sbs_pps_sample: Generate probability proportional to size (PPS) and spatially...

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

Generate probability proportional to size (PPS) and spatially balanced sampling on the population provided.

Description

Generate probability proportional to size (PPS) and spatially balanced sampling on the population provided.

Usage

sbs_pps_sample(population, n, 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).
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 named list of:

• heatmap: heat map of the sample

• sample: SBS PPS sample of the population

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)
print(sample_result$sample)
#>    sbs_pps_indices        x1    x2       size      weight inclusion_probability
#> 1               87 0.4615385 0.625  0.4665423 0.000000000            0.02319163
#> 2               88 0.1538462 0.625  1.7389902 0.000000000            0.02790409
#> 3               89 0.8461538 0.625  7.0815547 0.000000000            0.04749104
#> 4               90 0.6923077 0.750  9.5428032 0.000000000            0.05640733
#> 5               91 0.2307692 0.750  5.1375136 0.000000000            0.04039996
#> 6              173 0.1538462 0.500  6.6400168 0.005024130            0.04588620
#> 7               26 0.6153846 0.500  4.3146186 0.003264631            0.03738898
#> 8              232 0.8461538 0.750 12.0057856 0.009084108            0.06526583
#> 9              171 0.6153846 0.750  6.9029083 0.005223046            0.04684225
#> 10              29 0.8461538 0.375  4.6324720 0.003505133            0.03855377

biometryhub/RSS_package documentation built on Feb. 18, 2025, 11:56 p.m.