Multistage Sampling Allocation and Sample Selection
Sample sizes for each stratum
Computation of coefficient of variation (CV) for a given multivariate ...
Compute one stage multivariate optimal allocation.
Multivariate optimal allocation for different domains in two stage sta...
Derives dummy variables from target variables to the sampling frame
Check of coherence in the inputs for the allocation step
Starting values for the Design Effect ()
Sampling design variables
Estimator effect
Precision constraints (maximum CVs) as input for Bethel allocation
Evaluation of the two-stage sample design optimized solution by simula...
Input dataframes for R2BEAT two-stages sample design (when a previous ...
Prepares the design and psu file for two-stage sample design (when a p...
Plot of the sensitivity analysis for some parameters by means of grid ...
Input dataframes for R2BEAT two-stages sample design when sampling fra...
Input dataframes for R2BEAT two-stages sample design when both samplin...
Information on Primary Stage Units (PSUs) stratification
Intraclass correlation coefficients for self and non self representati...
Select sample of primary stage units (PSU)
Select sample of primary stage units (PSU)
Select sample of secondary stage units (SSU)
Function for better presentation of sensitivity information
Sensitivity analysis for choosing minimum number of SSUs per PSU (samp...
Sensitivity analysis for choosing minimum number of SSUs per PSU (no s...
Strata characteristics
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>.