Teradata R Package Function Reference - CoxPH - Teradata R Package - Look here for syntax, methods and examples for the functions included in the Teradata R Package.

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 td_coxph_mle function is the input to the function CoxHazardRatio (td_cox_hazard_ratio_mle) and CoxSurvival (td_cox_survival_mle).

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. Specifies the name of the tbl_teradata object that contains the input parameters.
`feature.columns`	Required Argument. Specifies the names of the columns from the input table used in the data argument that contain the features of the input parameters.
`time.interval.column`	Required Argument. Specifies the name of the column from input table used in data argument that contains the time intervals of the input parameters; that is, end_time - start_time, in any unit of time (for example, years, months, or days).
`event.column`	Required Argument. Specifies the name of the column from the input table used in the data argument that contains 1 if the event occurred by end_time and 0 if it did not. (0 represents survival or right-censorship.) The function ignores values other than 1 and 0.
`threshold`	Optional Argument. Specifies the convergence threshold. Default Value: 1.0E-9
`max.iter.num`	Optional Argument. Specifies the maximum number of iterations that the function runs before finishing, if the convergence threshold has not been met. Default Value: 10
`categorical.columns`	Optional Argument. Specifies the names of the columns from the input table used in the data argument that contain categorical predictors. Each categorical column must also be a feature column. By default, the function detects the categorical columns by their SQL data types.
`accumulate`	Optional Argument. Specifies the names of the columns from the input table used in the data argument that the function copies to linear.predictor.table.
`data.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "data". The argument is used to ensure deterministic results for functions which produce results that vary from run to run.

Value

Function returns an object of class "td_coxph_mle" which is a named list containing Teradata tbl objects.
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 remote tibble objects.
    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")
                             )