STRATVNS {stratvns} | R Documentation |
Vns Algorithm
Description
This function aims at constructing optimal strata with an optimization algorithm based on a global optimisation technique called Variable neighborhood search (VNS). The optimization algorithm is applied to solve the one dimensional case, which reduces the stratification problem to just determining strata boundaries. Assuming that the number L of strata and the coefficient of variation are fixed, it is possible to produce the strata boundaries by taking into consideration an objective function associated with the sample size. This function determines strata boundaries so that the elements in each stratum are more homogeneous among themselves and produce minimum sample size applying an integer formulation proposed by Brito et al (2015).
Usage
STRATVNS(
X,
L = 3,
cvt = 0.1,
nhmin = 2,
maxstart = 3,
imax = 3,
kmax = 3,
s = 30,
sl = 50,
tmax = 15,
nsols = 20,
cputime = 3600,
nIterWithNoImpMax = 5,
parallelize = TRUE
)
Arguments
X |
Stratification Variable |
L |
Number of strata |
cvt |
Target cv |
nhmin |
Mininum sample size by stratum |
maxstart |
Number of iterations in multstart |
imax |
Maximum Number Iterations - VNS |
kmax |
Maximum Neighborhoods = number of cut points selected to apply shaking and local search |
s |
Range of shaking procedure |
sl |
Range of RVNS procedure |
tmax |
Maximum number cut points in neighborhoods |
nsols |
Number of initial solutions generated |
cputime |
Maximum cpu time in seconds |
nIterWithNoImpMax |
Maximum number of iterations without improvement in VNS |
parallelize |
TRUE = Performs multiple vns calls in parallel |
Details
STRATVNS
Value
bk |
Cut points |
n |
Minimum sample size |
nh |
Sample size by strata |
cv |
coefficient of variation |
Nh |
Strata sizes |
Vh |
Strata variances |
cputime |
Runtime in seconds |
Author(s)
Leonardo de Lima, Jose Brito, Pedro Gonzalez and Breno Oliveira
References
1. Hansen, P., Mladenovi´c, N., 2001. Variable neighborhood search: Principles and applications. European Journal of Operational Research 130, 3, 449 – 467.
2. Brito, J.A.M., Silva, P.L.N., Semaan, G.S., Maculan, N., 2015. Integer programming formulations applied to optimal allocation in stratified sampling. Survey Methodology 41, 2, 427–442.
Examples
## Not run:
Example1:
s<-STRATVNS(U1,L=4,cvt=0.05,nhmin=3)
Example2:
s<-STRATVNS(U15,L=3)
#'Example3:
s<-STRATVNS(U21,L=5)
Example4:
s<-STRATVNS(U1,L=3,nhmin=4)
## End(Not run)