rss_jps_estimate: Estimate means from RSS or JPS sample.

In biometryhub/RankedSetSampling: Easing the Application of Ranked Set Sampling in Practice

Estimate means from RSS or JPS sample.

Description

Estimate means from RSS or JPS sample.

Usage

rss_jps_estimate(
  data,
  set_size,
  method,
  confidence = 0.95,
  replace = TRUE,
  model_based = FALSE,
  pop_size = NULL
)

Arguments

data	A data frame of JPS or RSS rankings.
set_size	The set size of the ranks.
method	A method used to sample: "JPS": Judgment-post stratified sampling "RSS": Ranked set sampling
confidence	The confidence level to use.
replace	Logical (default TRUE). Sample with replacement?
model_based	An inference mode: FALSE: design based inference TRUE: model based inference using super population model
pop_size	The population size. Must be provided if sampling without replacement, or model_based is TRUE.

Value

A data.frame with the point estimates provided by different types of estimators along with standard error and confidence intervals.

Examples

# JPS estimator
set.seed(112)
population_size <- 600
# the number of samples to be ranked in each set
H <- 3

with_replacement <- FALSE
sigma <- 4
mu <- 10
n_rankers <- 3
# sample size
n <- 30

rhos <- rep(0.75, n_rankers)
taus <- sigma * sqrt(1 / rhos^2 - 1)
population <- qnorm((1:population_size) / (population_size + 1), mu, sigma)

data <- InPlotSampling::jps_sample(population, n, H, taus, n_rankers, with_replacement)
data <- data[order(data[, 2]), ]

rss_jps_estimate(
  data,
  set_size = H,
  method = "JPS",
  confidence = 0.80,
  replace = with_replacement,
  model_based = FALSE,
  pop_size = population_size
)
#>          Estimator Estimate Standard Error 80% Confidence intervals
#> 1       UnWeighted    9.570          0.526               8.88,10.26
#> 2      Sd.Weighted    9.595          0.569             8.849,10.341
#> 3 Aggregate Weight    9.542          0.500             8.887,10.198
#> 4     JPS Estimate    9.502          0.650             8.651,10.354
#> 5     SRS estimate    9.793          0.783             8.766,10.821
#> 6          Minimum    9.542          0.500             8.887,10.198

# RSS estimator
set.seed(112)
population_size <- 600
# the number of samples to be ranked in each set
H <- 3

with_replacement <- FALSE
sigma <- 4
mu <- 10
n_rankers <- 3
# sample size
n <- 30

population <- qnorm((1:population_size) / (population_size + 1), mu, sigma)
rho <- 0.75
tau <- sigma * sqrt(1 / rho^2 - 1)
x <- population + tau * rnorm(population_size, 0, 1)

population <- cbind(population, x)
data <- InPlotSampling::rss_sample(population, n, H, n_rankers, with_replacement)
data <- data[order(data[, 2]), ]

rss_estimates <- rss_jps_estimate(
  data,
  set_size = H,
  method = "RSS",
  confidence = 0.80,
  replace = with_replacement,
  model_based = FALSE,
  pop_size = population_size
)

print(rss_estimates)
#>             Estimator point.est St.error 80% Confidence Interval
#> 1               RSS-1     9.153    0.766            8.148,10.158
#> 2  Aggregate Weighted     9.064    0.652             8.209,9.919

biometryhub/RankedSetSampling documentation built on Feb. 16, 2025, 11:31 p.m.