Optimum Allocation for Adaptive Multi-Wave Sampling in R: The R Package optimall

06/17/2021
by   Jasper B. Yang, et al.
0

The R package optimall offers a collection of functions that efficiently streamline the design process of sampling in surveys ranging from simple to complex. The package's main functions allow users to interactively define and adjust strata cut points based on values or quantiles of auxiliary covariates, adaptively calculate the optimum number of samples to allocate to each stratum using Neyman or Wright allocation, and select specific IDs to sample based on a stratified sampling design. Using real-life epidemiological study examples, we demonstrate how optimall facilitates an efficient workflow for the design and implementation of surveys in R. Although tailored towards multi-wave sampling under two- or three-phase designs, the R package optimall may be useful for any sampling survey.

READ FULL TEXT
research
09/14/2022

Two-stage Sampling Design and Sample Selection with the R package R2BEAT

R2BEAT (R "to" Bethel Extended Allocation for Two-stage sampling) is an ...
research
05/28/2020

Optimal multi-wave sampling for regression modelling in two-phase designs

Two-phase designs involve measuring extra variables on a subset of the c...
research
12/10/2020

PoolTestR: An R package for estimating prevalence and regression modelling with pooled samples

Pooled testing (also known as group testing), where diagnostic tests are...
research
11/30/2022

Integrated distance sampling models for simple point counts

Point counts (PCs) are widely used in biodiversity surveys, but despite ...
research
04/14/2023

Recursive Neyman Algorithm for Optimum Sample Allocation under Box Constraints on Sample Sizes in Strata

The optimal sample allocation in stratified sampling is one of the basic...
research
12/02/2019

SSNdesign – an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

Streams and rivers are biodiverse and provide valuable ecosystem service...
research
06/17/2016

PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm

This paper discusses about an R package that implements the Pattern Sequ...

Please sign up or login with your details

Forgot password? Click here to reset