Description Usage Arguments Value Author(s) References See Also Examples

Custom DA function which prepares for graphical displays such as the `classmap`

. The disciminant analysis itself is carried out by the maximum a posteriori rule, which maximizes the density of the mixture.

1 | ```
vcr.da.train(X, y, rule = "QDA", estmethod = "meancov")
``` |

`X` |
a numerical matrix containing the predictors in its columns. Missing values are not allowed. |

`y` |
a factor with the given class labels. |

`rule` |
either " |

`estmethod` |
function for location and covariance estimation.
Should return a list with the center |

A list with components:

`yint` |
number of the given class of each case. Can contain |

`y` |
given class label of each case. Can contain |

`levels` |
levels of |

`predint` |
predicted class number of each case. For each case this is the class with the highest posterior probability. Always exists. |

`pred` |
predicted label of each case. |

`altint` |
number of the alternative class. Among the classes different from the given class, it is the one with the highest posterior probability. Is |

`altlab` |
label of the alternative class. Is |

`PAC` |
probability of the alternative class. Is |

`figparams` |
parameters for computing |

`fig` |
distance of each case |

`farness` |
farness of each case from its given class. Is |

`ofarness` |
for each case |

`classMS` |
list with center and covariance matrix of each class |

`lCurrent` |
log of mixture density of each case in its given class. Is |

`lPred` |
log of mixture density of each case in its predicted class. Always exists. |

`lAlt` |
log of mixture density of each case in its alternative class. Is |

Raymaekers J., Rousseeuw P.J.

Raymaekers J., Rousseeuw P.J., Hubert M. (2021). Class maps for visualizing classification results. *Technometrics*, appeared online. doi: 10.1080/00401706.2021.1927849(link to open access pdf)

`vcr.da.newdata`

, `classmap`

, `silplot`

, `stackedplot`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
data("data_floralbuds")
X <- data_floralbuds[, 1:6]; y <- data_floralbuds[, 7]
vcrout <- vcr.da.train(X, y, rule = "QDA")
# For linear discriminant analysis, put rule = "LDA".
confmat.vcr(vcrout) # There are a few outliers
cols <- c("saddlebrown", "orange", "olivedrab4", "royalblue3")
stackedplot(vcrout, classCols = cols)
classmap(vcrout, "bud", classCols = cols)
# For more examples, we refer to the vignette:
## Not run:
vignette("Discriminant_analysis_examples")
## End(Not run)
``` |

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