diff --git a/man/details_decision_tree_partykit.Rd b/man/details_decision_tree_partykit.Rd index 87afcbcaa..ecd0cd264 100644 --- a/man/details_decision_tree_partykit.Rd +++ b/man/details_decision_tree_partykit.Rd @@ -8,14 +8,14 @@ tree-based structure using hypothesis testing methods. } \details{ -For this engine, there are multiple modes: censored regression, -regression, and classification +For this engine, there are multiple modes: regression, classification, +and censored regression \subsection{Tuning Parameters}{ This model has 2 tuning parameters: \itemize{ -\item \code{tree_depth}: Tree Depth (type: integer, default: see below) \item \code{min_n}: Minimal Node Size (type: integer, default: 20L) +\item \code{tree_depth}: Tree Depth (type: integer, default: see below) } The \code{tree_depth} parameter defaults to \code{0} which means no restrictions diff --git a/man/details_discrim_linear_sparsediscrim.Rd b/man/details_discrim_linear_sparsediscrim.Rd index 3e1c2eed6..6e4233118 100644 --- a/man/details_discrim_linear_sparsediscrim.Rd +++ b/man/details_discrim_linear_sparsediscrim.Rd @@ -51,7 +51,7 @@ discrim_linear(regularization_method = character(0)) \%>\% ## ## Model fit template: ## discrim::fit_regularized_linear(x = missing_arg(), y = missing_arg(), -## method = character(0)) +## regularization_method = character(0)) }\if{html}{\out{}} } diff --git a/man/details_discrim_quad_sparsediscrim.Rd b/man/details_discrim_quad_sparsediscrim.Rd index 9d588c471..ddc934dfb 100644 --- a/man/details_discrim_quad_sparsediscrim.Rd +++ b/man/details_discrim_quad_sparsediscrim.Rd @@ -49,7 +49,7 @@ discrim_quad(regularization_method = character(0)) \%>\% ## ## Model fit template: ## discrim::fit_regularized_quad(x = missing_arg(), y = missing_arg(), -## method = character(0)) +## regularization_method = character(0)) }\if{html}{\out{}} } diff --git a/man/details_linear_reg_lm.Rd b/man/details_linear_reg_lm.Rd index a046e4f67..33085b721 100644 --- a/man/details_linear_reg_lm.Rd +++ b/man/details_linear_reg_lm.Rd @@ -60,8 +60,8 @@ suboptimal; in the case of replication weights, \strong{even wrong}. Hence, standard errors and analysis of variance tables should be treated with care” (emphasis added) -Depending on your application, the degrees of freedown for the model -(and other statistics) might be incorrect. +Depending on your application, the degrees of freedom for the model (and +other statistics) might be incorrect. } \subsection{Saving fitted model objects}{ diff --git a/man/details_logistic_reg_gee.Rd b/man/details_logistic_reg_gee.Rd index 52f2d8072..df4b6db1e 100644 --- a/man/details_logistic_reg_gee.Rd +++ b/man/details_logistic_reg_gee.Rd @@ -62,7 +62,7 @@ call would look like: \if{html}{\out{
}}\preformatted{gee(breaks ~ tension, id = wool, data = warpbreaks, corstr = "exchangeable") }\if{html}{\out{
}} -With parsnip, we suggest using the formula method when fitting: +With \code{parsnip}, we suggest using the formula method when fitting: \if{html}{\out{
}}\preformatted{library(tidymodels) data("toenail", package = "HSAUR3") diff --git a/man/details_mars_earth.Rd b/man/details_mars_earth.Rd index 8478a10b8..1cb7a6c69 100644 --- a/man/details_mars_earth.Rd +++ b/man/details_mars_earth.Rd @@ -19,10 +19,7 @@ This model has 3 tuning parameters: \item \code{prune_method}: Pruning Method (type: character, default: ‘backward’) } -The default value of \code{num_terms} depends on the number of predictor -columns. For a data frame \code{x}, the default is -\code{min(200, max(20, 2 * ncol(x))) + 1} (see -\code{\link[earth:earth]{earth::earth()}} and the reference below). +Parsnip changes the default range for \code{num_terms} to \code{c(50, 500)}. } \subsection{Translation from parsnip to the original package (regression)}{ diff --git a/man/details_mlp_brulee.Rd