srvyr (version 0.3.10)

survey_quantile: Calculate the quantile and its variation using survey methods

Description

Calculate quantiles from complex survey data. A wrapper around svyquantile. survey_quantile and survey_median should always be called from summarise.

Usage

survey_quantile(
  x,
  quantiles,
  na.rm = FALSE,
  vartype = NULL,
  level = 0.95,
  q_method = "linear",
  f = 1,
  interval_type = c("Wald", "score", "betaWald", "probability", "quantile"),
  ties = c("discrete", "rounded"),
  df = NULL,
  .svy = current_svy(),
  ...
)
survey_median(
  x,
  na.rm = FALSE,
  vartype = c("se", "ci"),
  level = 0.95,
  q_method = "linear",
  f = 1,
  interval_type = c("Wald", "score", "betaWald", "probability", "quantile"),
  ties = c("discrete", "rounded"),
  df = NULL,
  .svy = current_svy(),
  ...
)

Arguments

x

A variable or expression

quantiles

A vector of quantiles to calculate

na.rm

A logical value to indicate whether missing values should be dropped

vartype

NULL to report no variability (default), otherwise one or more of: standard error ("se") confidence interval ("ci") (variance and coefficient of variation not available).

level

A single number indicating the confidence level (only one level allowed)

q_method

See "method" in approxfun

f

See approxfun

interval_type

See svyquantile

ties

See svyquantile

df

A number indicating the degrees of freedom for t-distribution. The default, Inf uses the normal distribution (matches the survey package). Also, has no effect for type = "betaWald".

.svy

A tbl_svy object. When called from inside a summarize function the default automatically sets the survey to the current survey.

...

Ignored

Examples

# NOT RUN {
library(survey)
data(api)

dstrata <- apistrat %>%
  as_survey_design(strata = stype, weights = pw)

dstrata %>%
  summarise(api99 = survey_quantile(api99, c(0.25, 0.5, 0.75)),
            api00 = survey_median(api00, vartype = c("ci")))

dstrata %>%
  group_by(awards) %>%
  summarise(api00 = survey_median(api00))

# }

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