The visualisation of multiplicative interaction
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The Visualisation of Multiplicative Interaction. John Gower, Open University, U.K. and Mark De Rooij, Leiden University, NL. 1. Asymmetric case (e.g. PCA). 2. Symmetric case (e.g. multiplicative interaction as in the biadditive model for genotype/environment interaction).

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The Visualisation of Multiplicative Interaction

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The Visualisation of Multiplicative Interaction

John Gower, Open University, U.K.

and

Mark De Rooij, Leiden University, NL


1. Asymmetric case (e.g. PCA).

2. Symmetric case (e.g. multiplicative interaction as in the biadditive model for genotype/environment interaction).

Biplot axes are coordinate axes

We shall first review the asymmetric case and then show how the ideas extend to the symmetric case and suggest some useful variants.


Aircraft data


Scatterplot of RGF and SPR


Usual linear biplot display (Gabriel)

Uses inner-product interpretation rirj cosθij


Same biplot but with calibrated axes interpretation


Once it is recognised that biplot axes behave as coordinate axes, the usual devices of choosing a convenient origin and orientation are available.

In particular, efforts can be made to disentangle the cases from the axes representing the variables.

There is no loss of information but the displays are more helpful.

Improving the display


Same biplot but with better choice of origin and axes rotated to correspond closely to a conventional x-y plot


Nonlinear PCA biplot (cases omitted)

The variables are of ordered categorical type, with four

categories for each variable, as shown on the next slide.


Origin shifted, cases shown


Interaction

The same methods may be used to display multiplicative interaction biplots for biadditive models but one has to choose either the rows (say genotypes) or columns (say environments) to play the role of variables plotted as calibrated axes; the others are then plotted as points.


Inner-product biplot

Black points-varieties, Red points (unlabelled) indicate varieties.


Inner-product biplot with equal scaling


Distance plot with equal scaling


Distance plot with asymmetric scaling (XD versus Y)


Possibilities and Difficulties

  • Different scalings have a major influence on the distances

  • Utilize this to

    • Optimize the correlation between data and distances

    • Minimize the constant in order to get optimal discriminability between the magnitude of distances

  • But never take a look at the plot without noticing the main effects


Biplot with scaled axes


Symmetric approaches

A problem with these biplots is that they do not treat rows and columns symmetrically.

rirjcosij = rirjsin(ij+90) so we may rotate the environment points through a rightangle and replace inner-products by areas of triangles.

Another way is to note that

So that inner-products may be replaced by Euclidean distance, provided we reparameterise the main effects.


Biplot – Area interpretation, The area of the triangle shows the interaction

between Variety Ho at “environment” H4. The sign depends on whether area is

determined clockwise or anticlockwise, so the line joining O and Ho separates

positive and negative interactions. Equal area loci are lines through H4 and through

Ho parallel to the opposite sides.


PLAN

1 Show Rob Kempton’s Gabriel type display, followed by its calibrated axis version (if possible with shifted origin and rotation). I hope to scan this from the original paper.

2 Say not symmetric as is desirable and offer (a) area display and (b) distance display.

3 Sketch algebra of (a) rirjcosij = rirjsin(ij+90) and (b) for equivalence of biadditive model parameterised in terms of innerproducts and distances. (b) needs something on tuning constants and handling of “main effects”.


Some other types of biplot


Some References

Denis, J-B., & Gower, J.C. (1996) Asymptotic confidence regions for biadditive models: Interpreting genotype-environment interactions. Applied Statistics,45, 479-493.

De Rooij, M. and Heiser, W.J. (2005). Graphical representations and odds ratios in a distance association model for the analysis of cross-classified data, Psychometrika, 70, *-*.

Gower, J.C. and Hand, D. J. (1996) Biplots. London: Chapman and Hall, 277 + xvi pp.

Kempton, R. A. (1984) The use of biplots in interpreting variety by environment interactions. J. Agric. Sci. Camb., 103, 123-135.


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