Weakly Associated Vectors (WAVE) Sampling
Spatial data are generally auto-correlated, meaning that if two units
selected are close to each other, then it is likely that they share the
same properties. For this reason, when sampling in the population it is
often needed that the sample is well spread over space. A new method to
draw a sample from a population with spatial coordinates is proposed.
This method is called wave
(weakly associated vectors) sampling. It
uses the less correlated vector to a spatial weights matrix to update
the inclusion probabilities vector into a sample. For more details see
Raphaël Jauslin and Yves Tillé (2020)
https://doi.org/10.1007/s13253-020-00407-1.
Installation
CRAN version
install.packages("WaveSampling")
Latest version
You can install the latest version of the package WaveSampling
with
the following command:
# install.packages("devtools")
devtools::install_github("Rjauslin/WaveSampling")
Simple example
This basic example shows you how to solve a common problem. Spatial
coordinates from the function runif()
are firstly generated.
library(WaveSampling)
#> Le chargement a nécessité le package : Matrix
N <- 144
n <- 48
X <- cbind(runif(N),runif(N))
head(X,10)
#> [,1] [,2]
#> [1,] 0.35000373 0.2557976
#> [2,] 0.87553309 0.6370745
#> [3,] 0.09019367 0.9000345
#> [4,] 0.97906235 0.3576902
#> [5,] 0.32768335 0.1444912
#> [6,] 0.41488141 0.5468550
#> [7,] 0.67789730 0.4799551
#> [8,] 0.21604234 0.8712608
#> [9,] 0.38741250 0.6283276
#> [10,] 0.78754375 0.6948966
Inclusion probabilities pik
is set up all equal with the function
rep()
.
pik <- rep(n/N,times = N)
It only remains to use the function wave()
,
s <- wave(X,pik)
We can also generate a plot to observe the result.
library(ggplot2)
ggplot() +
geom_point(data = data.frame(x = X[,1],y = X[,2]),
aes(x = x,y = y),
shape = 1,
alpha = 0.2)+
geom_point(data = data.frame(x = X[s == 1,1],y = X[s == 1,2]),
aes(x,y),
shape = 16,
colour = "black")+
theme_void()