Compute an estimator for SBS PPS sampled data.

Usage

sbs_pps_estimate(
  population,
  n,
  y,
  sample_matrix,
  n_bootstraps = 100,
  alpha = 0.05,
  n_cores = getOption("n_cores", 1)
)

Arguments

population

Population data frame to be sampled with 4 columns.

Halton numbers
X1-coordinate of population unit
X2-coordinate of population unit
Size measurements of population units

n

Sample sizes (SBS sample size, PPS sample size).

y

Sample response values.

sample_matrix

Sample data frame to be sampled with 6 columns.

Halton numbers
X1-coordinate of population unit
X2-coordinate of population unit
Size measurement of population unit
Weight
Inclusion probability

n_bootstraps

Number of bootstrap samples.

alpha

The significance level.

n_cores

The number of cores to be used for computational tasks (specify 0 for max). This can also be set by calling options, e.g., options(n_cores = 2).

Value

A summary data frame of the estimator.

Examples

set.seed(112)

# SBS sample size, PPS sample size
sample_sizes <- c(5, 5)

n_population <- 233
k <- 0:(n_population - 1)
x1 <- sample(1:13, n_population, replace = TRUE) / 13
x2 <- sample(1:8, n_population, replace = TRUE) / 8
y <- (x1 + x2) * runif(n = n_population, min = 1, max = 2) + 1
measured_sizes <- y * runif(n = n_population, min = 0, max = 4)

population <- matrix(cbind(k, x1, x2, measured_sizes), ncol = 4)
sample_result <- sbs_pps_sample(population, sample_sizes)

# estimate the population mean and construct a confidence interval
df_sample <- sample_result$sample
sample_id <- df_sample[, 1]
y_sample <- y[sample_id]

sbs_pps_estimates <- sbs_pps_estimate(
  population, sample_sizes, y_sample, df_sample,
  n_bootstrap = 100, alpha = 0.05
)
print(sbs_pps_estimates)
#>   n1 n2 Estimate  St.error 95% Confidence intervals
#> 1  5  5    2.849 0.1760682              2.451,3.247
#>   n1 n2 Estimate  St.error 95% Confidence intervals
#> 1  5  5    2.849 0.1760682              2.451,3.247