Package 'SamplingStrata'

Description

The optimisation step finds the best stratification that minimises the sample size under given precision constraints. In some cases, the goal is not the minimisation of the sample size, as this value is given externally. Nonetheless, it is still possible to perform the optimisation of the stratification, and then to proceed to an adjustment of the sample size by increasing or decreasing it proportionally in each resulting stratum.

Usage

adjustSize(size, strata, cens=NULL, minnumstr=2)

Arguments

Value

The strata generated by the function 'optimizeStrata', where the variable 'SOLUZ' has been adjusted by taking into account the total required sample size

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
solution <- optimizeStrata (
	errors = swisserrors,
	strata = swissstrata,
)
# 
sum(solution$aggr_strata$SOLUZ)
# Adjustment of total sample size (decreasing)
adjustedStrata <- adjustSize(size=300,strata=solution$aggr_strata)
sum(adjustedStrata$SOLUZ)
# Adjustment of total sample size (increasing)
adjustedStrata <- adjustSize(size=500,strata=solution$aggr_strata)
sum(adjustedStrata$SOLUZ)

## End(Not run)

Description

This function builds the dataframe "strata" considering as input a given domain in the sampling frame. In case a dataframe "model" is given, the anticipated variance in the strata for each target variable is calculated

Usage

aggrStrata2(dataset, 
            model=NULL, 
            vett,
            dominio)

Arguments

Value

A dataframe containing strata

Author(s)

Giulio Barcaroli

Examples

## Not run: 
strata <- aggrStrata2(dataset=frame,
                      model=NULL,
                      vett,
                      dominio=1)

## End(Not run)

Description

This function builds the dataframe "strata" considering as input a given domain in the sampling frame. The variance in each stratum is calculated by considering also the component of spatial autocorrelation.

Usage

aggrStrataSpatial(dataset,
            fitting,
            range,
            kappa, 
            vett, 
            dominio)

Arguments

Value

A dataframe containing strata

Author(s)

Giulio Barcaroli

Examples

## Not run: 
strata <- aggrStrataSpatial(dataset=frame,
                            fitting=1,
                            range=800,
                            kappa=1)

## End(Not run)

Description

Function to assign the optimized strata labels to new sampling units in the frame on the basis of the strata structure obtained by executing the function 'summaryStrata' after optimizing with 'optimizeStrata2'

Usage

assignStrataLabel(dataset, s)

Arguments

Value

The same dataset in input with the label of the optimized stratum

Examples

## Not run: 
library(SamplingStrata)
data("swissmunicipalities")
data("errors")
errors$CV1 <- 0.1
errors$CV2 <- 0.1
errors <- errors[rep(row.names(errors),7),]
errors$domainvalue <- c(1:7)
errors
swissmunicipalities$id <- c(1:nrow(swissmunicipalities))
swissmunicipalities$domain = 1
frame <- buildFrameDF(swissmunicipalities,
                      id = "id",
                      domainvalue = "REG",
                      X = c("Surfacesbois","Surfacescult"),
                      Y = c("Pop020", "Pop2040")
)
solution <- optimizeStrata2 (
  errors, 
  frame,
  nStrata = 5,
  iter = 10, 
  pops = 10, 
  writeFiles = FALSE, 
  showPlot = TRUE, 
  parallel = FALSE) 
strataStructure <- summaryStrata(solution$framenew, solution$aggr_strata)
strataStructure


newset <- assignStrataLabel(solution$framenew,strataStructure)

## End(Not run)

Description

Multivariate optimal allocation for different domains of interest in stratified sample design under a given stratification of the sampling frame

Usage

bethel  (
          stratif,
          errors, 
          minnumstrat=2,          
          maxiter=200,
          maxiter1=25,
          printa=FALSE,
		  realAllocation=FALSE,
          epsilon=1e-11
          )

Arguments

Value

A vector containing the computed optimal allocation

Author(s)

Daniela Pagliuca with contributions from Teresa Buglielli and Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data(strata)
data(errors)
n <- bethel(strata, errors, printa=TRUE)
sum(n)
attributes(n)$confr
attributes(n)$outcv

## End(Not run)

Description

This function allows to build the information regarding the sampling frame of the population of reference. Mandatory variables are: (i) the name of the dataset containing the sampling frame of the population of reference (ii) an identifier (Id) (iii) a set of auxiliary variables X (iv) a set of target variables Y (v) the indicator of the domain to which the unit belongs A further optional parameter is the the name of the variable containing weights (in case instead of a frame we use a sampling survey)

Usage

buildFrameDF(df, id, X, Y, domainvalue)

Arguments

Value

A dataframe

Author(s)

Giulio Barcaroli

Examples

## Not run: 
data(swissmunicipalities)
id = "Nom"
X = c("Surfacesbois","Surfacescult")
Y = c("Pop020","Pop2040")
domainvalue = "REG"
frame <- buildFrameDF(swissmunicipalities,id,X,Y,domainvalue)
head(frame)

## End(Not run)

Description

This function allows to build the information regarding the sampling frame of the population of reference. Mandatory variables are: (i) the name of the dataset containing the sampling frame of the population of reference (ii) an identifier (Id) (iii) a set of auxiliary variables 'X' (iv) a set of target variables 'Y' (v) a set of prediction errors variables 'variance' (vi) longitude (vii) latitude (viii) the indicator of the domain to which the unit belongs

Usage

buildFrameSpatial(df, id, X, Y, variance, lon, lat, domainvalue)

