Abstract
Multivariate analysis is concerned with datasets which have more than one response variable for each observational or experimental unit. The datasets can be summarized by data matrices X with n rows and p columns, the rows representing the observations or cases, and the columns the variables. The matrix can be viewed either way, depending whether the main interest is in the relationships between the cases or between the variables. Note that for consistency we represent the variables of a case by the row vector x.
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A divisor of n — 1 is more conventional, but princomp calls coy . wt , which uses n .
In S we would use (1: 150)[duplicated(do.call(“paste”, data.frame(ir)))]
A corrected version of biplot . princomp from our library is used.
actually, this needs a modification to pltree . agnes
This will not work in earlier versions of S-PLUS (3.2 and 3.3).
with a Bartlett correction: see Bartholomew (1987, p. 46) or Lawley & Maxwell (1971, pp. 35–36). For a Heywood case (as here) Lawley & Maxwell (1971, p. 37) suggest the number of degrees of freedom should be increased by the number of variables with zero uniqueness.
This is a vector x such that original variable j was divided by xJ .
Bartholomew gives both covariance and correlation matrices, but these are inconsistent. Neither are in the original paper.
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© 1997 Springer Science+Business Media New York
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Venables, W.N., Ripley, B.D. (1997). Multivariate Analysis. In: Modern Applied Statistics with S-PLUS. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2719-7_13
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DOI: https://doi.org/10.1007/978-1-4757-2719-7_13
Publisher Name: Springer, New York, NY
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