`VSS`

).This function applies the iclust algorithm to hierarchically cluster items to form composite scales. Clusters are combined if coefficients alpha and beta will increase in the new cluster.

Alpha, the mean split half correlation, and beta, the worst split half correlation, are estimates of the reliability and general factor saturation of the test. (See also the `omega`

function to estimate McDonald's coeffients $\omega_h$ and $\omega_t$)

```
iclust(r.mat, nclusters=0, alpha=3, beta=1, beta.size=4, alpha.size=3,
correct=TRUE,correct.cluster=TRUE, reverse=TRUE, beta.min=.5, output=1, digits=2,labels=NULL,cut=0,
n.iterations = 0,title="iclust",plot=TRUE,weighted=TRUE,cor.gen=TRUE,SMC=TRUE)
ICLUST(r.mat, nclusters=0, alpha=3, beta=1, beta.size=4, alpha.size=3,
correct=TRUE,correct.cluster=TRUE, reverse=TRUE, beta.min=.5, output=1, digits=2,labels=NULL,cut=0,
n.iterations = 0,title="ICLUST",plot=TRUE,weighted=TRUE,cor.gen=TRUE,SMC=TRUE)
```
#iclust(r.mat) #use all defaults
#iclust(r.mat,nclusters =3) #use all defaults and if possible stop at 3 clusters
#ICLUST(r.mat, output =3) #long output shows clustering history
#ICLUST(r.mat, n.iterations =3) #clean up solution by item reassignment

r.mat

A correlation matrix or data matrix/data.frame. (If r.mat is not square i.e, a correlation matrix, the data are correlated using pairwise deletion.

nclusters

Extract clusters until nclusters remain (default will extract until the other criteria are met or 1 cluster, whichever happens first). See the discussion below for alternative techniques for specifying the number of clusters.

alpha

Apply the increase in alpha criterion (0) never or for (1) the smaller, 2) the average, or 3) the greater of the separate alphas. (default = 3)

beta

Apply the increase in beta criterion (0) never or for (1) the smaller, 2) the average, or 3) the greater of the separate betas. (default =1)

beta.size

Apply the beta criterion after clusters are of beta.size (default = 4)

alpha.size

Apply the alpha criterion after clusters are of size alpha.size (default =3)

correct

Correct correlations for reliability (default = TRUE)

correct.cluster

Correct cluster -sub cluster correlations for reliability of the sub cluster , default is TRUE))

reverse

Reverse negative keyed items (default = TRUE

beta.min

Stop clustering if the beta is not greater than beta.min (default = .5)

output

1) short, 2) medium, 3 ) long output (default =1)

labels

vector of item content or labels. If NULL, then the colnames are used. If FALSE, then labels are V1 .. Vn

cut

sort cluster loadings > absolute(cut) (default = 0)

n.iterations

iterate the solution n.iterations times to "purify" the clusters (default = 0)

digits

Precision of digits of output (default = 2)

title

Title for this run

plot

Should ICLUST.rgraph be called automatically for plotting (requires Rgraphviz default=TRUE)

weighted

Weight the intercluster correlation by the size of the two clusters (TRUE) or do not weight them (FALSE)

cor.gen

When correlating clusters with subclusters, base the correlations on the general factor (default) or general + group (cor.gen=FALSE)

SMC

When estimating cluster-item correlations, use the smcs as the estimate of an item communality (SMC=TRUE) or use the maximum correlation (SMC=FALSE).

title Name of this run results A list containing the step by step cluster history, including which pair was grouped, what were the alpha and betas of the two groups and of the combined group. Note that the alpha values are ``standardized alphas'' based upon the correlation matrix, rather than the raw alphas that will come from `score.items`

The print.psych and summary.psych functions will print out just the must important results.corrected The raw and corrected for alpha reliability cluster intercorrelations. clusters a matrix of -1,0, and 1 values to define cluster membership. purified A list of the cluster definitions and cluster loadings of the purified solution. To show just the most salient items, use the cutoff option in `print.psych`

cluster.fit, structure.fit, pattern.fit There are a number of ways to evaluate how well any factor or cluster matrix reproduces the original matrix. Cluster fit considers how well the clusters fit if only correlations with clusters are considered. Structure fit evaluates R = CC' while pattern fit evaluate R = C inverse (phi) C' where C is the cluster loading matrix, and phi is the intercluster correlation matrix.

The results are best visualized using `ICLUST.graph`

, the results of which can be saved as a dot file for the Graphviz program.

