### Description

The GLMPredict function uses the model generated by the GLM `td_glm_mle`

function to perform generalized linear model prediction
on new input data.

### Usage

td_glm_predict_sqle ( modeldata = NULL, newdata = NULL, terms = NULL, family = NULL, linkfunction = "CANONICAL", newdata.order.column = NULL ) ## S3 method for class 'td_glm_mle' predict( modeldata = NULL, newdata = NULL, terms = NULL, family = NULL, linkfunction = "CANONICAL", newdata.order.column = NULL)

### Arguments

`modeldata` |
Required Argument. |

`newdata` |
Required Argument. |

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

`terms` |
Optional Argument. |

`family` |
Optional Argument. |

`linkfunction` |
Optional Argument. |

### Value

Function returns an object of class "td_glm_predict_sqle" 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()$connection # Load example data. loadExampleData("glm_example", "admissions_train", "housing_train") loadExampleData("glmpredict_example", "admissions_test", "housing_test") # Create object(s) of class "tbl_teradata". admissions_test <- tbl(con, "admissions_test") admissions_train <- tbl(con, "admissions_train") housing_test <- tbl(con, "housing_test") housing_train <- tbl(con, "housing_train") # Example 1 - # First train the data, i.e., create a GLM Model td_glm_out <- td_glm_mle(formula = (admitted ~ stats + masters + gpa + programming), family = "LOGISTIC", linkfunction = "LOGIT", data = admissions_train, weights = "1", threshold = 0.01, maxit = 25, step = FALSE, intercept = TRUE ) # Run predict on the output of td_glm_mle() function. td_glm_predict_out1 <- td_glm_predict_sqle(modeldata = td_glm_out, newdata = admissions_test, terms = c("id","masters","gpa","stats","programming","admitted"), family = "LOGISTIC", linkfunction = "LOGIT" ) # Example 2 - # First train the data, i.e., create a GLM Model td_glm_out_hs <- td_glm_mle(formula = (price ~ recroom + lotsize + stories + garagepl + gashw + bedrooms + driveway + airco + homestyle + bathrms + fullbase + prefarea), family = "GAUSSIAN", linkfunction = "IDENTITY", data = housing_train, weights = "1", threshold = 0.01, maxit = 25, step = FALSE, intercept = TRUE ) # Run predict on the output of td_glm_mle() function by passing coefficients. td_glm_predict_out2 <- td_glm_predict_sqle(modeldata = td_glm_out_hs$coefficients, newdata = housing_test, terms = c("sn", "price"), family = "GAUSSIAN", linkfunction = "CANONICAL" ) # Alternatively use S3 predict method to find predictions. td_glm_predict_out3 <- predict(td_glm_out_hs, newdata = housing_test, terms = c("sn", "price"), family = "GAUSSIAN", linkfunction = "CANONICAL" )