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
coxph.h2o <- h2o.coxph(x=x, event_column=y,
                       start_column=start, stop_column=stop,
                       training_frame=heart.hex)

# view the model details
coxph.h2o
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
coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
                                           stop_column="stop",
                                           ties="breslow")

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

# view the model details
coxph