20.6 – Dimensional analysis

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Introduction

Cluster analysis or clustering is a multivariate analysis technique that includes a number of different algorithms for grouping objects in such a way that objects in the same group (called a cluster) are more similar to each other than they are to objects in other groups. A number of approaches have been taken, but loosely can be grouped into distance clustering methods (see Chapter 16.6 – Similarity and Distance) and linkage clustering methods: Distance methods involve calculating the distance (or similarity) between two points and whereas linkage methods involve calculating distances among the clusters. Single linkage involves calculating the distance among all pairwise comparisons between two clusters, then

Cluster analysis is common to molecular biology and phylogeny construction and more generally is an approach in use for exploratory data mining. Unsupervised machine learning (see 20.14 – Binary classification) used to classify, for example, methylation status of normal and diseased tissues from arrays (Clifford et al 2011)

Results from cluster analyses are often displayed as dendograms. Clustering methods include a number of different algorithms hierarchical clustering: single-linkage clustering; complete linkage clustering; average linkage clustering (UPGMA) centroid based clustering: k-means clustering

R packages

factoextra

MorphoFun/psa/

Principal component analysis

Bumpus data from MorphoFun/psa, variable names changed.

Figure ccc. Scatterplot English swallow mass (g) by total length (mm) by survival following winter storm

R code for graph

scatterplot(Weight~Total_length | Survival, regLine=FALSE, smooth=FALSE, boxplots=FALSE,
ellipse=list(levels=c(.9)), by.groups=TRUE, grid=FALSE, pch=c(19,19), cex=1.5, col=c(“red”,”blue”), xlab=”Total length, mm”, ylab=”Mass, g”, data=Bumpus)

Data ellipse — 90% of the pairwise points (red, did not survive; blue, did survive), not a confidence ellipse

Bumpus measured several traits, we want to use all of the data. However, highly correlated.

Figure ccc. Scatterplot matrix of Bumpus English sparrow traits. Traits were (left-right): Alar extent (mm), length (tip of beak to tip of tail), length of head (mm), length of femur (in.), length of humerus (in.), length of sternum (in.), skull width (in.), length of tibio-taurus (in.), and weight (g)

R code for graph

scatterplotMatrix(~Alar_extent+Beak_head_Length+Femur+Humerus+Keel_Length+Skull_width+Tibiotarsus+Total_length+Weight, regLine=FALSE, smooth=FALSE, diagonal=list(method=”density”), data=Bumpus)

Note. In Chapter 4, we discussed importance of white space and Y-scale for graphs that make comparisons. Figure ccc is a good example of where we trade-off the need for white space and concerns about telling the story — the various traits are positively correlated — against the dictum of an equal Y-scale for true comparisons.

Rcmdr: Statistics > Dimensional analysis > Principal component analysis …

.PC <-
princomp(~Alar_extent+Beak_head_Length+Femur+Humerus+Keel_Length+Skull_width+Tibiotarsus+Total_length+Weight, cor=TRUE, data=Bumpus)
cat(“\nComponent loadings:\n”)
print(unclass(loadings(.PC)))
cat(“\nComponent variances:\n”)
print(.PC$sd^2)
cat(“\n”)
print(summary(.PC))
screeplot(.PC)
Bumpus <<- within(Bumpus, {
PC2 <- .PC$scores[,2]
PC1 <- .PC$scores[,1]
})
})

Importance of components

Comp.1      Comp.2
Standard deviation                  2.3046882  0.9988978
Proportion of Variance           0.5901764   0.1108663
Cumulative Proportion           0.5901764   0.7010427

K-means clustering

Number of clusters

Iterations

kmeans cluster bi-plot Skittles data set

Figure ccc. Bi-plot of clusters, Skittles mini bags

Hierarchical clustering

Ward’s method

Complete linkage

McQuitty’s method

Centroid linkage

A common way to depict the results of a cluster analysis is to construct a dendogram.

Questions

[pending]

References and further reading

Bumpus, H. C. (1898). Eleventh lecture. The elimination of the unfit as illustrated by the introduced sparrow, Passer domesticus. (A fourth contribution to the study of variation.). Biology Lectures: Woods Hole Marine Biological Laboratory, 209–255.

Clifford, H., Wessely, F., Pendurthi, S., & Emes, R. D. (2011). Comparison of clustering methods for investigation of genome-wide methylation array data. Frontiers in genetics, 2, 88.

Ferreira, L., & Hitchcock, D. B. (2009). A comparison of hierarchical methods for clustering functional data. Communications in Statistics-Simulation and Computation, 38(9), 1925-1949.

Fraley C, Raftery AE. (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002; 97(458):611–31.

 

Chapter 20 contents

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