A suite of tools to perform joint optimal allocation and stratification of sampling units using simulated annealing. Soft and hard constraints placed on the coefficient of variances (CV) or variances of administrative variables are supported.
saMinCV: uses an exchange algorithm with simulated annealing to form strata from a set of sampling units and simultaneously updates the allocation at each iteration.
saEqualClus: allocates a set of sampling units to strata based on minimizing total sample size based on a set of target variances (for appromately equally sized strata only).
saSampleAlloc: uses simulated annealing to perform optimal allocation.
Lisic, J.J. , J. Sang, Z. Zhu, and S. Zimmer. (2015) Optimal stratification and allocation for the June agricultural survey. Presented at the Federal Committee on Statistical Methodology Research Conference, Washington DC.
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# data set with 100 observations and 4 characteristics split between two strata x <- matrix(rnorm( 100*4 ),ncol=4) # create an initial set of strata assignments label <- c(rep(0,50),rep(1,50)) #target variance targetVar <- c(10000, 20000,30000, 40000) # run equal cluster a <- saEqualClus( x, label, cooling=20, targetVar=targetVar ) # run minCV for a sample size of 20 b <- saMinCV( x, label, targetCV=c( 0.08, 0.10, 1.00, 0.05), sampleSize=20 )
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