Description Usage Arguments Value Author(s) Examples

Performs a principal component analysis based on Singular Value Decomposition, on the given data matrix
and returns the result as an object of the S3 class `pca`

1 |

`X` |
a n x p data frame of n observations and p variables. |

`autoscale` |
a logical value indicating whether the variables should be autoscaled |

`exclude` |
a logical value indicating whether the first two columns should be excluded from the computation. The default is TRUE, because usually the first two columns of the dataset processed represent respectively the sample names and the class labels associated with the samples |

an S3 object of class `pca`

with the following components:

scores the scores matrix

loadings the loading matrix

variances the vector of variances explained by each PC

classes the vector of the class labels associated with the samples

features the vector with the names of the input variables

Piergiorgio Palla

1 2 | ```
data(cachexiaData)
pca_obj <- pca(cachexiaData, autoscale = TRUE, exclude = TRUE)
``` |

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