shrinkage factor for weights in Fay's method For type = "bootstrap", if the bootstrap weights have been averaged, gives the number of iterations averaged over. Scaling constant for variance, see svrepdesign for more information. Scaling constant for variance, see svrepdesign for more information. Finite population correction information if TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates list of two sets of variable names for sampling unit identifiers bare name of a variable which specifies which observations are selected in phase 2 "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. Used to work around non-standard evaluation. See vignette("nse", package = "dplyr") for details. Value to fill in for group summaries