min_count {surveyplanning} | R Documentation |
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.
min_count(n, pop, RMoE, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)
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. |
The estimate of minimal count of respondents for the given relative margin of error.
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
## End(Not run)