GLMPredict
Description
The td_glm_predict_mle_sqle()
function uses the model generated by the
function td_glm_mle()
to perform generalized
linear model prediction on new input data.
Usage
td_glm_predict_mle_sqle (
modeldata = NULL,
newdata = NULL,
terms = NULL,
family = NULL,
linkfunction = "CANONICAL",
output.prob = FALSE,
output.responses = NULL,
...
)
Arguments
modeldata |
Required Argument. |
newdata |
Required Argument. |
terms |
Optional Argument. |
family |
Optional Argument. |
linkfunction |
Optional Argument. |
output.prob |
Optional Argument. |
output.responses |
Optional Argument. |
... |
Specifies the generic keyword arguments SQLE functions accept. volatile: Function allows the user to partition, hash, order or local order the input data. These generic arguments are available for each argument that accepts tbl_teradata as input and can be accessed as:
Note: |
Value
Function returns an object of class "td_glm_predict_mle_sqle"
which is a named list containing object of class "tbl_teradata".
Named list member(s) can be referenced directly with the "$" operator
using the name(s):result
Examples
# Get the current context/connection.
con <- td_get_context()$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_mle_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_mle_sqle(
modeldata = td_glm_out_hs$coefficients,
newdata = housing_test,
terms = c("sn", "price"),
family = "GAUSSIAN",
linkfunction = "CANONICAL"
)