TemporalPivot: Adaptation of the local pivotal method on temporal samples
In SpotSampling: SPatial and Optimally Temporal (SPOT) Sampling

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/TemporalPivot.R

This function considers longitudinal systematic sampling designs of two different units that result from function SystematicDesign. It allows to decide if one of these 2 units is selected at a specific time by putting at least one of the samples selection probabilities to 0. It is based on the local pivotal method.

1	TemporalPivot(design1, design2, d, EPS = 1e-06)

`design1`	a longitudinal systematic sampling design of a first unit. The length of the longitudinal samples is T. It results from function `SystematicDesign`.
`design2`	a longitudinal systematic sampling design of a second unit. The length of the longitudinal samples is T. It results from function `SystematicDesign`.
`d`	a vector of size T that specify for which time t a decision must be taken, with 1 ≤ t ≤ T. `d` is such that the t-th element is equal to 1, and the others to 0.
`EPS`	a tolerance parameter. Default value is 1e-6.

Returns a list including:

p1_new the updated probabilities of the longitudinal systematic sampling design of the first unit.

p2_new the updated probabilities of the longitudinal systematic sampling design of the second unit.

Esther Eustache esther.eustache@unine.ch

Quenouille, M. H. (1949). Approximate Tests of Correlation in time-Series. Royal Statistical Society, Series B Vol. 11, No. 1 (1949), pp. 68-84.

Tille, Y. (2020). Sampling and Estimation from Finite Populations. John Wiley & Sons, 91(4), page 89.

Grafstrom, A., Lundstrom, N. L. P., and Schelin, L. (2012). Spatially balanced sampling through the pivotal method. Biometrics, 68(2):514-520.

SystematicDesign

## Vectors of temporal inclusion probabilities with 3 waves ##
pik1   <- c(0.2,0.3,0.5) # of a first unit
pik2   <- c(0.1,0.4,0.5) # of a second unit
## Find the systematic sampling designs of pik1 and pik2 ##
design1 <- SystematicDesign(pik1, EPS = 1e-6)
design2 <- SystematicDesign(pik2, EPS = 1e-6)
## The time we want to take a decision ##
t    <- 2
d    <- rep(0,3)
d[t] <- 1
## Update probabilities to take a decision at wave t=2 ##
TemporalPivot(design1, design2, d)