Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys

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Documentation for package ‘SamplingStrata’ version 1.5-4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

adjustSize	Adjustment of the sample size in case it is externally given
aggrStrata2	Builds the "strata" dataframe containing information on target variables Y's distributions in the different strata, starting from a frame
aggrStrataSpatial	Builds the "strata" dataframe containing information on target variables Y's distributions in the different strata, starting from a frame where units are spatially correlated.
assignStrataLabel	Function to assign the optimized strata labels
bethel	Multivariate optimal allocation
buildFrameDF	Builds the "sampling frame" dataframe from a dataset containing information on all the units in the population of reference
buildFrameSpatial	Builds the "sampling frame" dataframe from a dataset containing information all the units in the population of reference including spatial
buildStrataDF	Builds the "strata" dataframe containing information on target variables Y's distributions in the different strata, starting from sample data or from a frame
buildStrataDFSpatial	Builds the "strata" dataframe containing information on target variables Y's distributions in the different strata, starting from sample data or from a frame
checkInput	Checks the inputs to the package: dataframes "errors", "strata" and "sampling frame"
computeGamma	Function that allows to calculate a heteroscedasticity index, together with associate prediction variance, to be used by the optimization step to correctly evaluate the standard deviation in the strata due to prediction errors.
errors	Precision constraints (maximum CVs) as input for Bethel allocation
evalSolution	Evaluation of the solution produced by the function 'optimizeStrata' by selecting a number of samples from the frame with the optimal stratification, and calculating average CV's on the target variables Y's.
expected_CV	Expected coefficients of variation of target variables Y
KmeansSolution	Initial solution obtained by applying kmeans clustering of atomic strata
KmeansSolution2	Initial solution obtained by applying kmeans clustering of frame units
KmeansSolutionSpatial	Initial solution obtained by applying kmeans clustering of frame units
nations	Dataset 'nations'
optimizeStrata	Best stratification of a sampling frame for multipurpose surveys
optimizeStrata2	Best stratification of a sampling frame for multipurpose surveys (only with continuous stratification variables)
optimizeStrataSpatial	Best stratification of a sampling frame for multipurpose surveys considering also spatial correlation
optimStrata	Optimization of the stratification of a sampling frame given a sample survey
plotSamprate	Plotting sampling rates in the different strata for each domain in the solution.
plotStrata2d	Plot bivariate distibutions in strata
prepareSuggestion	Prepare suggestions for optimization with method = "continuous" or "spatial"
procBethel	Procedure to apply Bethel algorithm and select a sample from given strata
selectSample	Selection of a stratified sample from the frame with srswor method
selectSampleSpatial	Selection of geo-referenced points from the frame
selectSampleSystematic	Selection of a stratified sample from the frame with systematic method
strata	Dataframe containing information on strata in the frame
summaryStrata	Information on strata structure
swisserrors	Precision constraints (maximum CVs) as input for Bethel allocation
swissframe	Dataframe containing information on all units in the population of reference that can be considered as the final sampling unit (this example is related to Swiss municipalities)
swissmunicipalities	The Swiss municipalities population
swissstrata	Dataframe containing information on strata in the swiss municipalities frame
tuneParameters	Execution and compared evaluation of optimization runs
updateFrame	Updates the initial frame on the basis of the optimized stratification
updateStrata	Assigns new labels to atomic strata on the basis of the optimized aggregated strata
var.bin	Allows to transform a continuous variable into a categorical ordinal one by applying a modified version of the k-means clustering function in the 'stats' package.