Description
The CoxPH function is named for the Cox proportional hazards model, a
statistical survival model. The function estimates coefficients by learning
a set of explanatory variables. The output of the CoxPH function
is input to the CoxHazardRatio (td_cox_hazard_ratio_mle
)
and CoxSurvival (td_cox_survival_mle
) functions.
Usage
td_coxph_mle (
data = NULL,
feature.columns = NULL,
time.interval.column = NULL,
event.column = NULL,
threshold = 1.0E-9,
max.iter.num = 10,
categorical.columns = NULL,
accumulate = NULL,
data.sequence.column = NULL
)
Arguments
data |
Required Argument. |
feature.columns |
Required Argument. |
time.interval.column |
Required Argument. |
event.column |
Required Argument. |
threshold |
Optional Argument. |
max.iter.num |
Optional Argument. |
categorical.columns |
Optional Argument. |
accumulate |
Optional Argument. |
data.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_coxph_mle" which is a named
list containing objects of class "tbl_teradata".
Named list members can be referenced directly with the "$" operator
using following names:
coefficient.table
-
linear.predictor.table
output
Examples
# Get the current context/connection
con <- td_get_context()$connection
# Load example data.
# The input table, lungcancer, contains data from a randomized trial of two treatment
# regimens for lung cancer used to model survival analysis. There are three categorical
# predictors and three numerical predictors
loadExampleData("coxph_example", "lungcancer")
# Create object(s) of class "tbl_teradata".
lungcancer <- tbl(con, "lungcancer")
# Example 1 -
td_coxph_out <- td_coxph_mle(data = lungcancer,
feature.columns = c("trt", "celltype", "karno", "diagtime", "age",
"prior"),
time.interval.column = "time_int",
event.column = "status",
categorical.columns = c("trt", "celltype", "prior")
)