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Estimate Means of Numeric Variables in Structural Survey

Source: R/se_mean.R
se_mean.Rd

se_mean() estimates the means of numeric variables along with variance and confidence intervals for FSO's structural survey.

Usage

se_mean(data, variable, ..., strata, weight, alpha = 0.05)

Arguments

data

A data frame or tibble.

variable

Unquoted or quoted name of the numeric variable whose mean is to be estimated. Programmatic usage (e.g., using !!sym()) is supported.

...

Optional grouping variables. Can be passed unquoted (e.g., gender, birth_country) or programmatically using !!!syms(c("gender", "birth_country")).

strata

Unquoted or quoted name of the strata column. Defaults to zone if omitted.

weight

Unquoted or quoted name of the sampling weights column. For programmatic use with a string variable (e.g., wt <- "weights"), use !!sym(wt) in the function call.

alpha

Numeric significance level for confidence intervals. Default is 0.05 (95% CI).

Value

A tibble with columns:

occ

Sample size (number of observations) per group.

<variable>

Estimated mean of the specified numeric variable, named dynamically.

vhat, stand_dev

Estimated variance of the mean (vhat) and its standard deviation (stand_dev, square root of the variance).

ci, ci_l, ci_u

Confidence interval: half-width (ci), lower (ci_l) and upper (ci_u) bounds.

See also

se_prop()

Examples

# Direct column references (unquoted)
se_mean(
  data = nhanes,
  variable = age,
  strata = strata,
  weight = weights,
  gender, birth_country
)
#> # A tibble: 5 × 9
#>   gender birth_country   occ   age  vhat stand_dev    ci  ci_l  ci_u
#>   <fct>  <fct>         <int> <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>
#> 1 Female US             3909  38.0 0.230     0.480 0.940  37.1  39.0
#> 2 Female Other          1168  43.7 0.386     0.621 1.22   42.5  45.0
#> 3 Female Missing           2  30.2 0         0     0      30.2  30.2
#> 4 Male   US             3824  36.2 0.236     0.486 0.953  35.2  37.1
#> 5 Male   Other          1068  41.8 0.361     0.601 1.18   40.7  43.0

# Quoted column names
se_mean(
  data = nhanes,
  variable = "age", 
  strata = "strata", 
  weight = "weights", 
  gender, birth_country
)
#> # A tibble: 5 × 9
#>   gender birth_country   occ   age  vhat stand_dev    ci  ci_l  ci_u
#>   <fct>  <fct>         <int> <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>
#> 1 Female US             3909  38.0 0.230     0.480 0.940  37.1  39.0
#> 2 Female Other          1168  43.7 0.386     0.621 1.22   42.5  45.0
#> 3 Female Missing           2  30.2 0         0     0      30.2  30.2
#> 4 Male   US             3824  36.2 0.236     0.486 0.953  35.2  37.1
#> 5 Male   Other          1068  41.8 0.361     0.601 1.18   40.7  43.0

# Programmatic use with strings
v <- "age"
wt <- "weights"
vars <- c("gender", "birth_country")
se_mean(
  data = nhanes,
  variable = !!rlang::sym(v),
  strata = strata,
  weight = !!rlang::sym(wt),
  !!!rlang::syms(vars)
)
#> # A tibble: 5 × 9
#>   gender birth_country   occ   age  vhat stand_dev    ci  ci_l  ci_u
#>   <fct>  <fct>         <int> <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>
#> 1 Female US             3909  38.0 0.230     0.480 0.940  37.1  39.0
#> 2 Female Other          1168  43.7 0.386     0.621 1.22   42.5  45.0
#> 3 Female Missing           2  30.2 0         0     0      30.2  30.2
#> 4 Male   US             3824  36.2 0.236     0.486 0.953  35.2  37.1
#> 5 Male   Other          1068  41.8 0.361     0.601 1.18   40.7  43.0