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SamplingStrata (version 1.5-4)

Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys

Description

In the field of stratified sampling design, this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation. Functions for the execution of the genetic algorithm are a modified version of the functions in the 'genalg' package. M.Ballin, G.Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling".

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install.packages('SamplingStrata')

Monthly Downloads

484

Version

1.5-4

License

GPL (>= 2)

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Repository

https://github.com/barcaroli/SamplingStrata/

Homepage

https://barcaroli.github.io/SamplingStrata/

Maintainer

Giulio Barcaroli

Last Published

November 15th, 2022

Functions in SamplingStrata (1.5-4)

assignStrataLabel

Function to assign the optimized strata labels

KmeansSolution2

Initial solution obtained by applying kmeans clustering of frame units

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.

Builds the "strata" dataframe containing information on target variables Y's distributions in the different strata, starting from a frame

Multivariate optimal allocation

Builds the "sampling frame" dataframe from a dataset containing information on all the units in the population of reference

KmeansSolutionSpatial

Initial solution obtained by applying kmeans clustering of frame units

buildFrameSpatial

Builds the "sampling frame" dataframe from a dataset containing information all the units in the population of reference including spatial

Adjustment of the sample size in case it is externally given

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.

Builds the "strata" dataframe containing information on target variables Y's distributions in the different strata, starting from sample data or from a frame

Precision constraints (maximum CVs) as input for Bethel allocation

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

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.

Checks the inputs to the package: dataframes "errors", "strata" and "sampling frame"

Expected coefficients of variation of target variables Y

Best stratification of a sampling frame for multipurpose surveys

Optimization of the stratification of a sampling frame given a sample survey

Dataset 'nations'

Plotting sampling rates in the different strata for each domain in the solution.

Plot bivariate distibutions in strata

optimizeStrata2

Best stratification of a sampling frame for multipurpose surveys (only with continuous stratification variables)

Selection of a stratified sample from the frame with srswor method

selectSampleSystematic

Selection of a stratified sample from the frame with systematic method

optimizeStrataSpatial

Best stratification of a sampling frame for multipurpose surveys considering also spatial correlation

Dataframe containing information on strata in the frame

Procedure to apply Bethel algorithm and select a sample from given strata

Information on strata structure

prepareSuggestion

Prepare suggestions for optimization with method = "continuous" or "spatial"

selectSampleSpatial

Selection of geo-referenced points from the frame

Assigns new labels to atomic strata on the basis of the optimized aggregated strata

Updates the initial frame on the basis of the optimized stratification

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

Precision constraints (maximum CVs) as input for Bethel allocation

Dataframe containing information on strata in the swiss municipalities frame

Execution and compared evaluation of optimization runs

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.

Initial solution obtained by applying kmeans clustering of atomic strata