排序方式: 共有2条查询结果,搜索用时 15 毫秒
1
1.
Canonical Variate Analysis (CVA) is one of the most useful of multivariate methods. It is concerned with separating between and within group variation among N samples from K populations with respect to p measured variables. Mahalanobis distance between the K group means can be represented as points in a (K - 1) dimensional space and approximated in a smaller space, with the variables shown as calibrated biplot axes. Within group variation may also be shown, together with circular confidence regions and other convex prediction regions, which may be used to discriminate new samples. This type of representation extends to what we term Analysis of Distance (AoD), whenever a Euclidean inter-sample distance is defined. Although the N × N distance matrix of the samples, which may be large, is required, eigenvalue calculations are needed only for the much smaller K × K matrix of distances between group centroids. All the ancillary information that is attached to a CVA analysis is available in an AoD analysis. We outline the theory and the R programs we developed to implement AoD by presenting two examples. 相似文献
2.
In this paper we show how biplot methodology can be combined with
various forms of discriminant analyses leading to highly informative visual displays of
the respective class separations. It is demonstrated that the concept of distance as
applied to discriminant analysis provides a unified approach to a wide variety of
discriminant analysis procedures that can be accommodated by just changing to an
appropriate distance metric. These changes in the distance metric are crucial for the
construction of appropriate biplots. Several new types of biplots viz. quadratic
discriminant analysis biplots for use with heteroscedastic stratified data, discriminant
subspace biplots and flexible discriminant analysis biplots are derived and their use
illustrated. Advantages of the proposed procedures are pointed out. Although biplot
methodology is in particular well suited for complementing J > 2 classes discrimination
problems its use in 2-class problems is also illustrated. 相似文献
1