sscsample {Bolstad} | R Documentation |

## 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]
```

*Bolstad*version 0.2-41 Index]