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

varimpPred calculates Variable Importance and makes predictions, it returns a list containing a data frame of variable importance scores, predictions or class probabilities, and corresponding plots.

1 2 3 4 5 6 7 8 9 10 |

`newdata` |
object of class "data.frame" having test data. |

`y` |
character. Target variable. |

`positive` |
character. The positive class for the target variable if y is factor. Usually, it is the first level of the factor. |

`model` |
expression. The model object returned after training a model on training data. |

`scale` |
boolean. If |

`auc` |
boolean. If |

`predict` |
boolean. If |

`...` |
additional arguments to be passed to |

The importance measure for each variable is calculated based on the type of model.

For example for linear models, the absolute value of the t-statistic of each parameter is used in the importance calculation.

For classification models, with the exception of classification trees, bagged trees and boosted trees, a variable importance score is calculated for each class. See `varImp`

for details on model-specific metrics.

`varimpPred`

can be used to obtain either variable importance metrics, predictions, class probabilities, or a combination of these.

For classification models with `predict = TRUE`

, class probabilities and ROC curve are given in the results.

For regression models with `predict = TRUE`

, predictions and residuals versus predicted plot are given.

A list object with importance measures for variables in `newdata`

, predictions for regression models, class probabilities for classification models, and corresponding plots.

`newdata`

should be either the test data that remains after splitting whole data into training and test sets, or a new data set different from the one used to train the model.

If `y`

is factor, class probabilities are calculated for each class. If `y`

is numeric, predicted values are calculated.

A ROC curve is created if `predict = TRUE`

and `y`

is factor. Otherwise, a plot of residuals versus predicted values is created if `y`

is numeric.

`varimpPred`

relies on packages `caret`

, `ggplot2`

and `plotROC`

to perform the calculations and plotting.

Zakaria Kehel, Bancy Ngatia, Khadija Aziz, Zainab Azough

`varImp`

,
`predict.train`

,
`ggplot`

,
`geom_roc`

,
`calc_auc`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
if(interactive()){
# Calculate variable importance for classification model
data("septoriaDurumWC")
knn.mod <- tuneTrain(data = septoriaDurumWC,y = 'ST_S',method = 'knn')
testdata <- knn.mod$`Test Data`
knn.varimp<- varimpPred(newdata = testdata, y='ST_S', positive = 'R', model = knn.mod$Model)
knn.varimp
# Calculate variable importance and obtain class probabilities
data("septoriaDurumWC")
svm.mod <- tuneTrain(data = septoriaDurumWC, y = 'ST_S',method = 'svmLinear2',
predict = TRUE, positive = 'R',summary = twoClassSummary)
testdata <- svm.mod$`Test Data`
svm.varimp <- varimpPred(newdata = testdata, y = 'ST_S',
positive = 'R', model = svm.mod$Model,
ROC = TRUE, predict = TRUE)
svm.varimp
# Obtain variable importance plot for only first 20 variables
# with highest measure
svm.varimp <- varimpPred(newdata = testdata, y = 'ST_S',
positive = 'R', model = svm.mod$Model,
ROC = TRUE, predict = TRUE, top = 20)
svm.varimp
}
``` |

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