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. 

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; autogenerated 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 perrow observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but noninteger values are supported as well. During training, rows with higher weights matter more, due to the larger loss function prefactor. 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 logrelative 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 

export_checkpoints_dir  Automatically export generated models to this directory. 
single_node_mode 

if (FALSE) { library(h2o) h2o.init() # Import the heart dataset f < "https://s3.amazonaws.com/h2opublictestdata/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") }