Description Usage Arguments Details Value Author(s) References See Also Examples
Allocation of the overall sample size n to the strata for the following purpose:
A weighted sum of variances (or relative variances) of the estimates of totals for the m survey variables is minimized.
1 | BSSM_FD(Nh,Sh2j,Yj,Cust,nmin,ch,w,certain)
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Nh |
Vector with total number of population units in each stratum (h=1,...,H) |
Sh2j |
Matrix (or vector) mxH (m = number of variables and H =number of strata) with Population variance for each variable of the hth stratum |
Yj |
Vector with population total Yj for the jth survey variable |
Cust |
Corresponds to the overall variable cost budget for the survey C |
nmin |
Smallest possible sample size in any stratum |
ch |
Vector with the unit level survey costs for sampling from stratum h |
w |
Vector with Variable-specific weights, set a priori to represent the relative importance of the survey variables |
certain |
if (nH=NH) => certain=TRUE else certain=FALSE |
Function that uses an integer programming formulation
n |
Sample size |
nh |
Sample of size by stratum |
cvs |
Coefficients of variation for the estimators of totals of the survey variables considered |
time_cpu |
Time consumed by the algorithm (seconds) |
Jose Brito (jambrito@gmail.com), Pedro Silva, Gustavo Semaan and Nelson Maculan
Brito, J.A.M, Silva, P.L.N.,Semaan, G.Srogramming Formulations Applied to Optimal Allocation in Stratified Sampling. Survey Methodology, 41, No.2, pp.427-442.
BSSM_FC
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | #Example1
#Unit level survey costs for sampling from the strata are assumed to be the same.
#Number of survey variables (m=2) and seven strata (H=7)
#ch=1 ==> Cust = n
Nh<-c(49,78,20,39,73,82,89)
Yj<-c(542350,56089251)
Sh2j<-rbind(c(4436978,5581445,33454902,5763294,8689167,3716130,13938505),
c(11034299660,40919330279,33519355946,18228286901,74247764986,49062224184,5783096806))
n<-200 #sample size
result<-BSSM_FD(Nh,Sh2j,Yj,Cust=n)
#Example2
#Unit level survey costs for sampling from the strata are assumed to be the same.
#ch=1 ==> Cust = n
#nmin>2
Nh<-c(49,78,20,39,73,82,89)
Yj<-c(542350,56089251)
Sh2j<-rbind(c(4436978,5581445,33454902,5763294,8689167,3716130,13938505),
c(11034299660,40919330279,33519355946,18228286901,74247764986,49062224184,5783096806))
nmin<-20
n<-200
result<-BSSM_FD(Nh,Sh2j,Yj,nmin,Cust=n)
#Example3
#certain=TRUE
Nh<-c(49,78,20,39,73,82,89)
Yj<-c(542350,56089251)
Sh2j<-rbind(c(4436978,5581445,33454902,5763294,8689167,3716130,13938505),
c(11034299660,40919330279,33519355946,18228286901,74247764986,49062224184,5783096806))
n<-200
result<-BSSM_FD(Nh,Sh2j,Yj,Cust=n,certain=TRUE)
#Example4
#Relative importance of the survey variables is different
w<-c(0.3,0.7)
Nh<-c(49,78,20,39,73,82,89)
Yj<-c(542350,56089251)
Sh2j<-rbind(c(4436978,5581445,33454902,5763294,8689167,3716130,13938505),
c(11034299660,40919330279,33519355946,18228286901,74247764986,49062224184,5783096806))
n<-200
result<-BSSM_FD(Nh,Sh2j,Yj,Cust=n,w=w)
#Example5
#Number of survey variables m=1
Nh<-c(49,78,20,39,73,82,89)
Yj<-542350
Sh2j<-c(4436978,5581445,33454902,5763294,8689167,3716130,13938505)
n<-100
result<-BSSM_FD(Nh,Sh2j,Yj,Cust=n)
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