optimizeStrataSpatial.RdThis function runs a set of other functions to optimise the stratification of a sampling frame, only when stratification variables are of the continuous type, and if there is also a component of spatial autocorrelation in frame units.
optimizeStrataSpatial(
errors,
framesamp,
framecens = NULL,
strcens = FALSE,
alldomains = TRUE,
dom = NULL,
nStrata = c(5),
fitting=c(1),
range=c(0),
kappa=3,
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
)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.
This is the (mandatory) dataframe containing the information related to the sampling frame.
This the (optional) dataframe containing the units to be selected in any case. It has same structure than "frame" dataframe.
Flag (TRUE/FALSE) to indicate if takeall strata do exist or not. Default is FALSE.
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'.
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.
Indicates the number of strata for each variable.
Fitting of the model(s). Default is 1.
Maximum range for spatial autocorrelation. It is a vector with as many elements as the number of target variables Y.
Factor used in evaluating spatial autocorrelation. Default is 3.
Indicates the minimum number of units that must be allocated in each stratum. Default is 2.
Indicated the maximum number of iterations (= generations) of the genetic algorithm. Default is 50.
The dimension of each generations in terms of individuals. Default is 20.
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.
This parameter indicates the rate of better solutions that must be preserved from one generation to another. Default is 0.2 (20
Parameter for genetic algorithm. In should not be changed
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.
If FALSE, the allocation is based on INTEGER values; if TRUE, the allocation is based on REAL values. Default is TRUE.
Indicates if the various dataframes and plots produced during the execution have to be written in the working directory. Default is FALSE.
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.
Should the analysis be run in parallel. Default is TRUE.
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.
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
if (FALSE) {
#############################
# 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$y,
id = c(1:nrow(meuse.grid))))
frame <-buildFrameSpatial(df = df,
id = "id",
X = c("lead.pred"),
Y = c("lead.pred"),
variance = c("lead.var"),
lon = "lon",
lat = "lat",
domainvalue = "dom")
cv <- as.data.frame(list(DOM = rep("DOM1",1),
CV1 = rep(0.05,1),
domainvalue = c(1:1) ))
solution <- optimizeStrataSpatial(errors = cv,
framesamp = frame,
iter = 25,
pops = 10,
nStrata = 5,
fitting = 1,
kappa = 1,
range = fit.vgm.lead$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)
}