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Scilab help >> Statistics > show_pca

# show_pca

Visualization of principal components analysis results

### Calling Sequence

`show_pca(lambda,facpr,N)`

### Arguments

lambda

is a p x 2 numerical matrix. In the first column we find the eigenvalues of V, where V is the correlation p x p matrix and in the second column are the ratios of the corresponding eigenvalue over the sum of eigenvalues.

facpr

are the principal factors: eigenvectors of V. Each column is an eigenvector element of the dual of `R^p`.

N

Is a 2x1 integer vector. Its coefficients point to the eigenvectors corresponding to the eigenvalues of the correlation matrix `p` by `p` ordered by decreasing values of eigenvalues. If `N`. is missing, we suppose `N=[1 2]`..

### Description

This function visualize the pca results.

### Examples

```a=rand(100,10,'n');
[lambda,facpr,comprinc] = pca(a);
show_pca(lambda,facpr)```

### See Also

• pca — Computes principal components analysis with standardized variables
• princomp — Principal components analysis

Carlos Klimann

### Bibliography

Saporta, Gilbert, Probabilites, Analyse des Donnees et Statistique, Editions Technip, Paris, 1990.

### Comments

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