Code
expr1 %>% update(mixture = 0)
Output
Linear Regression Model Specification (regression)
Main Arguments:
mixture = 0
Engine-Specific Arguments:
model = FALSE
Computational engine: lm
Code
expr1 %>% update(mixture = 0, fresh = TRUE)
Output
Linear Regression Model Specification (regression)
Main Arguments:
mixture = 0
Computational engine: lm
Code
expr2 %>% update(nlambda = 10)
Output
Linear Regression Model Specification (regression)
Engine-Specific Arguments:
nlambda = 10
Computational engine: glmnet
Code
expr3 %>% update(mixture = 1, nlambda = 10)
Output
Linear Regression Model Specification (regression)
Main Arguments:
penalty = tune()
mixture = 1
Engine-Specific Arguments:
nlambda = 10
Computational engine: glmnet
Code
expr3 %>% update(mixture = 1, nlambda = 10, fresh = TRUE)
Output
Linear Regression Model Specification (regression)
Main Arguments:
mixture = 1
Engine-Specific Arguments:
nlambda = 10
Computational engine: glmnet
Code
expr3 %>% update(nlambda = 10)
Output
Linear Regression Model Specification (regression)
Main Arguments:
penalty = tune()
mixture = 0
Engine-Specific Arguments:
nlambda = 10
Computational engine: glmnet
Code
expr3 %>% update(nlambda = 10, fresh = TRUE)
Output
Linear Regression Model Specification (regression)
Engine-Specific Arguments:
nlambda = 10
Computational engine: glmnet
Code
expr4 %>% update(param_tibb)
Output
Linear Regression Model Specification (regression)
Main Arguments:
penalty = 1
mixture = 0.5
Engine-Specific Arguments:
nlambda = 10
Computational engine: glmnet
Code
expr4 %>% update(param_list)
Output
Linear Regression Model Specification (regression)
Main Arguments:
penalty = 1
mixture = 0.5
Engine-Specific Arguments:
nlambda = 10
Computational engine: glmnet
Code
expr4 %>% update(param_tibb, fresh = TRUE)
Output
Linear Regression Model Specification (regression)
Main Arguments:
penalty = 1
mixture = 0.5
Computational engine: glmnet
Code
expr4 %>% update(param_list, fresh = TRUE)
Output
Linear Regression Model Specification (regression)
Main Arguments:
penalty = 1
mixture = 0.5
Computational engine: glmnet
Code
expr5 %>% update(family = "poisson")
Output
Linear Regression Model Specification (regression)
Engine-Specific Arguments:
family = poisson
Computational engine: glm
Code
expr5 %>% update(family = "poisson", fresh = TRUE)
Output
Linear Regression Model Specification (regression)
Engine-Specific Arguments:
family = poisson
Computational engine: glm
Code
expr1 %>% update(param_tibb)
Condition
Error in `update()`:
! Argument `nlambda` is not a main argument.
Code
expr1 %>% update(param_list)
Condition
Error in `update()`:
! Argument `nlambda` is not a main argument.
Code
expr1 %>% update(parameters = "wat")
Condition
Error in `update()`:
! The parameter object should be a list or tibble.
Code
expr1 %>% update(parameters = tibble::tibble(wat = "wat"))
Condition
Error in `update()`:
! Argument `wat` is not a main argument.
Code
linear_reg() %>% update(boop = 0)
Condition
Error in `update_dot_check()`:
! The extra argument `boop` will be ignored.
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