# survey_mean: Calculate the mean and its variation using survey methods In srvyr: 'dplyr'-Like Syntax for Summary Statistics of Survey Data

## Description

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

 ```1 2 3 4``` ```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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37``` ```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 ```

srvyr documentation built on May 22, 2018, 5:06 p.m.