Last updated: Feb 11, 2025
The Cox regression node enables you to build a survival model for time-to-event
data in the presence of censored records. The model produces a survival function that predicts the
probability that the event of interest has occurred at a given time (t) for given values of
the input variables.
Example
node = stream.create("coxreg", "My node")
node.setPropertyValue("survival_time", "tenure")
node.setPropertyValue("method", "BackwardsStepwise")
# Expert tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("removal_criterion", "Conditional")
node.setPropertyValue("survival", True)
coxregnode Properties |
Values | Property description |
|---|---|---|
survival_time
|
field | Cox regression models require a single field containing the survival times. |
target
|
field | Cox regression models require a single target field, and one or more input fields. See Common modeling node properties for more information. |
method
|
Enter
Stepwise
BackwardsStepwise
|
|
groups
|
field | |
model_type
|
MainEffects
Custom
|
|
custom_terms
|
["BP*Sex" "BP*Age"] | |
mode
|
Expert
Simple
|
|
max_iterations
|
number | |
p_converge
|
1.0E-4
1.0E-5
1.0E-6
1.0E-7
1.0E-8
0
|
|
l_converge
|
1.0E-1
1.0E-2
1.0E-3
1.0E-4
1.0E-5
0
|
|
removal_criterion
|
LR
Wald
Conditional
|
|
probability_entry
|
number | |
probability_removal
|
number | |
output_display
|
EachStep
LastStep
|
|
ci_enable
|
flag | |
ci_value
|
90
95
99
|
|
correlation
|
flag | |
display_baseline
|
flag | |
survival
|
flag | |
hazard
|
flag | |
log_minus_log
|
flag | |
one_minus_survival
|
flag | |
separate_line
|
field | |
value
|
number or string | If no value is specified for a field, the default option "Mean" will be used for that field. |