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 (2019) https://arxiv.org/abs/1910.13152.

`install.packages("WaveSampling")`

You can install the latest version of the package
`WaveSampling`

with the following command:

```
# install.packages("devtools")
::install_github("Rjauslin/WaveSampling") devtools
```

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
<- 144
N <- 48
n <- cbind(runif(N),runif(N))
X head(X,10)
#> [,1] [,2]
#> [1,] 0.4357956 0.3757532
#> [2,] 0.3778253 0.9184581
#> [3,] 0.4171640 0.7267468
#> [4,] 0.4993113 0.4654331
#> [5,] 0.8498832 0.8845685
#> [6,] 0.5836470 0.6395404
#> [7,] 0.6874862 0.3310646
#> [8,] 0.1087650 0.9061414
#> [9,] 0.3874497 0.4504327
#> [10,] 0.4484322 0.6564196
```

Inclusion probabilities `pik`

is set up all equal with the
function `rep()`

.

`<- rep(n/N,times = N) pik `

It only remains to use the function `wave()`

,

`<- wave(X,pik) s `

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()
```

As explained on the website of Microsoft R open https://mran.microsoft.com/open, R is designed to use only one thread but could be linked with a multi-threaded version of BLAS/LAPACK. The package is implemented with the package RcppArmadillo http://arma.sourceforge.net/ that provide an integration of various matrix decompositions with LAPACK library. Intel MKL that is used by the Microsoft R open use a multi-threaded version of BLAS/LAPACK. Hence the package could gain time from the Microsoft R open. (Not tested, but linking R with OpenBLAS should also work).

```
N <- 500
n <- 100
X <- as.matrix(cbind(runif(N),runif(N)))
pik <- rep(n/N,N)
system.time(s <- wave(X,pik,tore = T,shift =T,comment = T))
```

```
user system elapsed
42.96 0.67 43.74
```

```
user system elapsed
53.63 0.87 9.34
```

This test was done on PC with a CPU Intel^{®} Core™ i5-8500
with 6 cores.