nested.glm | R Documentation |

Run linear or logistic regression on a set of cross-validation folds. This can be used to establish a baseline model, often built only on the initial set of covariates.

nested.glm(formula, data, family, folds, store.glm = FALSE)

`formula` |
An object of class |

`data` |
Data frame or matrix containing outcome variable and predictors. |

`family` |
Type of model fitted: either |

`folds` |
List of cross-validation folds, where each element contains the indices of the observations to be withdrawn in that fold. |

`store.glm` |
Whether the object produced by |

An object of class `nestglm`

of length equal to `length(folds)`

,
where each entry contains the following fields:

`summary` |
Summary of the coefficients of the model fitted on the training observations. |

`family` |
Type of model fitted. |

`fit` |
Predicted values for the withdrawn observations. |

`obs` |
Observed values for the withdrawn observations. |

`test.llk` |
Test log-likelihood. |

`test.idx` |
Indices of the the withdrawn observations for this fold. |

`regr` |
Object created by |

`nested.performance()`

.

data(diabetes) folds <- create.folds(10, nrow(diabetes), seed=1) res <- nested.glm(Y ~ age + sex + bmi + map, diabetes, gaussian(), folds)

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