``init`` (CoxPH)
----------------

- Available in: CoxPH
- Hyperparameter: no

Description
~~~~~~~~~~~

When building a CoxPH model, the ``init`` option specifies the initial value, :math:`\beta^{(0)}`, for the coefficient vector. This value defaults to 0.


Related Parameters
~~~~~~~~~~~~~~~~~~

- None

Example
~~~~~~~

.. example-code::
   .. code-block:: r

	library(h2o)
	h2o.init()

	# import the heart dataset
	heart <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

	# split the dataset into train and validation datasets
	heart.split <- h2o.splitFrame(data=heart, ratios=.8, seed=1234)
	train <- heart.split[[1]]
	test <- heart.split[[2]]

	# train your model
	coxph.model <- h2o.coxph(x="age", 
	                         event_column="event", 
	                         start_column="start", 
	                         stop_column="stop", 
	                         training_frame=heart, 
	                         init=3)

	# view the model details
	coxph.model
	Loading required namespace: survival
	Model Details:
	==============

	H2OCoxPHModel: coxph
	Model ID:  CoxPH_model_R_1570809926481_1 
	Call:
	Surv(start, stop, event) ~ age

	      coef exp(coef) se(coef)    z     p
	age 0.0307    1.0312   0.0143 2.15 0.031

	Likelihood ratio test=6109  on 1 df, p=<2e-16
	n= 172, number of events= 75 

   .. code-block:: python

	import h2o
	from h2o.estimators.coxph import H2OCoxProportionalHazardsEstimator
	h2o.init()

	# import the heart dataset
	heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

	# split the dataset into train and test datasets
	train, test = heart.split_frame(ratios = [.8], seed=1234)

	#specify the init parameter's value
	init = 3

	# initialize an train a CoxPH model
	coxph = H2OCoxProportionalHazardsEstimator(start_column="start", 
	                                           stop_column="stop", 
	                                           ties="breslow", 
	                                           init=init)
	coxph.train(x="age", y="event", training_frame=heart)

	# view the model details
	coxph.train
	Model Details
	=============
	H2OCoxProportionalHazardsEstimator :  Cox Proportional Hazards
	Model Key:  CoxPH_model_python_1570808496252_2

	Call: 
	Surv(start, stop, event) ~ age

	Coefficients: CoxPH Coefficients
	names    coefficients    exp_coef    exp_neg_coef    se_coef    z_coef
	-------  --------------  ----------  --------------  ---------  --------
	age      0.030691        1.03117     0.969775        0.0142686  2.15095

	Likelihood ratio test=5.160759
	n=172, number of events=75

	Scoring History: 
	    timestamp            duration    iterations    loglik
	--  -------------------  ----------  ------------  --------
	    2019-10-11 08:59:31  0.000 sec   0             -298.326
	    2019-10-11 08:59:31  0.001 sec   1             -295.799
	    2019-10-11 08:59:31  0.002 sec   2             -295.745
	    2019-10-11 08:59:31  0.004 sec   3             -295.745
	<bound method H2OEstimator.train of >