Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset
h2o.coxph(x, event_column, training_frame, model_id = NULL, start_column = NULL, stop_column = NULL, weights_column = NULL, offset_column = NULL, ties = c("efron", "breslow"), init = 0, lre_min = 9, iter_max = 20)
| x | (Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except event_column, start_column and stop_column are used. |
|---|---|
| event_column | The name of binary data column in the training frame indicating the occurrence of an event. |
| training_frame | Id of the training data frame. |
| model_id | Destination id for this model; auto-generated if not specified. |
| start_column | start_column |
| stop_column | stop_column |
| weights_column | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. |
| offset_column | Offset column. This will be added to the combination of columns before applying the link function. |
| ties | ties Must be one of: "efron", "breslow". Defaults to efron. |
| init | init Defaults to 0. |
| lre_min | lre_min Defaults to 9. |
| iter_max | iter_max Defaults to 20. |