Arguments

Value

A dataframe

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(sp)
library(gstat)
library(automap)
library(SamplingStrata)
data("meuse")
data("meuse.grid")
meuse.grid$id <- c(1:nrow(meuse.grid))
coordinates(meuse)<-c("x","y")
coordinates(meuse.grid)<-c("x","y")
#################
# kriging
#################
v <- variogram(lead ~ dist + soil, data=meuse)
fit.vgm <- autofitVariogram(lead ~ elev + soil, meuse, model = "Exp")
plot(v, fit.vgm$var_model)
fit.vgm$var_model
g <- NULL
g <- gstat(g, "Pb", lead ~ dist + soil, meuse)
g
vm <- variogram(g)
vm.fit <- fit.lmc(vm, g, vgm(psill=fit.vgm$var_model$psill[2], 
                             model="Exp", range=fit.vgm$var_model$range[2], 
                             nugget=fit.vgm$var_model$psill[1]))
# Prediction on the whole grid
preds <- predict(vm.fit, meuse.grid)
names(preds)
# [1] "Pb.pred" "Pb.var"
preds$Pb.pred <- ifelse(preds$Pb.pred < 0,0,preds$Pb.pred)
df <- NULL
df$id <- meuse.grid$id
df$Pb.pred <- preds@data$Pb.pred
df$Pb.var <- preds@data$Pb.var
df$lon <- meuse.grid$x
df$lat <- meuse.grid$y
df$dom1 <- 1
df <- as.data.frame(df)
frame <- buildFrameSpatial(df=df,
                      id="id",
                      X=c("Pb.pred"),
                      Y=c("Pb.pred"),
                      variance=c("Pb.var"),
                      lon="lon",
                      lat="lat",
                      domainvalue = "dom1")
head(frame)

## End(Not run)

Description

This function allows to build the information regarding strata in the population required as an input by the algorithm of Bethel for the optimal allocation. In order to estimate means and standard deviations for target variables Y's, we need data coming from: (1) a previous round of the survey whose sample we want to plan; (2) sample data from a survey with variables that are proxy to the ones we are interested to; (3) a frame containing values of Y's variables (or proxy variables) for all the population. In all cases, each unit in the dataset must contain auxiliary information (X's variables) and also target variables Y's (or proxy variables) values: under these conditions it is possible to build the dataframe "strata", containing information on the distribution of Y's in the different strata (namely, means and standard deviations), together with information on strata (total population, if it is to be censused or not, the cost per single interview). If the information is contained in a sample dataset, a variable named WEIGHT is expected to be present. In case of a frame, no such variable is given, and the function will define a WEIGHT variable for each unit, whose value is always '1'. Missing values for each Y variable will not be taken into account in the computation of means and standard deviations (in any case, NA's can be present in the dataset). The dataframe "strata" is written to an external file (tab delimited, extension "txt"), and will be used as an input by the function "optimizeStrata".

Usage

buildStrataDF(dataset,
              model=NULL,
              progress=TRUE,
              verbose=TRUE)

Arguments

Value

A dataframe containing strata

Author(s)

Giulio Barcaroli

Examples

## Not run: 
# Plain example without model
data(swissframe)
strata <- buildStrataDF(dataset=swissframe,model=NULL)
head(strata)

# More complex example with models
library(SamplingStrata)
data(swissmunicipalities)
swiss <- swissmunicipalities[,c("HApoly","Surfacesbois","Airind","POPTOT")]
Y1 = swiss$Surfacesbois
X1 = swiss$HApoly
mod1 <- lm( Y1 ~ X1 )
summary(mod1)
mod1$coefficients[2]
summary(mod1)$sigma

Y2 = swiss$Airind
X2 = swiss$POPTOT
plot(log(X2[X2>0]),log(Y2[X2>0]))
mod2 <- lm( log(Y2[X2 > 0 & Y2>0]) ~ log(X2[X2 > 0 & Y2>0]) )
summary(mod2)
mod2$coefficients[2]
summary(mod2)$sigma

swiss$id <- c(1:nrow(swiss))
swiss$dom <- 1
frame <- buildFrameDF(swiss,id="id",X="id",Y=c("HApoly","POPTOT"),domainvalue="dom")

model <- NULL
model$type[1] <- "linear"
model$beta[1] <- mod1$coefficients[2]
model$sig2[1] <- summary(mod1)$sigma
model$gamma[1] = 2
model$type[2] <- "loglinear"
model$beta[2] <- mod2$coefficients[2]
model$sig2[2] <- summary(mod2)$sigma
model$gamma[2] = NA
model <- as.data.frame(model)

strata <- buildStrataDF(dataset=frame, model=model)

## End(Not run)

Description

This function allows to build the information regarding strata in the population required as an input by the algorithm of Bethel for the optimal allocation. In order to estimate means and standard deviations for target variables Y's, we need data coming from: (1) a previous round of the survey whose sample we want to plan; (2) sample data from a survey with variables that are proxy to the ones we are interested to; (3) a frame containing values of Y's variables (or proxy variables) for all the population. In all cases, each unit in the dataset must contain auxiliary information (X's variables) and also target variables Y's (or proxy variables) values: under these conditions it is possible to build the dataframe "strata", containing information on the distribution of Y's in the different strata (namely, means and standard deviations), together with information on strata (total population, if it is to be censused or not, the cost per single interview). If the information is contained in a sample dataset, a variable named WEIGHT is expected to be present. In case of a frame, no such variable is given, and the function will define a WEIGHT variable for each unit, whose value is always '1'. Missing values for each Y variable will not be taken into account in the computation of means and standard deviations (in any case, NA's can be present in the dataset). The dataframe "strata" is written to an external file (tab delimited, extension "txt"), and will be used as an input by the function "optimizeStrata".

Usage

buildStrataDFSpatial(dataset,
                     fitting=c(1),
                     range=c(0),
                     kappa=3,
                     progress=FALSE,
                     verbose=FALSE)

Arguments

Value

A dataframe containing strata

Author(s)

Giulio Barcaroli

Examples

## Not run: 
strata <- buildStrataDFSpatial(dataset=frame,range=800)

## End(Not run)

Description

This functions checks the internal structure of the different input dataframes ("errors", "strata" and "sampling frame"), and also the correctness of the relationships among them.

Usage

checkInput(errors=NULL, strata=NULL, sampframe=NULL)

Arguments

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
data(swissframe)
checkInput(swisserrors,swissstrata,swissframe)
checkInput(strata=swissstrata,sampframe=swissframe)
checkInput(strata=swissstrata)

## End(Not run)

Description

When the anticipated variance has to be calculated during the execution of the optimization step, his function allows to calculate a heteroscedasticity index, together with associate prediction variance, to be used to correctly evaluate the variance in the strata. The function returns a list where the first object is the heteroscedasticity index and the second is the associated standard deviation in the strata due to prediction errors. The two parameters are calculated in this way: (i) residuals 'e' are grouped in clusters defined by values of the explanatory variable 'x'; (ii) a model is fitted by considering log(e) and log(mean(x)) values; (iii) the intercept is the value of standard deviation of residuals; (iv) the slope is the value of the heteroscedasticity index. These two values can be passed as parameters of the model, or used to calculate prediction errors for ach unit in the frame.

