regenerate engine docs

man/rmd/boost_tree_h2o.md

@@ -62,6 +62,7 @@ boost_tree(
 ## 
 ## Model fit template:
 ## agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(), 
+##     weights = missing_arg(), validation_frame = missing_arg(), 
 ##     col_sample_rate = integer(), ntrees = integer(), min_rows = integer(), 
 ##     max_depth = integer(), learn_rate = numeric(), min_split_improvement = numeric(), 
 ##     stopping_rounds = integer())
@@ -98,6 +99,7 @@ boost_tree(
 ## 
 ## Model fit template:
 ## agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(), 
+##     weights = missing_arg(), validation_frame = missing_arg(), 
 ##     col_sample_rate = integer(), ntrees = integer(), min_rows = integer(), 
 ##     max_depth = integer(), learn_rate = numeric(), min_split_improvement = numeric(), 
 ##     stopping_rounds = integer())

man/rmd/glmnet-details.md

@@ -200,6 +200,7 @@ all_tidy_coefs
 ##  9 (Intercept)     9     30.3   2.45     0.640
 ## 10 (Intercept)    10     31.1   2.23     0.673
 ## # … with 630 more rows
+## # ℹ Use `print(n = ...)` to see more rows
 ```
 
 ```r

man/rmd/linear_reg_gls.md

@@ -182,6 +182,7 @@ lme_fit %>% tidy() %>%
 ## # A tibble: 0 × 6
 ## # … with 6 variables: term <chr>, estimate <dbl>, std.error <dbl>, df <dbl>,
 ## #   statistic <dbl>, p.value <dbl>
+## # ℹ Use `colnames()` to see all variable names
 ```
 
 ```r

man/rmd/linear_reg_h2o.md

@@ -40,8 +40,9 @@ linear_reg(penalty = 1, mixture = 0.5) %>%
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), lambda = 1, 
-##     alpha = 0.5, family = "gaussian")
+## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), lambda = 1, alpha = 0.5, 
+##     family = "gaussian")
 ```
 
 ## Preprocessing requirements

man/rmd/logistic_reg_h2o.md

@@ -37,7 +37,8 @@ logistic_reg() %>%
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), family = "binomial")
+## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), family = "binomial")
 ```
 
 To use a non-default argument in [h2o::h2o.glm()], pass in as an engine argument to `set_engine()`:
@@ -58,7 +59,8 @@ logistic_reg() %>%
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), compute_p_values = TRUE, 
+## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), compute_p_values = TRUE, 
 ##     family = "binomial")
 ```
 

man/rmd/mlp_h2o.md

@@ -66,9 +66,10 @@ mlp(
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_mlp(x = missing_arg(), y = missing_arg(), hidden = integer(1), 
-##     l2 = double(1), hidden_dropout_ratios = double(1), epochs = integer(1), 
-##     activation = character(1), rate = double(1))
+## agua::h2o_train_mlp(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), hidden = integer(1), l2 = double(1), 
+##     hidden_dropout_ratios = double(1), epochs = integer(1), activation = character(1), 
+##     rate = double(1))
 ```
 
 ## Translation from parsnip to the original package (classification)
@@ -102,9 +103,10 @@ mlp(
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_mlp(x = missing_arg(), y = missing_arg(), hidden = integer(1), 
-##     l2 = double(1), hidden_dropout_ratios = double(1), epochs = integer(1), 
-##     activation = character(1), rate = double(1))
+## agua::h2o_train_mlp(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), hidden = integer(1), l2 = double(1), 
+##     hidden_dropout_ratios = double(1), epochs = integer(1), activation = character(1), 
+##     rate = double(1))
 ```
 
 

man/rmd/multinom_reg_h2o.md

@@ -39,8 +39,9 @@ multinom_reg(penalty = double(1), mixture = double(1)) %>%
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), family = missing_arg(), 
-##     lambda = double(1), alpha = double(1), family = "multinomial")
+## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), lambda = double(1), alpha = double(1), 
+##     family = "multinomial")
 ```
 
 ## Preprocessing requirements

man/rmd/naive_Bayes_h2o.md

@@ -45,7 +45,8 @@ naive_Bayes(Laplace = numeric(0)) %>%
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_nb(x = missing_arg(), y = missing_arg(), laplace = numeric(0))
+## agua::h2o_train_nb(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), laplace = numeric(0))
 ```
 
 ## Initializing h2o 

man/rmd/poisson_reg_h2o.md

@@ -43,8 +43,9 @@ poisson_reg(penalty = double(1), mixture = double(1)) %>%
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), lambda = double(1), 
-##     alpha = double(1), family = "poisson")
+## agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), lambda = double(1), alpha = double(1), 
+##     family = "poisson")
 ```
 
 ## Preprocessing requirements

man/rmd/proportional_hazards_glmnet.md

@@ -42,7 +42,7 @@ proportional_hazards(penalty = double(1), mixture = double(1)) %>%
 ## Computational engine: glmnet 
 ## 
 ## Model fit template:
-## censored::glmnet_fit_wrapper(formula = missing_arg(), data = missing_arg(), 
+## censored::coxnet_train(formula = missing_arg(), data = missing_arg(), 
 ##     weights = missing_arg(), alpha = double(1))
 ```
 

man/rmd/rand_forest_h2o.md

@@ -44,8 +44,9 @@ rand_forest(
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), mtries = integer(1), 
-##     ntrees = integer(1), min_rows = integer(1))
+## agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), mtries = integer(1), ntrees = integer(1), 
+##     min_rows = integer(1))
 ```
 
 `min_rows()` and `min_cols()` will adjust the number of neighbors if the chosen value if it is not consistent with the actual data dimensions.
@@ -75,8 +76,9 @@ rand_forest(
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), mtries = integer(1), 
-##     ntrees = integer(1), min_rows = integer(1))
+## agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), mtries = integer(1), ntrees = integer(1), 
+##     min_rows = integer(1))
 ```
 
 ## Preprocessing requirements

man/rmd/rule_fit_h2o.md

@@ -59,7 +59,8 @@ rule_fit(
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), rule_generation_ntrees = integer(1), 
+## agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), rule_generation_ntrees = integer(1), 
 ##     max_rule_length = integer(1), lambda = numeric(1))
 ```
 
@@ -94,7 +95,8 @@ rule_fit(
 ## Computational engine: h2o 
 ## 
 ## Model fit template:
-## agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), rule_generation_ntrees = integer(1), 
+## agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     validation_frame = missing_arg(), rule_generation_ntrees = integer(1), 
 ##     max_rule_length = integer(1), lambda = numeric(1))
 ```