Description Usage Arguments Note Author(s) Examples

Fit a linear model using gradient descent methods. Given a loss function, gdlm will minimize summed loss across observations. Optionally generate bootstrapped standard errors & confidence intervals for estimators.

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`formula` |
the form of the linear model to be fit |

`data` |
the dataset used to fit the model |

`loss` |
a function accepting a vector of parameter estimators, a matrix of training data, and a vector of responses. represents the loss at the current model parameterization |

`bootstrapped_se` |
whether to perform bootstrapping to generate standard errors and confidence intervals |

`initial_estimators` |
the starting fit used by gradient descent. default is zeros |

`boostrap_trials` |
how many bootstrap trials to perform |

It is left to the user to consider the convexity of the summed loss function. For non-convex loss, gradient descent methods may not be generally appropriate.

The user can control the parallelism of bootstrapping by setting

The loss and gradient arguments are currently mutually exclusive, although some gradient descent methods can take advantage of them simultaneously. This may be subject to change in the future.

kholub

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