sscsample function

Simple, Stratified and Cluster Sampling

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

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

Returns

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

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]

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