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"
)