summary.BTFit {BT} | R Documentation |

## Summary of a BTFit object.

### Description

Computes the relative influence of each variable in the BTFit object.

### Usage

```
## S3 method for class 'BTFit'
summary(
object,
cBars = length(object$var.names),
n.iter = object$BTParams$n.iter,
plot_it = TRUE,
order_it = TRUE,
method = .BT_relative_influence,
normalize = TRUE,
...
)
```

### Arguments

`object` |
a |

`cBars` |
the number of bars to plot. If |

`n.iter` |
the number of trees used to compute the relative influence. Only the first |

`plot_it` |
an indicator as to whether the plot is generated. |

`order_it` |
an indicator as to whether the plotted and/or returned relative influences are sorted. |

`method` |
the function used to compute the relative influence. Currently, only |

`normalize` |
if |

`...` |
additional argument passed to the plot function. |

### Details

Please note that the relative influence for variables having an original **negative** relative influence is forced to 0.

### Value

Returns a data frame where the first component is the variable name and the second one is the computed relative influence, normalized to sum up to 100.
Depending on the `plot_it`

value, the relative influence plot will be performed.

### Author(s)

Gireg Willame gireg.willame@gmail.com

*This package is inspired by the gbm3 package. For more details, see https://github.com/gbm-developers/gbm3/*.

### References

M. Denuit, D. Hainaut and J. Trufin (2019). **Effective Statistical Learning Methods for Actuaries |: GLMs and Extensions**, *Springer Actuarial*.

M. Denuit, D. Hainaut and J. Trufin (2019). **Effective Statistical Learning Methods for Actuaries ||: Tree-Based Methods and Extensions**, *Springer Actuarial*.

M. Denuit, D. Hainaut and J. Trufin (2019). **Effective Statistical Learning Methods for Actuaries |||: Neural Networks and Extensions**, *Springer Actuarial*.

M. Denuit, D. Hainaut and J. Trufin (2022). **Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link**.
Accepted for publication in *Scandinavian Actuarial Journal*.

M. Denuit, J. Huyghe and J. Trufin (2022). **Boosting cost-complexity pruned trees on Tweedie responses: The ABT machine for insurance ratemaking**.
Paper submitted for publication.

M. Denuit, J. Trufin and T. Verdebout (2022). **Boosting on the responses with Tweedie loss functions**. Paper submitted for publication.

### See Also

*BT*version 0.4 Index]