Usage

computeGamma(e,x,nbins,showPlot)

Arguments

Value

A list containing: (i) the value of the heteroscedasticity index, (ii) associated standard deviation, (iii) R^2 of the interpolating model.

Author(s)

Marco Ballin, Giulio Barcaroli

Examples

## Not run: 
data("swissmunicipalities")
swiss_sample <- swissmunicipalities[sample(c(1:nrow(swissmunicipalities)),500),]
mod_Airind_POPTOT <- lm(swiss_sample$Airind ~ swiss_sample$POPTOT)
computeGamma(mod_Airind_POPTOT$residuals,
             swiss_sample$POPTOT,
             nbins = 8)
#     gamma     sigma  r.square 
# 0.8029292 0.0150446 0.9598539

## End(Not run)

Description

Dataframe containing precision levels (expressed in terms of acceptable CV's)

Usage

data(errors)

Format

The constraint data frame (errors) contains a row per each domain value with the following variables:

DOM

Type of domain code (factor)

CV1

Planned coefficient of variation for first variable Y1 (numeric)

CVj

Planned coefficient of variation for j-th variable Yj (numeric)

CVn

Planned coefficient of variation for last variable Yn (numeric)

domainvalue

Value of the domain to which the constraints refer (numeric)

Details

Note: the names of the variables must be the ones indicated above

Examples

## data(errors)
## errors

Description

The user can indicate the number of samples that must be selected by the optmized frame. First, the true values of the parameters are calculated from the frame. Then, for each sample the sampling estimates are calculated, together with the differences between them and the true values of the parameters. At the end, an estimate of the CV is produced for each target variable, in order to compare them with the precision constraints set at the beginning of the optimization process. If the flag 'writeFiles' is set to TRUE, boxplots of distribution of the CV's in the different domains are produced for each Y variable ('cv.pdf'), together with boxplot of the distributions of differences between estimates and values of the parameters in the population ('differences.pdf').

Usage

evalSolution(frame,
		outstrata,
		nsampl=100,
		cens=NULL,
		writeFiles=TRUE,
		outputFolder = file.path(getwd(),"simulation"),
		progress=TRUE)

Arguments

Value

A list containing (i) the CV distribution in the domains, (ii) the bias distribution in the domains, (iii) the dataframe containing the sampling estimates by domain

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
solution <- optimizeStrata (
	errors = swisserrors,
	strata = swissstrata,
)
# update sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, solution, writeFiles = TRUE)
# update sampling frame with new strata labels
data(swissframe)
framenew <- updateFrame(frame=swissframe,newstrata=newstrata,writeFile=TRUE)
samp <- selectSample(framenew,solution$aggr_strata,writeFiles=TRUE)
# evaluate the current solution
results <- evalSolution(framenew, solution$aggr_strata, 10, cens=NULL, writeFiles = TRUE)
results$coeff_var
results$rel_bias

## End(Not run)

Description

Once optimized the sampling frame, this function allows to calculate the expected coefficients of variation on the aggregated strata of the current optimized solution, and to compare them to the precision constraints.

Usage

expected_CV(strata)

Arguments

Value

Matrix containing values of the CV's in the different domains

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata ( 
    errors = swisserrors, 
    strata = swissstrata, 
)
# calculate CV's on Y's
expected_CV(solution$aggr_strata)
# compare to precision constraints
swisserrors

## End(Not run)

Description

In order to speed the convergence towards the optimal solution, an initial one can be given as "suggestion" to "optimizeStrata" function. The function "KmeansSolution" produces this initial solution using the k-means algorithm by clustering atomic strata on the basis of the values of the means of target variables in them. Also, if the parameter "nstrata" is not indicated, the optimal number of clusters is determined inside each domain, and the overall solution is obtained by concatenating optimal clusters obtained in domains. The result is a dataframe with two columns: the first indicates the clusters, the second the domains.

Usage

KmeansSolution(strata, 
               errors, 
               nstrata=NA, 
			         minnumstrat=2,
               maxclusters = NA,
               showPlot=TRUE)

Arguments

Value

A dataframe containing the solution

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)

# suggestion
solutionKmean <- KmeansSolution(strata=swissstrata,
								errors=swisserrors,
								nstrata=NA,
								showPlot=TRUE)

# number of strata to be obtained in each domain		
nstrat <- tapply(solutionKmean$suggestions,
                 solutionKmean$domainvalue,
                 FUN=function(x) length(unique(x)))

# optimisation of sampling strata
solution <- optimStrata ( 
  method = "atomic",
  errors = swisserrors, 
  strata = swissstrata,
  nStrata = nstrat,
  suggestions = solutionKmean
)

## End(Not run)

Description

This function has to be used only in conjunction with "optimizeStrata2", or with "optimStrata" when method = "continuous", i.e. in the case of optimizing with only continuous stratification variables. The function "KmeansSolution2" has a twofold objective: - to give indication about a possible best number of final strata (by fixing a convenient value for "maxclusters", and leaving NA to "nstrata"; - to give an initial solution fo the optimization step. If the parameter "nstrata" is not indicated, the optimal number of clusters is determined inside each domain, and the overall solution is obtained by concatenating optimal clusters obtained in domains. The result is a dataframe with two columns: the first indicates the clusters, the second the domains.

