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
)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 |
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 |
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
## 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]
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