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Create `Tobit`

model object for loss given
default

Create and analyze a `Tobit`

model object to calculate
loss given default (LGD) using this workflow:

Use

`fitLGDModel`

to create a`Tobit`

model object.Use

`predict`

to predict the LGD.Use

`modelDiscrimination`

to return AUROC and ROC data. You can plot the results using`modelDiscriminationPlot`

.Use

`modelAccuracy`

to return the R-squared, RMSE, correlation, and sample mean error of predicted and observed LGD data. You can plot the results using`modelAccuracyPlot`

.

specifies options using one or more name-value pair arguments in
addition to the input arguments in the previous syntax. The optional
name-value pair arguments set the model object properties. For example,
`TobitLGDModel`

= fitLGDModel(___,`Name,Value`

)```
lgdModel = fitLGDModel(data,'tobit','PredictorVars',{'LTV'
'Age'
'Type'},'ResponseVar','LGD','CensoringSide','left','LeftLimit',1e-4)
```

creates a `Tobit`

model object.

`predict` | Predict loss given default |

`modelDiscrimination` | Compute AUROC and ROC data |

`modelDiscriminationPlot` | Plot ROC curve |

`modelAccuracy` | Compute R-square, RMSE, correlation, and sample mean error of predicted and observed LGDs |

`modelAccuracyPlot` | Scatter plot of predicted and observed LGDs |

[1] Baesens, Bart, Daniel
Roesch, and Harald Scheule. *Credit Risk Analytics: Measurement
Techniques, Applications, and Examples in SAS.* Wiley,
2016.

[2] Bellini, Tiziano.
*IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical
Guide with Examples Worked in R and SAS.* San Diego, CA: Elsevier,
2019.