A common problem in the social sciences is to construct scales or composites of items to measure constructs of theoretical interest and practical importance. This process frequently involves administering a battery of items from which those that meet certain criteria are selected. These criteria might be rational, empirical,or factorial. A similar problem is to analyze the adequacy of scales that already have been formed and to decide whether the putative constructs are measured properly. Both of these problems have been discussed in numerous texts, as well as in myriad articles. Proponents of various methods have argued for the importance of face validity, discriminant validity, construct validity, factorial homogeneity, and theoretical importance.
Revelle (1979) proposed that hierachical cluster analysis could be used to estimate a new coefficient (beta) that was an estimate of the general factor saturation of a test. More recently, Zinbarg, Revelle, Yovel and Li (2005) compared McDonald's Omega to Chronbach's alpha and Revelle's beta. They conclude that $\omega_h$ hierarchical is the best estimate. An algorithm for estimating `omega`

is available as part of this package.

Revelle and Zinbarg (2009) discuss alpha, beta, and omega, as well as other estimates of reliability.

The original ICLUST program was written in FORTRAN to run on CDC and IBM mainframes and was then modified to run in PC-DOS. The R version of iclust is a completely new version written for the psych package. Please email me if you want help with this version of iclust or if you desire more features.

A requested feature (not yet available) is to specify certain items as forming a cluster. That is, to do confirmatory cluster analysis.

The program currently has three primary functions: cluster, loadings, and graphics.

In June, 2009, the option of weighted versus unweighted beta was introduced. Unweighted beta calculates beta based upon the correlation between two clusters, corrected for test length using the Spearman-Brown prophecy formala, while weighted beta finds the average interitem correlation between the items within two clusters and then finds beta from this. That is, for two clusters A and B of size N and M with between average correlation rb, weighted beta is (N+M)^2 rb/(Va +Vb + 2Cab). Raw (unweighted) beta is 2rab/(1+rab) where rab = Cab/sqrt(VaVb). Weighted beta seems a more appropriate estimate and is now the default. Unweighted beta is still available for consistency with prior versions.

Also modified in June, 2009 was the way of correcting for item overlap when calculating the cluster-subcluster correlations for the graphic output. This does not affect the final cluster solution, but does produce slightly different path values. In addition, there are two ways to solve for the cluster - subcluster correlation.

Given the covariance between two clusters, Cab with average rab = Cab/(N*M), and cluster variances Va and Vb with Va = N + N*(N-1)*ra then the correlation of cluster A with the combined cluster AB is either

a) ((N^2)ra + Cab)/sqrt(Vab*Va) (option cor.gen=TRUE) or b) (Va - N + Nra + Cab)/sqrt(Vab*Va) (option cor.gen=FALSE)

The default is to use cor.gen=TRUE.

Although iclust will give what it thinks is the best solution in terms of the number of clusters to extract, the user will sometimes disagree. To get more clusters than the default solution, just set the nclusters parameter to the number desired. However, to get fewer than meet the alpha and beta criteria, it is sometimes necessary to set alpha=0 and beta=0 and then set the nclusters to the desired number.

Clustering 24 tests of mental ability

A sample output using the 24 variable problem by Harman can be represented both graphically and in terms of the cluster order. The default is to produce graphics using the Rgraphviz package (from BioConductor). Because this package is sometimes hard to install, there is an alternative option to write the output out using the dot language. This will create a graphic suitable for any viewing program that uses the dot language. `ICLUST.graph`

produces the dot code for Graphviz. Somewhat lower resolution graphs with fewer options are available in the `ICLUST.rgraph`

function which requires Rgraphviz. Dot code can be viewed directly in Graphviz or can be tweaked using commercial software packages (e.g., OmniGraffle)

Note that for this problem, with the default parameters, the data form one large cluster. (This is consistent with the Very Simple Structure (`VSS`

) output as well, which shows a clear one factor solution for complexity 1 data.)

An alternative solution is to ask for a somewhat more stringent set of criteria and require an increase in the size of beta for all clusters greater than 3 variables. This produces a 4 cluster solution.

It is also possible to use the original parameter settings, but ask for a 4 cluster solution.

At least for the Harman 24 mental ability measures, it is interesting to compare the cluster pattern matrix with the oblique rotation solution from a factor analysis. The factor congruence of a four factor oblique pattern solution with the four cluster solution is > .99 for three of the four clusters and > .97 for the fourth cluster.

To see the graphic output go to `ICLUST.rgraph`

(requires Rgraphviz).

Revelle, W. and Zinbarg, R. E. (2009) Coefficients alpha, beta, omega and the glb: comments on Sijtsma. Psychometrika, 2009.

`ICLUST.graph`

,`ICLUST.cluster`

, `cluster.fit`

, `VSS`

, `omega`

```
test.data <- Harman74.cor$cov
ic.out <- ICLUST(test.data)
summary(ic.out)
ic.out <- ICLUST(test.data,nclusters =4) #use all defaults and stop at 4 clusters
ic.out1 <- ICLUST(test.data,beta=3,beta.size=3) #use more stringent criteria
print(ic.out1)
plot(ic.out) #this shows the spatial representation
ic.no.graph <- ICLUST(test.data,plot=FALSE)
dot.graph <- ICLUST.graph(ic.no.graph,out.file="test.ICLUST.graph.dot") #use a dot graphics viewer
```

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