Description Usage Arguments Details Value Author(s) References See Also Examples
Principal component analysis based on different score functions
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X |
a numeric data frame or matrix with p columns. |
score |
score to be used. Can be either |
estimate |
can be |
na.action |
a function which indicates what should happen when the data contain 'NA's. Default is to fail. |
... |
further arguments passed to or from other methods. |
PCA as descriped in chapter 9 of the MNM book. Note that here ALL scatter matrices used are standardized to have trace(p). This function differs from most other PCA functions in R in that it does not center the data. The 'mvPCA' class has a print, summary, plot and predict method.
A list with class 'mvloc' containing the following components:
EigenV |
the standardized eigenvalues. |
loadings |
matrix with the corresponding loadings. |
scores |
matrix with the principal components. |
dname |
name of X. |
method |
Which shape matrix was used for the computation. |
n.obs |
number of observations used. |
p |
number of variables. |
Klaus Nordhausen
Oja, H. (2010), Multivariate Nonparametric Methods with R, Springer.
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Loading required package: ICSNP
Loading required package: mvtnorm
Loading required package: ICS
Loading required package: SpatialNP
PCA for IRIS based on Tyler's shape matrix
Standardized eigenvalues:
Comp.1 Comp.2 Comp.3 Comp.4
3.79375793 0.14506408 0.04803174 0.01314625
4 variables and 150 observations.
Importance of components:
Comp.1 Comp.2 Comp.3 Comp.4
Proportion of Variation 0.9484395 0.03626602 0.01200793 0.003286563
Cumulative Proportion 0.9484395 0.98470550 0.99671344 1.000000000
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