plsda | R Documentation |

Perform a PLS discriminant analysis

plsda(X, Y, nc, scale = TRUE, center = TRUE, cv = TRUE, nr_folds = 5)

`X` |
a matrix of predictor variables. |

`Y` |
a single vector indicate the group |

`nc` |
the number of pls components (the one joint components + number of orthogonal components ). |

`scale` |
logical indicating whether |

`center` |
logical indicating whether |

`cv` |
logical indicating whether cross-validation will be performed or not (suggest TRUE). |

`nr_folds` |
nr_folds Integer to indicate the folds for cross validation. |

a list containing the following elements:

`nc`

the number of components used(one joint components + number of orthogonal components`scores`

a matrix of scores corresponding to the observations in`X`

, The components retrieved correspond to the ones optimized or specified.`Xloadings`

a matrix of loadings corresponding to the explanatory variables. The components retrieved correspond to the ones optimized or specified.`vip`

the VIP matrix.`xvar`

variance explained of X by each single component.`R2Y`

variance explained of Y by each single component.codePRESSThe residual sum of squares for the samples which were not used to fit the model

codeQ2quality of cross-validation

Kai Guo

X <- matrix(rnorm(500),10,50) Y <- rep(c("a","b"),each=5) fit <- plsda(X,Y,2)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.