Usage

KmeansSolution2(frame,
                model=NULL,
                errors,
                nstrata = NA,
                minnumstrat =2,
                maxclusters = NA,
                showPlot = TRUE)

Arguments

Value

A dataframe containing the solution

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data("swissmunicipalities")
swissmunicipalities$id <- c(1:nrow(swissmunicipalities))
swissmunicipalities$dom <- 1
frame <- buildFrameDF(swissmunicipalities,
                      id = "id",
                      domainvalue = "REG",
                      X = c("Pop020", "Pop2040"),
                      Y = c("Pop020", "Pop2040")
)
cv <- NULL
cv$DOM <- "DOM1"
cv$CV1 <- 0.1
cv$CV2 <- 0.1
cv <- as.data.frame(cv)
cv <- cv[rep(row.names(cv),7),]
cv$domainvalue <- c(1:7)
cv

# Solution with kmean clustering 
kmean <- KmeansSolution2(frame,model=NULL,errors=cv,nstrata=NA,maxclusters=4)
# number of strata to be obtained in each domain in final solution	
nstrat <- tapply(solutionKmean$suggestions,
                 solutionKmean$domainvalue,
                 FUN=function(x) length(unique(x)))
# Prepare suggestion for optimization
sugg <- prepareSuggestion(kmean,frame,nstrat)
# Optimization
# Attention: number of strata must be the same than in kmeans
solution <- optimStrata (
  method = "continuous",
  cv, 
  framesamp=frame,
  iter = 50,
  pops = 20,
  nStrata = nstrat,
  suggestions = sugg,
  writeFiles = FALSE,
  showPlot = FALSE,
  parallel = FALSE
)

## End(Not run)

Description

This function has to be used only in conjunction with "optimizeStrataSpatial", i.e. in the case of optimizing with only continuous stratification variables and with a component of spatial autocorrelation. The function "KmeansSolutionSpatial" has a twofold objective: - to give indications about a possible best number of final strata (by fixing a convenient value for "maxclusters", and leaving NA to "nstrata"; - to give an initial solution fo the optimization step. If the parameter "nstrata" is not indicated, the optimal number of clusters is determined inside each domain, and the overall solution is obtained by concatenating optimal clusters obtained in domains. The result is a dataframe with two columns: the first indicates the clusters, the second the domains.

Usage

KmeansSolutionSpatial(frame, 
                    fitting = 1, 
                    range = c(0), 
                    kappa = 3, 
                    errors, 
                    nstrata = NA, 
                    minnumstrat = 2, 
                    maxclusters = NA, 
                    showPlot = TRUE)

Arguments

Value

A dataframe containing the solution

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(sp)
library(gstat)
library(automap)
library(SamplingStrata)
#################
# data
#################
# locations (155 observed points)
data("meuse")
# grid of points (3103)
data("meuse.grid")
meuse.grid$id <- c(1:nrow(meuse.grid))
coordinates(meuse)<-c("x","y")
coordinates(meuse.grid)<-c("x","y")
#################
# kriging
#################
v <- variogram(lead ~ dist + soil, data=meuse)
fit.vgm <- autofitVariogram(lead ~ elev + soil, meuse, model = "Exp")
plot(v, fit.vgm$var_model)
fit.vgm$var_model
# model    psill    range
# 1   Nug 1524.895   0.0000
# 2   Exp 8275.431 458.3303
g <- NULL
g <- gstat(g, "Pb", lead ~ dist + soil, meuse)
g
vm <- variogram(g)
vm.fit <- fit.lmc(vm, g, vgm(psill=fit.vgm$var_model$psill[2], 
                  model="Exp", range=fit.vgm$var_model$range[2], 
                  nugget=fit.vgm$var_model$psill[1]))
# Prediction on the whole grid
preds <- predict(vm.fit, meuse.grid)
names(preds)
# [1] "Pb.pred" "Pb.var"
preds$Pb.pred <- ifelse(preds$Pb.pred < 0,0,preds$Pb.pred)
df <- NULL
df$Pb.pred <- preds@data$Pb.pred
df$Pb.var <- preds@data$Pb.var
df$dom1 <- 1
df <- as.data.frame(df)
df$id <- meuse.grid$id
#####################################
# Optimization with kmeans clustering
#####################################
frame <- buildFrameDF(df=df,
                      id="id",
                      X=c("Pb.pred"),
                      Y=c("Pb.pred"),
                      domainvalue = "dom1")
frame$var1 <- df$Pb.var
frame$lon <- meuse.grid$x
frame$lat <- meuse.grid$y
cv <- as.data.frame(list(DOM=rep("DOM1",1),
                         CV1=rep(0.05,1),
                         domainvalue=c(1:1) ))
km <- KmeansSolutionSpatial(frame,
                            errors = cv,
                            fitting = 1,
                            range = fit.vgm$var_model$range[2],
                            kappa=1,
                            nstrata=NA,
                            maxclusters = 5)
############################
# Analysis and visualization
############################
strataKm <- aggrStrataSpatial(dataset=frame,
                        fitting = 1,
                        range = fit.vgm$var_model$range[2],
                        kappa=1,
                        vett=km$suggestions,
                        dominio=1)
strataKm$SOLUZ <- bethel(strataKm,cv)
sum(strataKm$SOLUZ)
framenew <- frame
framenew$LABEL <- km$suggestions
strataKm$STRATO <- strataKm$stratum
ssKm <- summaryStrata(framenew,strataKm)
ssKm
frameres <- SpatialPointsDataFrame(data=framenew, 
                                   coords=cbind(framenew$lon,framenew$lat) )
frameres2 <- SpatialPixelsDataFrame(points=frameres[c("lon","lat")], 
                                    data=framenew)
frameres2$LABEL <- as.factor(frameres2$LABEL)
spplot(frameres2,c("LABEL"), col.regions=bpy.colors(5))

## End(Not run)

Description

Dataset containing data on 207 countries (from 'nations.txt' in Rcmdr, with missing values imputed)

Usage

data(nations)

Format

A data frame with 207 observations on the following variables:

Country

Name of the country

TFR

Total Fertility Rate

contraception

Rate of women making use of contraceptive means

infant.mortality

Infant mortality rate

GDP

Gross Domestic Product ($ per capita)

region

Continent (description)

Continent

Continent (code)

Source

Rcmdr package

Description

This function runs a set of other functions to optimise the stratification of a sampling frame

Usage

optimizeStrata(
	errors , 
	strata , 
	cens = NULL, 
	strcens = FALSE,
	alldomains = TRUE,
	dom = NULL,	
	initialStrata = NA, 
	addStrataFactor = 0.0, 
	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
	)

Arguments

Value

A list containing (1) the vector of the solution and (2) the optimal 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 <- optimizeStrata(strata = strata,
                        errors = cv,
                        initialStrata = nstrat,
                        suggestions = kmean,
                        iter = 50,
                        pops = 10)
outstrata <- solution$aggr_strata
newstrata <- updateStrata(strata,solution)
framenew <- updateFrame(frame, newstrata)
s <- selectSample(framenew, outstrata)

## End(Not run)

Description

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

Usage

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

Value

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

## 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)

Description

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, and if there is also a component of spatial autocorrelation in frame units.

