Simple, Stratified and Cluster Sampling

Description

Samples from a fixed population using either simple random sampling, stratitified sampling or cluster sampling.

Usage

sscsample(
size,
n.samples,
sample.type = c("simple", "cluster", "stratified"),
x = NULL,
strata = NULL,
cluster = NULL
)


Arguments

 size the desired size of the sample n.samples the number of repeat samples to take sample.type the sampling method. Can be one of "simple", "stratified", "cluser" or 1, 2, 3 where 1 corresponds to "simple", 2 to "stratified" and 3 to "cluster" x a vector of measurements for each unit in the population. By default x is not used, and the builtin data set sscsample.data is used strata a corresponding vector for each unit in the population indicating membership to a stratum cluster a corresponding vector for each unit in the population indicating membership to a cluster

Value

A list will be returned with the following components:

 samples a matrix with the number of rows equal to size and the number of columns equal to n.samples. Each column corresponds to a sample drawn from the population s.strata a matrix showing how many units from each stratum were included in the sample means a vector containing the mean of each sample drawn

Author(s)

James M. Curran, Dept. of Statistics, University of Auckland. Janko Dietzsch, Proteomics Algorithm and Simulation,Zentrum f. Bioinformatik Tuebingen Fakultaet f. Informations- und Kognitionswissenschaften, Universitaet Tuebingen

Examples


## Draw 200 samples of size 20 using simple random sampling
sscsample(20,200)

## Draw 200 samples of size 20 using simple random sampling and store the
## results. Extract the means of all 200 samples, and the 50th sample
res = sscsample(20,200)
res$means res$samples[,50]