View source: R/sbs_pps_estimate.R
sbs_pps_estimate | R Documentation |
Compute an estimator for SBS PPS sampled data.
sbs_pps_estimate(
population,
n,
y,
sample_matrix,
n_bootstraps = 100,
alpha = 0.05,
n_cores = getOption("n_cores", 1)
)
population |
Population data frame to be sampled with 4 columns.
|
n |
Sample sizes (SBS sample size, PPS sample size). |
y |
Sample response values. |
sample_matrix |
Sample data frame to be sampled with 6 columns.
|
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 |
A summary data frame of the estimator.
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
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