# Best stratification of a sampling frame for multipurpose surveys (only with continuous stratification variables) This function runs a set of other functions to optimise the stratification of a sampling frame, only when stratification variables are of the continuous type. It differentiates from 'optimizeStrata' that accepts both continuous and categorical variables 1.3-1 ```r optimizeStrata2( errors, framesamp, framecens = NULL, strcens = FALSE, model = NULL, alldomains = TRUE, dom = NULL, nStrata = c(5), minnumstr = 2, iter = 50, pops = 20, mut_chance = NA, elitism_rate = 0.2, highvalue = 1e+08, suggestions = NULL, realAllocation = TRUE, writeFiles = FALSE, showPlot = TRUE, parallel = TRUE, cores = NA ) ``` ## Arguments - `errors`: This is the (mandatory) dataframe containing the precision levels expressed in terms of maximum expected value of the Coefficients of Variation related to target variables of the survey. - `framesamp`: This is the (mandatory) dataframe containing the information related to the sampling frame. - `framecens`: This the (optional) dataframe containing the units to be selected in any case. It has same structure than "frame" dataframe. - `strcens`: Flag (TRUE/FALSE) to indicate if takeall strata do exist or not. Default is FALSE. - `model`: In case the Y variables are not directly observed, but are estimated by means of other explicative variables, in order to compute the anticipated variance, information on models are given by a dataframe "model" with as many rows as the target variables. Each row contains the indication if the model is linear o loglinear, and the values of the model parameters beta, sig2, gamma (> 1 in case of heteroscedasticity). Default is NULL. - `alldomains`: Flag (TRUE/FALSE) to indicate if the optimization must be carried out on all domains (default is TRUE). If it is set to FALSE, then a value must be given to parameter 'dom'. - `dom`: Indicates the domain on which the optimization must be carried. It is an integer value that has to be internal to the interval (1 <--> number of domains). If 'alldomains' is set to TRUE, it is ignored. - `nStrata`: Indicates the number of strata for each variable. - `minnumstr`: Indicates the minimum number of units that must be allocated in each stratum. Default is 2. - `iter`: Indicated the maximum number of iterations (= generations) of the genetic algorithm. Default is 50. - `pops`: The dimension of each generations in terms of individuals. Default is 20. - `mut_chance`: Mutation chance: for each new individual, the probability to change each single chromosome, i.e. one bit of the solution vector. High values of this parameter allow a deeper exploration of the solution space, but a slower convergence, while low values permit a faster convergence, but the final solution can be distant from the optimal one. Default is NA, in correspondence of which it is computed as 1/(vars+1) where vars is the length of elements in the solution. - `elitism_rate`: This parameter indicates the rate of better solutions that must be preserved from one generation to another. Default is 0.2 (20 - `highvalue`: Parameter for genetic algorithm. In should not be changed - `suggestions`: Optional parameter for genetic algorithm that indicates a suggested solution to be introduced in the initial population. The most convenient is the one found by the function "KmeanSolution". Default is NULL. - `realAllocation`: If FALSE, the allocation is based on INTEGER values; if TRUE, the allocation is based on REAL values. Default is TRUE. - `writeFiles`: Indicates if the various dataframes and plots produced during the execution have to be written in the working directory. Default is FALSE. - `showPlot`: Indicates if the plot showing the trend in the value of the objective function has to be shown or not. In parallel = TRUE, this defaults to FALSE Default is TRUE. - `parallel`: Should the analysis be run in parallel. Default is TRUE. - `cores`: If the analysis is run in parallel, how many cores should be used. If not specified n-1 of total available cores are used OR if number of domains < (n-1) cores, then number of cores equal to number of domains are used. ## Returns A list containing (1) the vector of the solution, (2) the optimal aggregated strata, (3) the total sampling frame with the label of aggregated strata ## Author(s) Giulio Barcaroli ## Examples ```r ## Not run: library(SamplingStrata) data(swissmunicipalities) swissmunicipalities$id <- c(1:nrow(swissmunicipalities)) frame <- buildFrameDF(df = swissmunicipalities, id = "id", domainvalue = "REG", X = c("POPTOT","HApoly"), Y = c("Surfacesbois", "Airind")) ndom <- length(unique(frame$domainvalue)) cv <- as.data.frame(list(DOM = rep("DOM1",ndom), CV1 = rep(0.1,ndom), CV2 = rep(0.1,ndom), domainvalue = c(1:ndom))) ################################ # Example of "continuous" method ################################ kmean <- KmeansSolution2(frame = frame, errors = cv, maxclusters = 10) nstrat <- tapply(kmean$suggestions, kmean$domainvalue, FUN=function(x) length(unique(x))) sugg <- prepareSuggestion(kmean = kmean, frame = frame, nstrat = nstrat) solution <- optimizeStrata2(framesamp = frame, errors = cv, nStrata = nstrat, iter = 50, pops = 10, suggestions = sugg) framenew <- solution$framenew outstrata <- solution$aggr_strata s <- selectSample(framenew,outstrata) ## End(Not run) ```

optimizeStrata2 function