Usage

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
	)

Arguments

Value

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

## Not run: 
#############################
# 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)


## End(Not run)

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

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)

Description

Once the optimization step has been carried out, by applying this function it is possible to obtain the visualization of the proportion of sampling units in the different strata for each domain in the obtained solution.

Usage

plotSamprate(solution, dom)

Arguments

Value

Plot

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata ( 
    errors = swisserrors, 
    strata = swissstrata, 
)
# plot of the sampling rates in strata 
for (i in (1:length(unique(swissstrata$DOM1)))) plotSamprate(solution, i)

## End(Not run)

Description

Plots a 2d graph showing obtained strata

Usage

plotStrata2d (x,outstrata,domain,vars,labels)

Arguments

Value

A formatted output containing information on the strata in the given domain

Examples

## Not run: 
library(SamplingStrata)
data("swissmunicipalities")
swissmunicipalities = swissmunicipalities[swissmunicipalities$REG==1,]
data("errors")
swissmunicipalities$id <- c(1:nrow(swissmunicipalities))
swissmunicipalities$domain = 1
frame <- buildFrameDF(swissmunicipalities,
                      id = "id",
                      domainvalue = "REG",
                      X = c("Surfacesbois","Surfacescult"),
                      Y = c("Pop020", "Pop2040")
)
solution <- optimStrata (method = "continuous",
                        errors = errors,, 
                        framesamp = frame,
                        nStrata = 8, 
                        iter = 25, 
                        pops = 10) 
p <- plotStrata2d(solution$framenew, 
                  solution$aggr_strata,
                  domain = 1, 
                  vars = c("X1","X2"),
                  labels = c("Surfacesbois","Surfacescult"))
p

## End(Not run)

Description

This function has to be used only in conjunction with "KmeansSolution2" or with "KmeansSolutionSpatial", i.e. in the case of optimizing with only continuous stratification variables. This function prepares the suggestion for the optimization function in case of continuous variables (i.e. with with "optimStrata" when method = "continuous" or method = "spatial").

Usage

prepareSuggestion(kmean = kmean, frame = frame, nstrat = nstrat)

Arguments

Value

A dataframe containing the suggestions

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data("swissmunicipalities")
swissmunicipalities$id <- c(1:nrow(swissmunicipalities))
swissmunicipalities$dom <- 1
frame <- buildFrameDF(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)))

# Solution with kmean clustering 
kmean <- KmeansSolution2(frame,model=NULL,errors=cv,nstrata=NA,maxclusters=4)
# Number of strata for each domain
nstrat <- tapply(kmean$suggestions,
                 kmean$domainvalue,
                 FUN=function(x) length(unique(x)))
# Prepare suggestion for optimization step                 
sugg <- prepareSuggestion(kmean = kmean,
                          frame = frame,
                          nstrat = nstrat)
# Optimization
solution <- optimStrata (
  method="continuous",
  errors=cv, 
  framesamp=frame,
  iter = 50,
  pops = 10,
  nStrata = nstrat,
  suggestions = sugg)

## End(Not run)

Description

This function allows to execute a complete procedure from the Bethel optimal allocation to the selection of a sample, without having to optimize the strata, that are supposed to be given and fixed.

Usage

procBethel(framesamp,
           framecens,
           errors,
           sampling_method=c("srs","systematic","spatial"),
           minnumstrat=2)

Arguments

Value

List containing (1) the selected sample, (2) the strata with allocated sampling units, (3) the take-all strata (4) the sampling frame with the labels linking to (2) (5) the frame with take-all units, with the labels linking to (3)

Author(s)

Giulio Barcaroli

Examples

## Not run: 
data("swissmunicipalities")
swissmun <- swissmunicipalities[swissmunicipalities$REG < 4,
                                c("REG","COM","Nom","HApoly",
                                  "Surfacesbois","Surfacescult",
                                  "Airbat","POPTOT")]
ndom <- length(unique(swissmun$REG))
cv <- as.data.frame(list(DOM=rep("DOM1",ndom),
                         CV1=rep(0.10,ndom),
                         CV2=rep(0.10,ndom),
                         domainvalue=c(1:ndom) ))
cv
swissmun$HApoly.cat <- var.bin(swissmun$HApoly,15)
swissmun$POPTOT.cat <- var.bin(swissmun$POPTOT,15)
frame <- buildFrameDF(df = swissmun,
                         id = "COM",
                         X = c("POPTOT.cat","HApoly.cat"),
                         Y = c("Airbat","Surfacesbois"),
                         domainvalue = "REG")
summary(frame)
#----Selection of units to be censused from the frame
ind_framecens <- which(frame$X1 > 9)
framecens <- frame[ind_framecens,]
#----Selection of units to be sampled from the frame
# (complement to the previous)
framesamp <- frame[-ind_framecens,]

a <- procBethel(framesamp,framecens,errors=cv,sampling_method="srs",minnumstrat=2)
head(a$sample)
expected_CV(a$strata)

## End(Not run)

Description

Once optimal stratification has been obtained, and a new frame has been built by assigning to the units of the old one the new strata labels, it is possible to select a stratified sample from the frame with the simple random sampling without replacement (srswor) method. The result of the execution of "selectSample" function is a dataframe containing the selected units, with their weights (inverse of the probabilities of inclusion). It is possible to output this dataframe in a .csv file. One more .csv file is produced ("sampling_check"), containing coeherence checks between (a) population in frame strata (b) population in optimised strata (c) planned units to be selected in optimised strata (d) actually selected units (e) sum of weights in each stratum

Usage

selectSample(frame, outstrata, writeFiles = FALSE,outputFolder = file.path(getwd(),"output"), verbatim=TRUE)

Arguments

Value

A dataframe containing the sample

Author(s)

Giulio Barcaroli with contribution from Diego Zardetto

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata ( 
    errors = swisserrors, 
    strata = swissstrata 
)
# updating sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, solution)
# updating sampling frame with new strata labels
data(swissframe)
framenew <- updateFrame(frame=swissframe,newstrata=newstrata)
# selection of sample
sample <- selectSample(frame=framenew,outstrata=solution$aggr_strata)
head(sample)

## End(Not run)

Description

Once optimal stratification has been obtained, and a new frame has been built by assigning to the units of the old one the new strata labels, it is possible to select a stratified sample from the frame. If geographical coordinates are available in the frame, in order to obtain spatially distributed selected points this function makes use of the 'lpm2_kdtree' function from the' SamplingBigData' package (Lisic-Grafstrom).

