Aliases: survey_mean survey_prop
Keywords:
### ** Examples data(api, package = "survey") dstrata <- apistrat %>% as_survey_design(strata = stype, weights = pw) dstrata %>% summarise(api99_mn = survey_mean(api99), api_diff = survey_mean(api00 - api99, vartype = c("ci", "cv")))
# A tibble: 1 × 6
api99_mn api99_mn_se api_diff api_diff_low api_diff_upp api_diff_cv
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 629. 10.1 32.9 28.8 37.0 0.0632
dstrata %>% group_by(awards) %>% summarise(api00 = survey_mean(api00))
# A tibble: 2 × 3 awards api00 api00_se <fct> <dbl> <dbl> 1 No 634. 15.6 2 Yes 678. 12.0
# Use `survey_prop` calculate the proportion in each group dstrata %>% group_by(awards) %>% summarise(pct = survey_prop())
# A tibble: 2 × 3 awards pct pct_se <fct> <dbl> <dbl> 1 No 0.361 0.0349 2 Yes 0.639 0.0349
# Or you can also leave out `x` in `survey_mean`, so this is equivalent dstrata %>% group_by(awards) %>% summarise(pct = survey_mean())
# A tibble: 2 × 3 awards pct pct_se <fct> <dbl> <dbl> 1 No 0.361 0.0349 2 Yes 0.639 0.0349
# When there's more than one group, the last group is "peeled" off and proportions are # calculated within that group, each adding up to 100%. # So in this example, the sum of prop is 200% (100% for awards=="Yes" & # 100% for awards=="No") dstrata %>% group_by(stype, awards) %>% summarize(prop = survey_prop())
# A tibble: 6 × 4 # Groups: stype [3] stype awards prop prop_se <fct> <fct> <dbl> <dbl> 1 E No 0.27 0.0446 2 E Yes 0.73 0.0446 3 H No 0.68 0.0666 4 H Yes 0.32 0.0666 5 M No 0.52 0.0714 6 M Yes 0.48 0.0714
# The `interact` function can help you calculate the proportion over # the interaction of two or more variables # So in this example, the sum of prop is 100% dstrata %>% group_by(interact(stype, awards)) %>% summarize(prop = survey_prop())
# A tibble: 6 × 4 stype awards prop prop_se <fct> <fct> <dbl> <dbl> 1 E No 0.193 0.0318 2 E Yes 0.521 0.0318 3 H No 0.0829 0.00812 4 H Yes 0.0390 0.00812 5 M No 0.0855 0.0117 6 M Yes 0.0789 0.0117
# Setting proportion = TRUE uses a different method for calculating confidence intervals dstrata %>% summarise(high_api = survey_mean(api00 > 875, proportion = TRUE, vartype = "ci"))
# A tibble: 1 × 3
high_api high_api_low high_api_upp
<dbl> <dbl> <dbl>
1 0.0318 0.0129 0.0765
# level takes a vector for multiple levels of confidence intervals dstrata %>% summarise(api99 = survey_mean(api99, vartype = "ci", level = c(0.95, 0.65)))
# A tibble: 1 × 5 api99 api99_low95 api99_upp95 api99_low65 api99_upp65 <dbl> <dbl> <dbl> <dbl> <dbl> 1 629. 609. 649. 620. 639.
# 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))
# A tibble: 1 × 6
srvyr_default srvyr_default_low srvyr_default_upp survey_defualt
<dbl> <dbl> <dbl> <dbl>
1 629. 609. 649. 629.
# … with 2 more variables: survey_defualt_low <dbl>, survey_defualt_upp <dbl>
comparison <- survey::svymean(~api99, dstrata) confint(comparison) # survey's default
2.5 % 97.5 % api99 609.6051 649.1846
confint(comparison, df = survey::degf(dstrata)) # srvyr's default
2.5 % 97.5 % api99 609.4828 649.3069