fishmethods (version 1.3-0)

clusmean: Estimation of Population Attributes and Effective Sample Size for Fishes Collected Via Cluster Sampling

Description

Calculates mean attribute, variance, and effective sample size for samples collected by simple random cluster sampling.

Usage

clusmean(popchar = NULL, cluster = NULL, clustotal = NULL)

Arguments

popchar

vector of population characteristic measurements (e.g., length, weight, etc.). One row represents the measurement for an individual.

cluster

vector of numeric or character codes identifying individual clusters (or hauls).

clustotal

vector of total number of fish caught per cluster.

Value

• Matrix table of total number of clusters (n), total number of samples (M), total number of samples measured (m), the mean attribute (R), variance of R (varR), variance of population attribute (s2x), and effective sample size (meff).

Details

In fisheries, gears (e.g., trawls, haul seines, gillnets, etc.) are used to collect fishes. Often, estimates of mean population attributes (e.g., mean length) are desired. The samples of individual fish are not random samples, but cluster samples because the "haul" is the primary sampling unit. Correct estimation of mean attributes requires the use of cluster sampling formulae. Estimation of the general mean attribute and variance follows Pennington et al. (2002). In addition, the effective sample size (the number of fish that would need to be sampled randomly to obtained the same precision as the mean estimate from cluster sampling) is also calculated. The total number of fish caught in a cluster (clustotal) allows correct computation for one- and two-stage sampling of individuals from each cluster (haul).

References

Pennington, M. , L. Burmeister, and V. Hjellvik. 2002. Assessing the precision of frequency distributions estimated from trawl-survey samples Fish. Bull. 100:74-80.

Examples

data(codlengths)
clusmean(popchar=codlengths$tl,cluster=codlengths$station,
clustotal=codlengths$total)