Package: R2BEAT 1.0.6

Andrea Fasulo

R2BEAT: Multistage Sampling Allocation and Sample Selection

Multivariate optimal allocation for different domains in one and two stages stratified sample design. 'R2BEAT' extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). 'R2BEAT' develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of 'ReGenesees', that can be installed from <https://github.com/DiegoZardetto/ReGenesees>.

Authors:Andrea Fasulo,Giulio Barcaroli,Ilaria Bombelli,Stefano Falorsi,Alessio Guandalini,Marco Dionisio Terribili

R2BEAT_1.0.6.tar.gz
R2BEAT_1.0.6.zip(r-4.7)R2BEAT_1.0.6.zip(r-4.6)R2BEAT_1.0.6.zip(r-4.5)
R2BEAT_1.0.6.tgz(r-4.6-any)R2BEAT_1.0.6.tgz(r-4.5-any)
R2BEAT_1.0.6.tar.gz(r-4.7-any)R2BEAT_1.0.6.tar.gz(r-4.6-any)
R2BEAT_1.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
R2BEAT/json (API)

# Install 'R2BEAT' in R:
install.packages('R2BEAT', repos = c('https://barcaroli.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/barcaroli/r2beat/issues

Pkgdown/docs site:https://barcaroli.github.io

Datasets:
  • allocation - Sample sizes for each stratum
  • deft_start - Starting values for the Design Effect
  • design - Sampling design variables
  • effst - Estimator effect
  • errors - Precision constraints (maximum CVs) as input for Bethel allocation
  • PSU_strat - Information on Primary Stage Units (PSUs) stratification
  • rho - Intraclass correlation coefficients for self and non self representative in the strata
  • strata - Strata characteristics

On CRAN:

Conda:

4.46 score 2 stars 36 scripts 368 downloads 25 exports 8 dependencies

Last updated from:38418fc33f. Checks:8 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR256
source / vignettesERROR336
linux-release-x86_64ERROR227
macos-release-arm64ERROR162
macos-oldrel-arm64ERROR163
windows-develERROR172
windows-releaseERROR165
windows-oldrelERROR165
wasm-releaseOK115

Exports:adjust_CVsaggrStratabeat.1cvbeat.1cv_2beat.1stbeat.2stbeat.cvbuild_dummy_variablescheck_inputCVs_hinteval_2stageexpected_CVinput_to_beat.2st_1input_to_beat.2st_2paretoplot_sensprepareInputToAllocation_beat.1stprepareInputToAllocation1prepareInputToAllocation2select_PSUselect_PSU2select_SSUsens_namessensitivity_min_SSUsensitivity_min_SSU2

Dependencies:codetoolsdoParallelforeachglueiteratorslpSolveMASSsampling

Readme and manuals

Help Manual

Help pageTopics
adjustCVsadjust_CVs
aggrStrataaggrStrata
Sample sizes for each stratumallocation
The function returns a dataframe with planned and actual coefficients of variation (CV) in a multivariate multi-domain allocation problem.beat.1cv
The function returns a dataframe with planned and actual coefficients of variation (CV) in a multivariate multi-domain allocation problem.beat.1cv_2
Compute one stage multivariate optimal allocation.beat.1st
Multivariate optimal allocation for different domains in two stage statified sample designbeat.2st
Computation of coefficient of variation (CV) for a given multivariate multiple allocationbeat.cv
build_dummy_variablesbuild_dummy_variables
Check of coherence in the inputs for the allocation stepcheck_input
cvs_hintCVs_hint
Starting values for the Design Effect (deft)deft_start
Sampling design variablesdesign
Estimator effecteffst
Precision constraints (maximum CVs) as input for Bethel allocationerrors
Evaluation of the two-stage sample design optimized solution by simulationeval_2stage
expected_CVexpected_CV
Input dataframes for R2BEAT two-stages sample design (when a previous round of the survey is available, but no sampling frame)input_to_beat.2st_1
Prepares the design and psu file for two-stage sample design (when a previous round of the survey is available, but no sampling frame)input_to_beat.2st_2
Pareto functionpareto
Plot of the sensitivity analysis for some parameters by means of grid searchplot_sens
The function returns a dataframe, starting from the sampling frame (either universe or sample of a previous survey) with strata information.prepareInputToAllocation_beat.1st
Input dataframes for R2BEAT two-stages sample design when sampling frame is availableprepareInputToAllocation1
Input dataframes for R2BEAT two-stages sample design when both sampling frame and a previous round of the survey are availableprepareInputToAllocation2
Information on Primary Stage Units (PSUs) stratificationPSU_strat
Intraclass correlation coefficients for self and non self representative in the stratarho
Select sample of primary stage units (PSU)select_PSU
Select sample of primary stage units (PSU)select_PSU2
Select sample of secondary stage units (SSU)select_SSU
Function for better presentation of sensitivity informationsens_names
Sensitivity analysis for choosing minimum number of SSUs per PSU (sampling frame available)sensitivity_min_SSU
Sensitivity analysis for choosing minimum number of SSUs per PSU (no sampling frame available)sensitivity_min_SSU2
Strata characteristicsstrata