### 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` |
Required Argument. |

`modeldata.order.column` |
Optional Argument. |

`newdata` |
Required Argument. |

`newdata.order.column` |
Optional Argument. |

`terms` |
Optional Argument. |

`family` |
Optional Argument. |

`linkfunction` |
Optional Argument. |

`output.response.probdist` |
Optional Argument. |

`output.responses` |
Optional Argument. |

`newdata.sequence.column` |
Optional Argument. |

`modeldata.sequence.column` |
Optional Argument. |

### Value

Function returns an object of class "td_glm_predict_mle" which is a
named list containing object of class "tbl_teradata".

Named list member can be referenced directly with the "$" operator
using name: result.

### Examples

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