fishmethods (version 1.5-0)

mhclus: Measures of Homogeneity Within Clusters

Description

Calculates measures of homogeneity within clusters according to Lohr (1999)

Usage

mhclus(popchar=NULL, cluster = NULL, mhtype=c(1,2))

Arguments

popchar

vector containing containing the population characteristic (e.g., length, weight, etc.). One line per individual.

cluster

vector containing the variable used to identify the cluster. Identifier can be numeric or character.

mhtype

the type of homogeneity measure to calculate: 1 = intra-cluster correlation coefficient, 2 = adjusted r-square.Default = c(1,2).

Value

• A matrix of values by mhtype.

Details

The intracluster correlation coefficient (ICC) and adjusted r-square provides measures of homeogeneity within clusters. ICC is defined to be the Pearson crrelation coefficient for NM(M-1)pairs (yij,yik) for i between 1 and N and j<>k (see Lohr (1999: p. 139). The average cluster size is used as the equal cluster size quantity in Equation 5.8 of Lohr (1999). If the clusters are perfectly homogeneous (total variation is all between-cluster variability), then ICC=1. Adjusted r-square is an alternative quantity following Equation 5.10 in Lohr (1999). It is the relative amount of variability in the population explained by the cluster means, adjusted for the number of degrees of freedom. If the clusters are homogeneous, then the cluster means are highly variable relative to variation within clusters, and the r-square will be high. All clusters with zero elementary units should be deleted before calculation.

References

Lohr, S. L. Sampling: design and analysis. Duxbury Press,New York, NY. 494 p.

See Also

lfstrclus lfclus

Examples

data(codcluslen)
  tem<-codcluslen[codcluslen[,1]=="NorthCape" & codcluslen[,3]>0,]
  outs<-data.frame(tow=NA,len=NA)
  cnt<-0
  for(i in 1:as.numeric(length(tem$number))){
    for(j in 1:tem$number[i]){
     cnt<-cnt+1
     outs[cnt,1]<-tem$tow[i]
     outs[cnt,2]<-tem$length[i]
   }
 }
 mhclus(popchar=outs$len,cluster=outs$tow)