Usage

selectSampleSpatial(frame, outstrata, coord_names)

Arguments

Value

A dataframe containing the selected sample

Author(s)

Giulio Barcaroli

Examples

## Not run: 
#############################
# Example of "spatial" method
#############################
library(sp)
# locations (155 observed points)
data("meuse")
# grid of points (3103)
data("meuse.grid")
meuse.grid$id <- c(1:nrow(meuse.grid))
coordinates(meuse)<-c("x","y")
coordinates(meuse.grid)<-c("x","y")

## Kriging model
library(automap)
kriging_lead = autoKrige(log(lead) ~ dist, meuse, meuse.grid)
plot(kriging_lead,sp.layout = NULL, justPosition = TRUE)
kriging_zinc = autoKrige(log(zinc) ~ dist, meuse, meuse.grid)
plot(kriging_zinc, sp.layout = list(pts = list("sp.points", meuse)))
r2_lead <- 1 - kriging_lead$sserr/sum((meuse$lead-mean(meuse$lead))^2)
r2_lead
r2_zinc <- 1 - kriging_zinc$sserr/sum((meuse$zinc-mean(meuse$zinc))^2)
r2_zinc
df <- NULL
df$id <- meuse.grid$id
df$lead.pred <- kriging_lead$krige_output@data$var1.pred
df$lead.var <- kriging_lead$krige_output@data$var1.var
df$zinc.pred <- kriging_zinc$krige_output@data$var1.pred
df$zinc.var <- kriging_zinc$krige_output@data$var1.var
df$lon <- meuse.grid$x
df$lat <- meuse.grid$y
df$dom1 <- 1
df <- as.data.frame(df)
head(df)

## Optimization 
library(SamplingStrata)
frame <- buildFrameSpatial(df=df,
                           id="id",
                           X=c("lead.pred","zinc.pred"),
                           Y=c("lead.pred","zinc.pred"),
                           variance=c("lead.var","zinc.var"),
                           lon="lon",
                           lat="lat",
                           domainvalue = "dom1")
cv <- as.data.frame(list(DOM=rep("DOM1",1),
                         CV1=rep(0.01,1),
                         CV2=rep(0.01,1),
                         domainvalue=c(1:1) ))
set.seed(1234)
solution <- optimStrata (
  method = "spatial",
  errors=cv, 
  framesamp=frame,
  iter = 15,
  pops = 10,
  nStrata = 5,
  fitting = c(r2_lead,r2_zinc),
  range = c(kriging_lead$var_model$range[2],kriging_zinc$var_model$range[2]),
  kappa=1,
  writeFiles = FALSE,
  showPlot = TRUE,
  parallel = FALSE)
framenew <- solution$framenew
outstrata <- solution$aggr_strata

# Sample selection
samp <- selectSampleSpatial(framenew,outstrata,coord_names=c("LON","LAT"))
table(samp$STRATO)

# Plot
library(sf)
samp_sf <- st_as_sf(samp, coords = c("LON", "LAT"))
plot(samp_sf["STRATO"])


## End(Not run)

Description

Once optimal stratification has been obtained, and a new frame has been built by assigning to the units of the old one the new stratum labels, it is possible to select a stratified sample from the frame with the systematic method, that is a selection that begins selecting the first unit by an initial ramndomly chosen starting point, and proceeding in selecting other units by adding an interval that is the inverse of the sampling rate in the stratum.

This selection method can be useful if associated to a particular ordering of the selection frame, where the ordering variable(s) can be considered as additional stratum variable(s).

The result of the execution of "selectSampleSystematic" function is a dataframe containing selected units, with the probabilities of inclusion. It is possible to output this dataframe in a .csv file. One more .csv file is produced ("sampling_check"), containing coeherence checks between (a) population in frame strata (b) population in optimised strata (c) planned units to be selected in optimised strata (d) actually selected units (e) sum of weights in each stratum

Usage

selectSampleSystematic(frame, 
                      outstrata, 
                      sortvariable = NULL, 
                      writeFiles = FALSE,
                      verbatim=TRUE)

Arguments

Value

A dataframe containing the sample

Author(s)

Giulio Barcaroli with contribution from Diego Zardetto

Examples

#
# The following example is realistic, but is time consuming
#
## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata ( 
    errors = swisserrors, 
    strata = swissstrata)
# updating sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, solution)
# updating sampling frame with new strata labels
data(swissframe)
framenew <- updateFrame(frame=swissframe,newstrata=newstrata)
# adding variable "POPTOT" to framenew
data("swissmunicipalities")
framenew <- merge(framenew,swissmunicipalities[,c("REG","Nom","POPTOT")],
                  by.x=c("REG","ID"),by.y=c("REG","Nom"))
# selection of sample with systematic method
sample <- selectSampleSystematic(frame=framenew,
								outstrata=solution$aggr_strata,
								sortvariable="POPTOT")
head(sample)

## End(Not run)

Description

Dataframe containing information on strata in the frame

Usage

data(strata)

Format

The strata data frame (strata) contains a row per stratum with the following variables:

stratum

Identifier of the stratum (numeric)

N

Number of population units in the stratum (numeric)

X1

Value of first auxiliary variable X1 in the stratum (factor)

Xi

Value of i-th auxiliary variable Xi in the stratum (factor)

Xk

Value of last auxiliary variable Xk in the stratum (factor)

M1

Mean in the stratum of the first variable Y1 (numeric)

