srvyr (version 0.2.1)

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 = c("none", "se",
  "ci"), level = 0.95, q_method = "linear", f = 1,
  interval_type = c("Wald", "score", "betaWald", "probability", "quantile"),
  ties = c("discrete", "rounded"), df = Inf, ...)
survey_median(x, na.rm = FALSE, vartype = c("none", "se", "ci"),
  level = 0.95, q_method = "linear", f = 1, interval_type = c("Wald",
  "score", "betaWald", "probability", "quantile"), ties = c("discrete",
  "rounded"), df = Inf, ...)

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

Report variability as one or more of: standard error ("se", default), 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".

...

Ignored

Examples

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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