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
)

## Arguments

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. The name of binary data column in the training frame indicating the occurrence of an event. Id of the training data frame. Destination id for this model; auto-generated if not specified. Start Time Column. Stop Time 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. This will be added to the combination of columns before applying the link function. List of columns to use for stratification. Method for Handling Ties. Must be one of: "efron", "breslow". Defaults to efron. Coefficient starting value. Defaults to 0. Minimum log-relative error. Defaults to 9. Maximum number of iterations. Defaults to 20. A list of predictor column indices to interact. All pairwise combinations will be computed for the list. A list of pairwise (first order) column interactions. A list of columns that should only be used to create interactions but should not itself participate in model training. Logical. (Internal. For development only!) Indicates whether to use all factor levels. Defaults to FALSE. Automatically export generated models to this directory. Logical. Run on a single node to reduce the effect of network overhead (for smaller datasets) Defaults to FALSE.

## Examples

# NOT RUN {
library(h2o)
h2o.init()

# Import the heart dataset
f <- "http://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")
# }