View source: R/survey_statistics.r
survey_total | R Documentation |
Calculate totals from complex survey data. A wrapper
around svytotal
. survey_total
should always be
called from summarise
.
survey_total( x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), level = 0.95, deff = FALSE, df = NULL, ... )
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 |
A single number or vector of numbers indicating the confidence level |
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 |
... |
Ignored |
library(survey) data(api) dstrata <- apistrat %>% as_survey_design(strata = stype, weights = pw) dstrata %>% summarise(enroll_tot = survey_total(enroll), tot_meals = survey_total(enroll * meals / 100, vartype = c("ci", "cv"))) dstrata %>% group_by(awards) %>% summarise(api00 = survey_total(enroll)) # Leave x empty to calculate the total in each group dstrata %>% group_by(awards) %>% summarise(pct = survey_total()) # level takes a vector for multiple levels of confidence intervals dstrata %>% summarise(enroll = survey_total(enroll, 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 exactly match. To # replicate survey's behavior, use df = Inf dstrata %>% summarise(srvyr_default = survey_total(api99, vartype = "ci"), survey_defualt = survey_total(api99, vartype = "ci", df = Inf)) comparison <- survey::svytotal(~api99, dstrata) confint(comparison) # survey's default confint(comparison, df = survey::degf(dstrata)) # srvyr's default
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