README.Rmd

---
output: github_document
keep_md: TRUE
---

<!-- README.md is generated from README.Rmd. Please edit README.Rmd file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# SamplingStrata

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.  

## Installation

You can install SamplingStrata from github with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("barcaroli/SamplingStrata")
```

## Example

This is a basic example which shows you how to solve a common problem:

```{r, eval=FALSE, echo=TRUE}
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
solution <- optimizeStrata (
	errors = swisserrors,
	strata = swissstrata,
  showPlot = FALSE)
# update sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, 
                          solution, 
                          writeFiles = FALSE)
# update sampling frame with new strata labels
data(swissframe)
framenew <- updateFrame(frame=swissframe,
                        newstrata=newstrata,
                        writeFile=FALSE)
samp <- selectSample(framenew,solution$aggr_strata,writeFiles=TRUE)
# evaluate the current solution
eval <- evalSolution(frame = framenew, 
                     outstrata =solution$aggr_strata, 
                     nsampl = 100, 
                     cens = NULL, 
                     writeFiles = FALSE)
eval$coeff_var
swisserrors

```