start_column

• Available in: CoxPH

• Hyperparameter: no

Description

This option is used to specify the name of an integer column in the source data set representing the start time. If supplied, then the value of the start_column must be strictly less than the stop_column in each row.

Related Parameters

• stop_column

Example

R

Python

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

# set the predictor name and response column
x <- "age"
y <- "event"

# set the start and stop columns
start <- "start"
stop <- "stop"

# train your model
heart_coxph <- h2o.coxph(x = x, event_column = y,
                         start_column = start, stop_column = stop,
                         training_frame = heart)

# view the model details
heart_coxph
Model Details:
==============

H2OCoxPHModel: coxph
Model ID:  CoxPH_model_R_1527700369755_2
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 = 5.17  on 1 df, p = 0.023
n = 172, number of events = 75

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")

# set the parameters
heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
                                                 stop_column="stop",
                                                 ties="breslow")

# train your model
heart_coxph.train(x="age", y="event", training_frame=heart)

# view the model details
heart_coxph