``lre_min``
-----------

- Available in: CoxPH
- Hyperparameter: no

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

The ``lre_min`` option measures the relative difference of log likelihood before and after iteration of the CoxPH algorithm. When building a CoxPH model, the algorithm stops when :math:`|(logLik - newLoglik) / newLoglik| <= 1e-9`. This value defaults to 9.


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 test datasets
	heart.split <- h2o.splitFrame(data=heart, ratios=.8, seed=1234)
	train <- heart.split[[1]]
	test <- heart.split[[2]]

	# train a CoxPH model
	coxph.model <- h2o.coxph(x="age", 
	                         event_column="event", 
	                         start_column="start", 
	                         stop_column="stop", 
	                         training_frame=heart, 
	                         lre_min=9)

	# run prediction against the test dataset
	predicted <- h2o.predict(coxph.model, test)

	# view the predictions
	predicted
	           lp
	1 0.26964730
	2 0.16438761
	3 0.07569035
	4 0.27813870
	5 0.27813870
	6 0.26090368

	[34 rows x 1 column]

   .. 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)

	# set the lre_mind parameter's value
	lre_min = 9

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

	# run prediction against the test dataset
	pred = coxph.predict(test_data=test)

	# view the predictions
	pred
           lp
    ---------
    0.269501
    0.164298
    0.0756492
    0.277987
    0.277987
    0.260762
    0.260762
    0.254712
    0.347814
    0.299666

    [34 rows x 1 column]