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Clustering and Classification methods for Biologists

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Discriminant Analysis

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Iris Data : Discriminant Analysis 1 (4 pedictors)

These are the results of a discriminant analysis using all four predictors

Summary of Canonical Discriminant Functions

Eigenvalues

Function	Eigenvalue	% of Variance	Cumulative %	Canonical Correlation
1	32.192	99.1	99.1	.985
2	.285	.9	100.0	.471

 

Wilks' Lambda

Test of Function(s)	Wilks' Lambda	Chi-square	df	Sig.
1 through 2	.023	546.115	8	.000
2	.778	36.530	3	.000

 

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Standardized Canonical Discriminant Function Coefficients

	Function
	1	2
SEPAL_L	-0.427	0.012
SEPAL_W	-0.521	0.735
PETAL_L	0.947	-0.401
PETAL_W	0.575	0.581

 

Structure Matrix

	Function
	1	2
PETAL_L	0.706	0.168
SEPAL_W	-0.119	0.864
PETAL_W	0.633	0.737
SEPAL_L	0.223	0.311

 

Functions at Group Centroids

	Function
SPECIES	1	2
I.setosa	-7.608	.215
I.versicolor	1.825	-.728
I.virginica	5.783	.513

 

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Classification Statistics

Prior Probabilities for Groups

	Prior	Cases Used in Analysis
SPECIES			Unweighted
I.setosa	.333	50	50.000
I.versicolor	.333	50	50.000
I.virginica	.333	50	50.000
Total	1.000	150

 

 

Classification Results

Predicted Group Membership			Total
		SPECIES		I.setosa	I.versicolor	I.virginica
Original	Count	I.setosa	50	0	0	50
		I.versicolor	0	48	2	50
		I.virginica	0	1	49	50
	%	I.setosa	100.0	.0	.0	100
		I.versicolor	.0	96.0	4.0	100
		I.virginica	.0	2.0	98.0	100
Cross-validated	Count	I.setosa	50	0	0	50
		I.versicolor	0	48	2	50
		I.virginica	0	1	49	50
	%	I.setosa	100.0	.0	.0	100
		I.versicolor	.0	96.0	4.0	100
		I.virginica	.0	2.0	98.0	100

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