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, stratify_by = NULL, ties = c("efron", "breslow"), init = 0, lre_min = 9, max_iterations = 20, interactions = NULL, interaction_pairs = NULL, interactions_only = NULL, use_all_factor_levels = FALSE, export_checkpoints_dir = NULL, single_node_mode = FALSE )
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. |
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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 Time Column. |
stop_column | Stop Time 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. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
offset_column | Offset column. This will be added to the combination of columns before applying the link function. |
stratify_by | List of columns to use for stratification. |
ties | Method for Handling Ties. Must be one of: "efron", "breslow". Defaults to efron. |
init | Coefficient starting value. Defaults to 0. |
lre_min | Minimum log-relative error. Defaults to 9. |
max_iterations | Maximum number of iterations. Defaults to 20. |
interactions | A list of predictor column indices to interact. All pairwise combinations will be computed for the list. |
interaction_pairs | A list of pairwise (first order) column interactions. |
interactions_only | A list of columns that should only be used to create interactions but should not itself participate in model training. |
use_all_factor_levels |
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export_checkpoints_dir | Automatically export generated models to this directory. |
single_node_mode |
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# NOT RUN { library(h2o) h2o.init() # Import the heart dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) # Set the predictor and response predictor <- "age" response <- "event" # Train a Cox Proportional Hazards model heart_coxph <- h2o.coxph(x = predictor, training_frame = heart, event_column = "event", start_column = "start", stop_column = "stop") # }