survey_mean

0th

Percentile

Calculate the mean and its variation using survey methods

Calculate means and proportions from complex survey data. A wrapper around svymean, or if proportion = TRUE, svyciprop. survey_mean should always be called from summarise.

Usage
survey_mean(x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"),
  level = 0.95, proportion = FALSE, prop_method = c("logit",
  "likelihood", "asin", "beta", "mean"), deff = FALSE, df = NULL,
  .svy = current_svy(), ...)
Arguments
x

A variable or expression, or empty

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 ("var") or coefficient of variation ("cv").

level

(For vartype = "ci" only) A single number or vector of numbers indicating the confidence level

proportion

Use methods to calculate the proportion that may have more accurate confidence intervals near 0 and 1. Based on svyciprop.

prop_method

Type of proportion method to use if proportion is TRUE. See svyciprop for details.

deff

A logical value to indicate whether the design effect should be returned.

df

(For vartype = "ci" only) A numeric value indicating the degrees of freedom for t-distribution. The default (NULL) uses degf, but Inf is the usual survey package's default (except in svyciprop.

.svy

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

...

Ignored

Aliases
  • survey_mean
Examples
# NOT RUN {
library(survey)
data(api)

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

dstrata %>%
  summarise(api99 = survey_mean(api99),
            api_diff = survey_mean(api00 - api99, vartype = c("ci", "cv")))

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

# Leave x empty to calculate the proportion in each group
dstrata %>%
  group_by(awards) %>%
  summarise(pct = survey_mean())

# Setting proportion = TRUE uses a different method for calculating confidence intervals
dstrata %>%
  summarise(high_api = survey_mean(api00 > 875, proportion = TRUE, vartype = "ci"))

# level takes a vector for multiple levels of confidence intervals
dstrata %>%
  summarise(api99 = survey_mean(api99, vartype = "ci", level = c(0.95, 0.65)))

# Note that the default degrees of freedom in srvyr is different from
# survey, so your confidence intervals might not be exact matches. To
# Replicate survey's behavior, use df = Inf
dstrata %>%
  summarise(srvyr_default = survey_mean(api99, vartype = "ci"),
            survey_defualt = survey_mean(api99, vartype = "ci", df = Inf))

comparison <- survey::svymean(~api99, dstrata)
confint(comparison) # survey's default
confint(comparison, df = survey::degf(dstrata)) # srvyr's default

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

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