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

Description

The GLMPredict function uses the model output by the function GLM to perform generalized linear model prediction on new input data.
Note: This function is only available when tdplyr is connected to Vantage 1.1 or later versions.

Usage

td_glm_predict_mle (
      modeldata = NULL,
      newdata = NULL,
      terms = NULL,
      family = NULL,
      linkfunction = "CANONICAL",
      output.response.probdist = FALSE,
      output.responses = NULL,
      newdata.sequence.column = NULL,
      modeldata.sequence.column = NULL,
      newdata.order.column = NULL,
      modeldata.order.column = NULL
  )

Arguments

`modeldata`	Optional Argument. Specifies the tbl_teradata containing the GLM model.
`modeldata.order.column`	Optional Argument. Specifies Order By columns for modeldata. Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
`newdata`	Required Argument. Specifies the tbl_teradata containing the input data.
`newdata.order.column`	Optional Argument. Specifies Order By columns for newdata. Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
`terms`	Optional Argument. Specifies the names of input tbl_teradata columns to copy to the output table. Types: character OR vector of Strings (character)
`family`	Optional Argument. Specifies the distribution exponential family. The default value is read from model table. If you specify this argument, you must give it the same value that you used for the 'family' argument of the function when you generated the model table. Permitted Values: LOGISTIC, BINOMIAL, POISSON, GAUSSIAN, GAMMA, INVERSE_GAUSSIAN, NEGATIVE_BINOMIAL Types: character
`linkfunction`	Optional Argument. Specifies the canonical link functions (default link functions) and the link functions that are allowed for each exponential family. Note: Use the same value that you used for the "linkfunction"" argument of the function when you generated the model table. Default Value: "CANONICAL" Permitted Values: CANONICAL, IDENTITY, INVERSE, LOG, COMPLEMENTARY_LOG_LOG, SQUARE_ROOT, INVERSE_MU_SQUARED, LOGIT, PROBIT, CAUCHIT Types: character
`output.response.probdist`	Optional Argument. Specifies whether to output probabilities. Note: "output.response.probdist" argument support is only available when tdplyr is connected to Vantage 1.1.1 or later versions. Default Value: FALSE Types: logical
`output.responses`	Optional Argument. Specifies responses to output probability. This argument can only be used when output.response.probdist is set to TRUE. Note: "output.responses" argument support is only available when tdplyr is connected to Vantage 1.1.1 or later versions. Permitted Values: 0, 1 Types: character OR vector of characters
`newdata.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "newdata". 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)
`modeldata.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "modeldata". 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)

Value

Function returns an object of class "td_glm_predict_mle" which is a named list containing Teradata tbl object.
Named list member can be referenced directly with the "$" operator using name: result.

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("glm_example", "admissions_train", "housing_train")
    loadExampleData("glmpredict_example", "admissions_test", "housing_test")
    
    # Create remote tibble objects.
    admissions_test <- tbl(con, "admissions_test")
    admissions_train <- tbl(con, "admissions_train")
    housing_test <- tbl(con, "housing_test")
    housing_train <- tbl(con, "housing_train")
    
    # Example 1 -
    # Generate a model based on train data "admissions_train".
    td_glm_out <- td_glm_mle(formula = (admitted ~ stats + masters + gpa + programming),
                         family = "LOGISTIC",
                         linkfunction = "LOGIT",
                         data = admissions_train,
                         weights = "1",
                         threshold = 0.01,
                         maxit = 25,
                         step = FALSE,
                         intercept = TRUE
                         )
    
    # Use the generated model to predict the 'admissions' on the test data
    # admissions_test by using model generated by GLM.
    td_glm_predict_out1 <- td_glm_predict_mle(modeldata = td_glm_out,
                             newdata = admissions_test,
                             terms = c("id","masters","gpa","stats","programming","admitted"),
                             family = "LOGISTIC",
                             linkfunction = "LOGIT",
                             output.response.probdist = TRUE
                             )
    
    # Example 2 -
    # Generate a model based on train data "housing_train".
    td_glm_out_hs <- td_glm_mle(formula = (price  ~ recroom  + lotsize  + stories  + garagepl +                                               gashw + bedrooms  + driveway  + airco  + homestyle +                                                bathrms  + fullbase + prefarea),
                                family = "GAUSSIAN",
                                linkfunction = "IDENTITY",
                                data = housing_train,
                                weights = "1",
                                threshold = 0.01,
                                maxit = 25,
                                step = FALSE,
                                intercept = TRUE
                                )
                                
    # Use the generated model to predict the 'price' on the test data
    # housing_test by using model generated by GLM.
    td_glm_predict_out2 <- td_glm_predict_mle(modeldata = td_glm_out_hs$coefficients,
                             newdata = housing_test,
                             terms = c("sn", "price"),
                             family = "GAUSSIAN",
                             linkfunction = "CANONICAL"
                             )