Function reference • SamplingStrata

All functions
`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
`errors`	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
`swissframe`	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.

Reference

All functions