Description Usage Arguments Examples
View source: R/survey_statistics.r
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
.
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(),
...)

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

prop_method 
Type of proportion method to use if proportion is 
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 tdistribution. The default (NULL) uses 
.svy 
A 
... 
Ignored 
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

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