| optimStrata {SamplingStrata} | R Documentation |
Optimization of the stratification of a sampling frame given a sample survey
Description
Wrapper function to call the different optimization functions: (i) optimizeStrata (method = "atomic"); (ii) optimizeStrata2 (method = "continuous"); (iii) optimizeStrataSpatial (method = "spatial"). For continuity reasons, these functions are still available to be used standalone.
Usage
optimStrata(method=c("atomic","continuous","spatial"),
# common parameters
framesamp,
framecens=NULL,
model=NULL,
nStrata=NA,
errors,
alldomains=TRUE,
dom=NULL,
strcens=FALSE,
minnumstr=2,
iter=50,
pops=20,
mut_chance=NA,
elitism_rate=0.2,
suggestions=NULL,
writeFiles=FALSE,
showPlot=TRUE,
parallel=TRUE,
cores=NA,
# parameters only for optimizeStrataSpatial
fitting=NA,
range=NA,
kappa=NA)
Arguments
method |
This parameter allows to choose the method to be applied in the optimization step: (i) optimizeStrata (method = "atomic"); (ii) optimizeStrata (method = "continuous"); (iii) optimizeStrata (method = "spatial") |
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 dataframe containing the information related to the sampling frame. |
framecens |
This the dataframe containing the units to be selected in any case. It has same structure than "framesamp" dataframe. |
nStrata |
Indicates the number of strata to be obtained in the final solution. |
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. |
strcens |
Flag (TRUE/FALSE) to indicate if takeall strata do exist or not. Default is FALSE. |
minnumstr |
Indicates the minimum number of units that must be allocated in each stratum. Default is 2. |
iter |
Indicates 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 |
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. |
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. |
fitting |
Fitting of the model(s) (in terms of R squared). It is a vector with as many elements as the number of target variables Y. |
range |
Maximum range for spatial autocorrelation. It is a vector with as many elements as the number of target variables Y. |
kappa |
Factor used in evaluating spatial autocorrelation. |
Value
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
## Not run:
library(SamplingStrata)
############################
# Example of "atomic" method
############################
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)))
strata <- buildStrataDF(frame)
kmean <- KmeansSolution(strata,cv,maxclusters=30)
nstrat <- tapply(kmean$suggestions, kmean$domainvalue,
FUN=function(x) length(unique(x)))
solution <- optimStrata(method ="atomic",
framesamp = frame,
errors = cv,
nStrata = nstrat,
suggestions = kmean,
iter = 50,
pops = 10)
outstrata <- solution$aggr_strata
framenew <- solution$framenew
s <- selectSample(framenew, outstrata)
################################
# 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 <- optimStrata(method = "continuous",
framesamp = frame,
errors = cv,
nStrata = nstrat,
iter = 50,
pops = 10,
suggestions = sugg)
framenew <- solution$framenew
outstrata <- solution$aggr_strata
s <- selectSample(framenew,outstrata)
#############################
# Example of "spatial" method
#############################
library(sp)
data("meuse")
data("meuse.grid")
meuse.grid$id <- c(1:nrow(meuse.grid))
coordinates(meuse) <- c('x','y')
coordinates(meuse.grid) <- c('x','y')
library(gstat)
library(automap)
v <- variogram(lead ~ dist + soil, data = meuse)
fit.vgm.lead <- autofitVariogram(lead ~ dist + soil,
meuse,
model = "Exp")
plot(v, fit.vgm.lead$var_model)
lead.kr <- krige(lead ~ dist + soil, meuse, meuse.grid,
model = fit.vgm.lead$var_model)
lead.pred <- ifelse(lead.kr[1]$var1.pred < 0,0, lead.kr[1]$var1.pred)
lead.var <- ifelse(lead.kr[2]$var1.var < 0, 0, lead.kr[2]$var1.var)
df <- as.data.frame(list(dom = rep(1,nrow(meuse.grid)),
lead.pred = lead.pred,
lead.var = lead.var,
lon = meuse.grid$x,
lat = meuse.grid$y,
id = c(1:nrow(meuse.grid))))
frame <-buildFrameSpatial(df = df,
id = "id",
X = c("lead.pred"),
Y = c("lead.pred"),
variance = c("lead.var"),
lon = "lon",
lat = "lat",
domainvalue = "dom")
cv <- as.data.frame(list(DOM = rep("DOM1",1),
CV1 = rep(0.05,1),
domainvalue = c(1:1) ))
solution <- optimStrata(method = "spatial",
errors = cv,
framesamp = frame,
iter = 25,
pops = 10,
nStrata = 5,
fitting = 1,
kappa = 1,
range = fit.vgm.lead$var_model$range[2])
framenew <- solution$framenew
outstrata <- solution$aggr_strata
frameres <- SpatialPixelsDataFrame(points = framenew[c("LON","LAT")],
data = framenew)
frameres$LABEL <- as.factor(frameres$LABEL)
spplot(frameres,c("LABEL"), col.regions=bpy.colors(5))
s <- selectSample(framenew,outstrata)
## End(Not run)