R: Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys

# Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys

## Documentation for package ‘SamplingStrata’ version 1.5-5

- 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.