vcr.rpart.train | R Documentation |

Produces output for the purpose of constructing graphical displays such as the `classmap`

. The user first needs to train a
classification tree on the data by `rpart::rpart`

.
This then serves as an argument to `vcr.rpart.train`

.

```
vcr.rpart.train(X, y, trainfit, type = list(),
k = 5, stand = TRUE)
```

`X` |
A rectangular matrix or data frame, where the
columns (variables) may be of mixed type and
may contain |

`y` |
factor with the given class labels.
It is crucial that |

`k` |
the number of nearest neighbors used in the farness computation. |

`trainfit` |
the output of an |

`type` |
list for specifying some (or all) of the types of the
variables (columns) in |

`stand` |
whether or not to standardize numerical (interval scaled) variables by their range as in the original |

A list with components:

`X` |
The input data |

`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 |

`trainfit` |
the trainfit used to build the VCR object. |

Raymaekers J., Rousseeuw P.J.

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

`vcr.rpart.newdata`

, `classmap`

, `silplot`

, `stackedplot`

```
library(rpart)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
str(traindata); table(traindata$y)
set.seed(123) # rpart is not deterministic
rpart.out <- rpart(y ~ Pclass + Sex + SibSp +
Parch + Fare + Embarked,
data = traindata, method = 'class', model = TRUE)
y_train <- traindata[, 12]
x_train <- traindata[, -12]
mytype <- list(nominal = c("Name", "Sex", "Ticket", "Cabin", "Embarked"), ordratio = c("Pclass"))
# These are 5 nominal columns, and one ordinal.
# The variables not listed are by default interval-scaled.
vcrtrain <- vcr.rpart.train(x_train, y_train, rpart.out, mytype)
confmat.vcr(vcrtrain)
silplot(vcrtrain, classCols = c(2, 4))
classmap(vcrtrain, "casualty", classCols = c(2, 4))
classmap(vcrtrain, "survived", classCols = c(2, 4))
# For more examples, we refer to the vignette:
## Not run:
vignette("Rpart_examples")
## End(Not run)
```

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