Mj

Mean in the stratum of the j-th variable Yt (numeric)

Mn

Mean in the stratum of the last variable Y (numeric)

S1

Standard deviation in the stratum of the first variable Y (numeric)

Sj

Standard deviation in the stratum of the j-th variable Yt (numeric)

Sn

Standard deviation in the stratum of the last variable Y (numeric)

cens

Flag (1 indicates a take all straum, 0 a sampling stratum) (numeric) Default = 0

cost

Cost per interview in each stratum. Default = 1 (numeric)

DOM1

Value of domain to which the stratum belongs (factor or numeric)

Details

Note: the names of the variables must be the ones indicated above

Examples

# data(strata)
# head(strata)

Description

Information on strata (population, allocation, sampling rate and X variables ranges)

Usage

summaryStrata(x,outstrata,progress,writeFiles)

Arguments

Value

A formatted output containing information on the strata in the given domain

Examples

## Not run: 
library(SamplingStrata)
data("swissmunicipalities")
data("errors")
errors$CV1 <- 0.1
errors$CV2 <- 0.1
errors <- errors[rep(row.names(errors),7),]
errors$domainvalue <- c(1:7)
errors
swissmunicipalities$id <- c(1:nrow(swissmunicipalities))
swissmunicipalities$domain = 1
frame <- buildFrameDF(swissmunicipalities,
                      id = "id",
                      domainvalue = "REG",
                      X = c("Surfacesbois","Surfacescult"),
                      Y = c("Pop020", "Pop2040")
)
solution <- optimizeStrata2 (
  errors, 
  frame,
  nStrata = 5,
  iter = 10, 
  pops = 10, 
  writeFiles = FALSE, 
  showPlot = TRUE, 
  parallel = FALSE) 
strataStructure <- summaryStrata(solution$framenew, solution$aggr_strata)
strataStructure

## End(Not run)

Description

Dataframe containing precision levels (expressed in terms of acceptable CV's)

Usage

data(errors)

Format

The constraint data frame (swisserrors) contains a row per each domain value with the following variables:

DOM

Type of domain code (factor)

CV1

Planned coefficient of variation for first variable Y1 (number of men and women aged between 0 and 19) (numeric)

CV2

Planned coefficient of variation for second variable Y2 (number of men and women aged between 20 and 39) (numeric)

CV3

Planned coefficient of variation for third variable Y3 (number of men and women aged between 40 and 64) (numeric)

CV4

Planned coefficient of variation for forth variable Y4 (number of men and women aged between 65 and over) (numeric)

domainvalue

Value of the domain to which the constraints refer (numeric)

Examples

## data(swisserrors)
## swisserrors

Description

Dataframe containing information on all municipalities in Swiss (it is a derivation of dataframe "swissmunicipalities" in "sampling" package)

Usage

data(swissframe)

Format

The "swissframe" dataframe contains a row per each Swiss municipality with the following variables:

progr

Progressive associated to the frame unit (numeric)

id

Name of the frame unit (character)

X1

Classes of total population in the municipality (factor with 18 values)

X2

Classes of wood area in the municipality (factor with 3 values)

X3

Classes of area under cultivation in the municipality (factor with 3 values)

X4

Classes of mountain pasture area in the municipality (factor with 3 values)

X5

Classes of area with buildings in the municipality (factor with 3 values)

X6

Classes of industrial area in the municipality (factor with 3 values)

Y1

Number of men and women aged between 0 and 19 (numeric)

Y2

Number of men and women aged between 20 and 39 (numeric)

Y3

Number of men and women aged between 40 and 64 (numeric)

Y4

Number of men and women aged between 65 and over (numeric)

domainvalue

Value of domain to which the municipality belongs (factor or numeric)

Examples

#data(swissframe)
#head(strata)

Description

This population provides information about the Swiss municipalities in 2003.

Usage

data(swissmunicipalities)

Format

A data frame with 2896 observations on the following 22 variables:

id

Municipality unique identifier.

CT

Swiss canton.

REG

Swiss region.

COM

municipality number.

Nom

municipality name.

HApoly

municipality area.

Surfacesbois

wood area.

Surfacescult

area under cultivation.

Alp

mountain pasture area.

Airbat

area with buildings.

Airind

industrial area.

P00BMTOT

number of men.

P00BWTOT

number of women.

Pop020

number of men and women aged between 0 and 19.

Pop2040

number of men and women aged between 20 and 39.

Pop4065

number of men and women aged between 40 and 64.

Pop65P

number of men and women aged between 65 and over.

H00PTOT

number of households.

H00P01

number of households with 1 person.

H00P02

number of households with 2 persons.

H00P03

number of households with 3 persons.

H00P04

number of households with 4 persons.

POPTOT

total population.

lat

latitude.

long

longitude.

Source

Swiss Federal Statistical Office.

Examples

# data(swissmunicipalities)
# hist(swissmunicipalities$POPTOT)

Description

Dataframe containing information on strata in the swiss municipalities frame

Usage

data(swissframe)

Format

The "swissstrata" dataframe contains a row per stratum with the following variables:

STRATO

Identifier of the stratum (character)

N

Number of population units in the stratum (numeric)

X1

Classes of total population in the municipality (factor with 18 values)

X2

Classes of wood area in the municipality (factor with 3 values)

X3

Classes of area under cultivation in the municipality (factor with 3 values)

X4

Classes of mountain pasture area in the municipality (factor with 3 values)

X5

Classes of area with buildings in the municipality (factor with 3 values)

X6

Classes of industrial area in the municipality (factor with 3 values)

M1

Mean in the stratum of Y1 (number of men and women aged between 0 and 19)(numeric)

M2

Mean in the stratum of Y2 (number of men and women aged between 20 and 39) (numeric)

M3

Mean in the stratum of Y3 (number of men and women aged between 40 and 64) (numeric)

M4

Mean in the stratum of Y4 (number of men and women aged between 64 and over) (numeric)

S1

Standard deviation in the stratum of Y1 (number of men and women aged between 0 and 19)(numeric)

S2

Standard deviation in the stratum of Y2 (number of men and women aged between 20 and 39) (numeric)

