init
(CoxPH)¶
Available in: CoxPH
Hyperparameter: no
Description¶
When building a CoxPH model, the init
option specifies the initial value, \(\beta^{(0)}\), for the coefficient vector. This value defaults to 0.
Example¶
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 = 0.8, 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 tes = 6109 on 1 df, p =< 2e-16
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")
# 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 >