surveyplanning (version 4.0)

min_count: Minimal count of respondents for the given relative margin of error

Description

The function computes minimal proportion for the given relative margin of error. The calculation takes into sample size, population size, margin of error, expected response rate and design effect.

Usage

min_count(n, pop, RMoE, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)

Arguments

n

The expected sample size.

pop

Population size.

RMoE

The expected relative margin of error.

confidence

Optional positive value for confidence interval. This variable by default is 0.95.

R

The expected response rate (optional). If not defined, it is assumed to be 1 (full-response).

deff_sam

The expected design effect of sample design for the estimates (optional). If not defined, it is assumed to be 1.

deff_est

The estimated design effect of estimator for the estimates (optional). If not defined, it is assumed to be 1.

Value

The estimate of minimal count of respondents for the given relative margin of error.

See Also

expvar, optsize, MoE_P

Examples

# NOT RUN {
min_count(n = 15e3, pop = 2e6, RMoE = 0.1)

# }
# NOT RUN {
library("data.table")
min_count(n = c(10e3, 15e3, 20e3), pop = 2e6, 0.1)

n <- seq(10e3, 30e3, length.out = 11)
# n <- sort(c(n, 22691))
n

RMoE <- seq(.02, .2, length.out = 10)
RMoE

dt <- data.table(n = rep(n, each = length(RMoE)), RMoE = RMoE)
dt[, Y := min_count(n = n, pop = 2.1e6, RMoE = RMoE, R = 1) / 1e3]
dt
# }
# NOT RUN {
# }