S3

Standard deviation in the stratum of Y3 (number of men and women aged between 40 and 64) (numeric)

S4

Standard deviation in the stratum of Y4 (number of men and women aged between 64 and over) (numeric)

cens

Flag (1 indicates a take all straum, 0 a sampling stratum) (numeric) Default = 0

cost

Cost per interview in each stratum. Default = 1 (numeric)

DOM1

Value of domain to which the stratum belongs Default = 1 (factor or numeric)

Examples

# data(swissstrata)
# head(swissstrata)

Description

This function allows to execute a number of optimization runs, varying in a controlled way the values of the parameters, in order to find their most suitable values. by comparing the resulting solutions. It can be applied only to a given domain per time. Most parameters of this function are the same than those of the function 'optimizeStrata', but they are given in a vectorial format. The length of each vector is given by the number of optimizations to be run: it is therefore possible to define different combination of values of the parameters for each execution of 'optimizeStrata'. After each optimization run, from the corrisponding optimized frame a given number of samples are drawn. For each of them, the estimates of the target variables Y's are computed ("precision"), together with the associated coefficients of variations, and the absolute differences between the values of the estimates and the true values in the population ("bias"). Information on the distribution of bias (differences) and precision (CV's) are outputted, and also boxplots for each of them are produced, in order to permit a compared evaluation of the different solutions found in the different runs. As the optimal solution is stored for each run, after the evaluation it is possible to use it directly, or as a "suggestion" for a new optimization with more iterations (in order to improve it).

Usage

tuneParameters (
		noptim,
		nsampl,
		frame,
		errors = errors, 
		strata = strata, 
		cens = NULL, 
		strcens = FALSE,
		alldomains = FALSE,
		dom = 1,			
		initialStrata, 
		addStrataFactor, 
		minnumstr, 
		iter, 
		pops, 
		mut_chance, 
		elitism_rate,
		writeFiles
		)

Arguments

Value

A dataframe containing for each iteration the number of strata, the cost of the solution and the values of the expected CV's

Author(s)

Giulio Barcaroli

Examples

#
## Not run: 
#------------------------------------------------------------
# data setting
library(SamplingStrata)
data(swissstrata)
data(swisserrors)
data(swissframe)
# As this function can be applied only to a given domain per time,
# we select the first domain
frame <- swissframe[swissframe$domainvalue == 1,]
strata <- swissstrata[swissstrata$DOM1 == 1,]
errors <- swisserrors[swisserrors$domainvalue == 1,]
#------------------------------------------------------------
# parameters setting
noptim <- 10 # Number of runs
nsampl <- 100 # Number of samples to be drawn after each optimization
initialStrata <- ceiling(c(1:noptim)*0.1*(nrow(strata))) # Number of initial strata
addStrataFactor <- rep(0.01,noptim) # Rate for increasing initial strata
minnumstr <- rep(2,noptim) # Minimum number of units per stratum
iter <- rep(200,noptim) # Number of iterations for each optimization
pops <- rep(20,noptim) # Number of solutions for each iteration
mut_chance <- rep(0.004,noptim) # Mutation chance
elitism_rate <- rep(0.2,noptim) # Elitism rate
#------------------------------------------------------------
results <- tuneParameters (
  noptim,
  nsampl,
  frame,
  errors = errors, 
  strata = strata,
  cens = NULL, 
  strcens = FALSE,
  alldomains = FALSE,
  dom = 1,			
  initialStrata, 
  addStrataFactor, 
  minnumstr, 
  iter, 
  pops, 
  mut_chance, 
  elitism_rate
  )
results

## End(Not run)

Description

Once optimal stratification has been obtained, and new labels have been attributed to initial atomic strata ("newstrata"), it is important to report the new classification of units in the sampling frame by attributing new strata labels to each unit. By executing this function, a new frame will be obtained with the same structure of the old, but with the addition of a new stratum label. The initial frame must contain a variable named 'domainvalue' that indicates the same values of the domain that has been used with the 'optimizeStrata' function. If no domains have been defined, this variable will contains all 1's, but it must exist

Usage

updateFrame(frame, newstrata, writeFiles = FALSE, outputFolder = file.path(getwd(),"output"))

Arguments

Value

A dataframe containing the frame

Author(s)

Giulio Barcaroli

Examples

#
# The following example is realistic, but is time consuming
#
## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata ( 
    errors = swisserrors, 
    strata = swissstrata)
# updating sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, solution, writeFiles = TRUE)
# updating sampling frame with new strata labels
data(swissframe)
framenew <- updateFrame(frame=swissframe, newstrata=newstrata, writeFiles = TRUE)

## End(Not run)

Description

Once optimal stratification has been obtained ('outstrata'), then we need to attribute new strata labels to each atomic stratum. By executing this function, a new dataframe "newstrata" will be obtained with the same structure of the old, ("strata") but with the addition of a new stratum label. By indicating "YES" to "writeFile" parameter, the dataframe "newstrata" will be written to a delimited file ("newstrata.txt"). Also a second delimited file ("strata_aggregation.txt") will be outputted, containing the indication of the relations bewteen atomic and aggregated strata.

Usage

updateStrata(strata, solution, writeFiles = FALSE, outputFolder = file.path(getwd(),"output"))

Arguments

Value

A dataframe containing the strata

Author(s)

Giulio Barcaroli

Examples

## Not run: 
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata ( 
    errors = swisserrors, 
    strata = swissstrata, 
)
# updating sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, solution, writeFiles = TRUE)

## End(Not run)

Description

The optimization of a frame stratification is applicable only in presence of all categorical auxiliary variables in the frame. If one or more continuous auxiliary variables are in the frame, it is necessary to pre-process in order to convert them into categorical (ordinal) variables. The applied method is the "k-means" clustering method contained in the in "stats" package. This function ensures that the final result is in an ordered categorical variable.

Usage

var.bin(x,
		bins=3,
		iter.max=100)

Arguments

Value

Binned variable

Examples

library(SamplingStrata)
data(swissmunicipalities)
data(swissframe)
swissframe$X1 <- var.bin(swissmunicipalities$POPTOT,bins = 18)
table(swissframe$X1)
tapply(swissmunicipalities$POPTOT,swissframe$X1,mean)