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Compute Design-Based Proportion and Confidence Interval

lpr_ci.Rd

Computes a weighted proportion (mean of a binary outcome) and its confidence interval using complex survey design features. When stratification and PSU variables are supplied, the function uses Taylor linearized variance estimation via the survey package.

Usage

lpr_ci(
  data,
  outcome,
  weight = "weight1500",
  strata = NULL,
  psu = NULL,
  conf.level = 0.95,
  na.rm = TRUE
)

Arguments

data

A data frame containing the outcome and survey design variables.

outcome

Character string. Name of a binary variable coded 0/1.

weight

Character string. Name of the sampling weight variable. Default is `"weight1500"`.

strata

Character string. Name of the stratification variable. Default is `NULL`. If provided together with `psu`, a complex survey design is used.

psu

Character string. Name of the primary sampling unit (cluster) variable. Default is `NULL`.

conf.level

Numeric. Confidence level for the interval. Default is `0.95`.

na.rm

Logical. If `TRUE`, rows with missing values in the required variables are removed prior to estimation.

Value

A data frame with:

prop

Estimated proportion (mean of binary outcome).

lb

Lower bound of the confidence interval.

ub

Upper bound of the confidence interval.

se

Standard error of the estimate.

Details

If both `strata` and `psu` are provided, a full complex survey design is declared. If they are `NULL`, the function computes a weighted estimate assuming simple random sampling (SRS) with weights.

Lonely PSUs are handled using `survey.lonely.psu = "adjust"`.

Variance estimation is performed using Taylor linearization as implemented in svymean. When both `strata` and `psu` are supplied, clustering and stratification are incorporated in the variance estimator.

If `strata` and `psu` are not provided, the function assumes a weighted simple random sample and estimates variance accordingly.

Examples

if (FALSE) { # \dontrun{
# Design-based estimate
data(cm23)
lpr_ci(data = cm23,
         outcome = "b13",
         weight = "weight1500",
         strata = "strata",
         psu = "upm")

# Weighted SRS estimate
data(bra23)
lpr_ci(data = bra23,
         outcome = "b13",
         weight = "wt")
} # }