Teradata R Package Function Reference | 17.00 - 17.00 - GLML1L2 - Teradata R Package

Teradata® R Package Function Reference

Teradata R Package
September 2020
Programming Reference


The GLML1L2 function differs from the GLM function in these ways:

  1. GLML1L2 supports the regularization models Ridge, LASSO, and Elastic Net.

  2. GLML1L2 outputs a model tbl_teradata and, optionally, a factor tbl_teradata (GLM outputs only a model).


  td_glml1l2_mle (
      formula = NULL,
      data = NULL,
      alpha = 0.0,
      lambda = 0,
      max.iter.num = 10000,
      stop.threshold = 1.0E-7,
      family = "Gaussian",
      randomization = FALSE,
      data.sequence.column = NULL



Required Argument.
An object of class "formula". Specifies the model to be fitted. Only basic formula of the (col1 ~ col2 + col3 +...) form are supported and all variables must be from the same tbl_teradata object. The response should be a column of type numeric or logical.


Required Argument.
Specifies the name of the tbl_teradata that contains the input data.


Optional Argument.
Specifies whether to use Lasso, Ridge or Elastic Net. If the value is 0, Ridge is used. If the value is 1, Lasso is used. For any value between 0 and 1, Elastic Net is applied.
Default Value: 0.0
Types: numeric


Optional Argument.
Specifies the parameter that controls the magnitude of the regularization term. The value of "lambda" must be in the range [0.0, 100.0]. A value of zero disables regularization.
Default Value: 0
Types: numeric


Optional Argument.
Specifies the maximum number of iterations over the data. The parameter "max.iter.num" must be a positive numeric value in the range [1, 100000].
Default Value: 10000
Types: integer


Optional Argument.
Specifies the convergence threshold.
Default Value: 1.0E-7
Types: numeric


Optional Argument.
Specifies the distribution exponential family.
Default Value: "Gaussian"
Permitted Values: Binomial, Gaussian
Types: character


Optional Argument.
Specifies whether to randomize the input tbl_teradata data.
Default Value: FALSE
Types: logical


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.
Types: character OR vector of Strings (character)


Function returns an object of class "td_glml1l2_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:

  1. factor.data

  2. output


  1. When argument randomization is TRUE or if any categorical columns are provided in formula argument, then and only then the output tbl_teradata object factor.data is created.

  2. factor.data can be used as the input (data) for future GLML1L2 function calls, thereby saving the function from repeating the categorical-to-numerical conversion or randomization.


    # Get the current context/connection
    con <- td_get_context()$connection
    # Load example data.
    loadExampleData("glml1l2_example", "admissions_train", "housing_train")

    # Create object(s) of class "tbl_teradata".
    admissions_train <- tbl(con, "admissions_train")
    housing_train <- tbl(con, "housing_train")

    # Example 1 -
    # Ridge Regression, family = 'Binomial'.
    # Because the response variable is binary (the admitted column has
    # two possible values), the call specifies family = 'Binomial'.
    # alpha = 0 indicates L2 (ridge regression) regularization.
    td_glml1l2_mle_out1 <- td_glml1l2_mle(formula = (admitted ~ stats + gpa + masters
                                                     + programming),
                                          data = admissions_train,
                                          alpha = 0,
                                          lambda = 0.02,
                                          family = "Binomial",
                                          randomization = TRUE

    # Example 2 -
    # factor.data (from Example 1) as input data.
    # Because Randomization was TRUE in the function call that created
    # the factor.data input, this call does not need it.
    td_glml1l2_mle_out2 <- td_glml1l2_mle(formula = (admitted ~ masters_yes + stats_novice
                                                     + programming_novice + stats_beginner
                                                     + programming_beginner + gpa),
                                          data = td_glml1l2_mle_out1$factor.data,
                                          alpha = 0,
                                          lambda = 0.02,
                                          family = "Binomial"

    # Example 3 -
    # LASSO, family = 'Gaussian'.
    # Because the response variable has a Gaussian distribution, the call specifies
    # family = 'Gaussian'.
    # alpha = 1 indicates L1 (LASSO) regularization.
    td_glml1l2_mle_out3 <- td_glml1l2_mle(formula = (price ~ lotsize + bedrooms + gashw + driveway
                                                     + stories + recroom + garagepl + bathrms
                                                     + homestyle + fullbase + airco + prefarea),
                                          data = housing_train,
                                          alpha = 1,
                                          lambda = 0.02,
                                          family = "Gaussian",
                                          randomization = TRUE