b/man/details_mlp_brulee.Rd index a3dc99f75..050be24ac 100644 --- a/man/details_mlp_brulee.Rd +++ b/man/details_mlp_brulee.Rd @@ -38,6 +38,8 @@ each batch. \item \code{stop_iter()}: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L). } + +Parsnip changes the default range for \code{learn_rate} to \code{c(-2.5, -0.5)}. } \subsection{Translation from parsnip to the original package (regression)}{ diff --git a/man/details_nearest_neighbor_kknn.Rd b/man/details_nearest_neighbor_kknn.Rd index e931e78c3..e88f854c3 100644 --- a/man/details_nearest_neighbor_kknn.Rd +++ b/man/details_nearest_neighbor_kknn.Rd @@ -18,6 +18,9 @@ This model has 3 tuning parameters: ‘optimal’) \item \code{dist_power}: Minkowski Distance Order (type: double, default: 2.0) } + +Parsnip changes the default range for \code{neighbors} to \code{c(1, 15)} and +\code{dist_power} to \code{c(1/10, 2)}. } \subsection{Translation from parsnip to the original package (regression)}{ diff --git a/man/details_rand_forest_aorsf.Rd b/man/details_rand_forest_aorsf.Rd index 7796fdf1d..eeee82cd3 100644 --- a/man/details_rand_forest_aorsf.Rd +++ b/man/details_rand_forest_aorsf.Rd @@ -9,16 +9,16 @@ trees, each de-correlated from the others. The final prediction uses all predictions from the individual trees and combines them. } \details{ -For this engine, there are multiple modes: censored regression, -classification, and regression +For this engine, there are multiple modes: classification, regression, +and censored regression \subsection{Tuning Parameters}{ This model has 3 tuning parameters: \itemize{ -\item \code{trees}: # Trees (type: integer, default: 500L) -\item \code{min_n}: Minimal Node Size (type: integer, default: 5L) \item \code{mtry}: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors))) +\item \code{trees}: # Trees (type: integer, default: 500L) +\item \code{min_n}: Minimal Node Size (type: integer, default: 5L) } Additionally, this model has one engine-specific tuning parameter: diff --git a/man/details_rand_forest_partykit.Rd b/man/details_rand_forest_partykit.Rd index 25184df2e..e7c759d7f 100644 --- a/man/details_rand_forest_partykit.Rd +++ b/man/details_rand_forest_partykit.Rd @@ -9,15 +9,15 @@ trees, each independent of the others. The final prediction uses all predictions from the individual trees and combines them. } \details{ -For this engine, there are multiple modes: censored regression, -regression, and classification +For this engine, there are multiple modes: regression, classification, +and censored regression \subsection{Tuning Parameters}{ This model has 3 tuning parameters: \itemize{ -\item \code{trees}: # Trees (type: integer, default: 500L) \item \code{min_n}: Minimal Node Size (type: integer, default: 20L) \item \code{mtry}: # Randomly Selected Predictors (type: integer, default: 5L) +\item \code{trees}: # Trees (type: integer, default: 500L) } } diff --git a/man/details_svm_linear_LiblineaR.Rd b/man/details_svm_linear_LiblineaR.Rd index b52638165..b006ca61e 100644 --- a/man/details_svm_linear_LiblineaR.Rd +++ b/man/details_svm_linear_LiblineaR.Rd @@ -22,6 +22,8 @@ This model has 2 tuning parameters: This engine fits models that are L2-regularized for L2-loss. In the \code{\link[LiblineaR:LiblineaR]{LiblineaR::LiblineaR()}} documentation, these are types 1 (classification) and 11 (regression). + +Parsnip changes the default range for \code{cost} to \code{c(-10, 5)}. } \subsection{Translation from parsnip to the original package (regression)}{ diff --git a/man/details_svm_linear_kernlab.Rd b/man/details_svm_linear_kernlab.Rd index aa60e8fee..355a71579 100644 --- a/man/details_svm_linear_kernlab.Rd +++ b/man/details_svm_linear_kernlab.Rd @@ -18,6 +18,8 @@ This model has 2 tuning parameters: \item \code{cost}: Cost (type: double, default: 1.0) \item \code{margin}: Insensitivity Margin (type: double, default: 0.1) } + +Parsnip changes the default range for \code{cost} to \code{c(-10, 5)}. } \subsection{Translation from parsnip to the original package (regression)}{ diff --git a/man/details_svm_poly_kernlab.Rd b/man/details_svm_poly_kernlab.Rd index 30f6c00fc..55e6463b8 100644 --- a/man/details_svm_poly_kernlab.Rd +++ b/man/details_svm_poly_kernlab.Rd @@ -20,6 +20,8 @@ This model has 4 tuning parameters: \item \code{scale_factor}: Scale Factor (type: double, default: 1.0) \item \code{margin}: Insensitivity Margin (type: double, default: 0.1) } + +Parsnip changes the default range for \code{cost} to \code{c(-10, 5)}. } \subsection{Translation from parsnip to the original package (regression)}{ diff --git a/man/details_svm_rbf_kernlab.Rd b/man/details_svm_rbf_kernlab.Rd index 7e4f8f6bc..f3a2ad418 100644 --- a/man/details_svm_rbf_kernlab.Rd +++ b/man/details_svm_rbf_kernlab.Rd @@ -26,6 +26,8 @@ kernlab estimates it from the data using a heuristic method. See \code{\link[kernlab:sigest]{kernlab::sigest()}}. This method uses random numbers so, without setting the seed before fitting, the model will not be reproducible. + +Parsnip changes the default range for \code{cost} to \code{c(-10, 5)}. } \subsection{Translation from parsnip to the original package (regression)}{ diff --git a/man/rmd/C5_rules_C5.0.md b/man/rmd/C5_rules_C5.0.md index c99ae4832..c57dee2fd 100644 --- a/man/rmd/C5_rules_C5.0.md +++ b/man/rmd/C5_rules_C5.0.md @@ -20,7 +20,7 @@ Note that C5.0 has a tool for _early stopping_ during boosting where less iterat The **rules** extension package is required to fit this model. -```r +``` r library(rules) C5_rules( diff --git a/man/rmd/auto_ml_h2o.md b/man/rmd/auto_ml_h2o.md index f2497d78d..2a1d3ac89 100644 --- a/man/rmd/auto_ml_h2o.md +++ b/man/rmd/auto_ml_h2o.md @@ -20,7 +20,7 @@ Engine arguments of interest [agua::h2o_train_auto()] is a wrapper around [h2o::h2o.automl()]. -```r +``` r auto_ml() %>% set_engine("h2o") %>% set_mode("regression") %>% @@ -41,7 +41,7 @@ auto_ml() %>% ## Translation from parsnip to the original package (classification) -```r +``` r auto_ml() %>% set_engine("h2o") %>% set_mode("classification") %>% diff --git a/man/rmd/bag_mars_earth.md b/man/rmd/bag_mars_earth.md index d3ef2a7a1..9825b5a70 100644 --- a/man/rmd/bag_mars_earth.md +++ b/man/rmd/bag_mars_earth.md @@ -22,7 +22,7 @@ The default value of `num_terms` depends on the number of predictor columns. For The **baguette** extension package is required to fit this model. -```r +``` r bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>% set_engine("earth") %>% set_mode("regression") %>% @@ -50,7 +50,7 @@ bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = charac The **baguette** extension package is required to fit this model. -```r +``` r library(baguette) bag_mars( diff --git a/man/rmd/bag_mlp_nnet.md b/man/rmd/bag_mlp_nnet.md index 93955cb49..d412ca3cc 100644 --- a/man/rmd/bag_mlp_nnet.md +++ b/man/rmd/bag_mlp_nnet.md @@ -22,7 +22,7 @@ These defaults are set by the `baguette` package and are different than those in The **baguette** extension package is required to fit this model. -```r +``` r library(baguette) bag_mlp(penalty = double(1), hidden_units = integer(1)) %>% @@ -52,7 +52,7 @@ bag_mlp(penalty = double(1), hidden_units = integer(1)) %>% The **baguette** extension package is required to fit this model. -```r +``` r library(baguette) bag_mlp(penalty = double(1), hidden_units = integer(1)) %>% diff --git a/man/rmd/bag_tree_C5.0.md b/man/rmd/bag_tree_C5.0.md index 18b139868..f14e053d7 100644 --- a/man/rmd/bag_tree_C5.0.md +++ b/man/rmd/bag_tree_C5.0.md @@ -16,7 +16,7 @@ This model has 1 tuning parameters: The **baguette** extension package is required to fit this model. -```r +``` r library(baguette) bag_tree(min_n = integer()) %>% diff --git a/man/rmd/bag_tree_rpart.md b/man/rmd/bag_tree_rpart.md index 1b20f023f..95729b6df 100644 --- a/man/rmd/bag_tree_rpart.md +++ b/man/rmd/bag_tree_rpart.md @@ -25,7 +25,7 @@ For the `class_cost` parameter, the value can be a non-negative scalar for a cla The **baguette** extension package is required to fit this model. -```r +``` r library(baguette) bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% @@ -56,7 +56,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1 The **baguette** extension package is required to fit this model. -```r +``` r library(baguette) bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% @@ -86,7 +86,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1 The **censored** extension package is required to fit this model. -```r +``` r library(censored) bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% diff --git a/man/rmd/boost_tree_C5.0.md b/man/rmd/boost_tree_C5.0.md index 07f73a2a1..7f26b8002 100644 --- a/man/rmd/boost_tree_C5.0.md +++ b/man/rmd/boost_tree_C5.0.md @@ -20,7 +20,7 @@ The implementation of C5.0 limits the number of trees to be between 1 and 100. ## Translation from parsnip to the original package (classification) -```r +``` r boost_tree(trees = integer(), min_n = integer(), sample_size = numeric()) %>% set_engine("C5.0") %>% set_mode("classification") %>% diff --git a/man/rmd/boost_tree_h2o.md b/man/rmd/boost_tree_h2o.md index 7b12a78f1..e40f0d731 100644 --- a/man/rmd/boost_tree_h2o.md +++ b/man/rmd/boost_tree_h2o.md @@ -36,7 +36,7 @@ This model has 8 tuning parameters: The **agua** extension package is required to fit this model. -```r +``` r boost_tree( mtry = integer(), trees = integer(), tree_depth = integer(), learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer() @@ -73,7 +73,7 @@ boost_tree( The **agua** extension package is required to fit this model. -```r +``` r boost_tree( mtry = integer(), trees = integer(), tree_depth = integer(), learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer() diff --git a/man/rmd/boost_tree_mboost.md b/man/rmd/boost_tree_mboost.md index 4bd1ed517..8e7c19e05 100644 --- a/man/rmd/boost_tree_mboost.md +++ b/man/rmd/boost_tree_mboost.md @@ -26,7 +26,7 @@ The `mtry` parameter is related to the number of predictors. The default is to u The **censored** extension package is required to fit this model. -```r +``` r library(censored) boost_tree() %>% diff --git a/man/rmd/boost_tree_spark.md b/man/rmd/boost_tree_spark.md index 37fbd0d86..4ea6fa7c9 100644 --- a/man/rmd/boost_tree_spark.md +++ b/man/rmd/boost_tree_spark.md @@ -28,7 +28,7 @@ The `mtry` parameter is related to the number of predictors. The default depends ## Translation from parsnip to the original package (regression) -```r +``` r boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric() @@ -63,7 +63,7 @@ boost_tree( ## Translation from parsnip to the original package (classification) -```r +``` r boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric() diff --git a/man/rmd/boost_tree_xgboost.md b/man/rmd/boost_tree_xgboost.md index 5ad594062..9d4c1778e 100644 --- a/man/rmd/boost_tree_xgboost.md +++ b/man/rmd/boost_tree_xgboost.md @@ -30,7 +30,7 @@ For `mtry`, the default value of `NULL` translates to using all available column ## Translation from parsnip to the original package (regression) -```r +``` r boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric(), @@ -67,7 +67,7 @@ boost_tree( ## Translation from parsnip to the original package (classification) -```r +``` r boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric(), @@ -128,7 +128,7 @@ This model can utilize sparse data during model fitting and prediction. Both spa The xgboost function that parsnip indirectly wraps, [xgboost::xgb.train()], takes most arguments via the `params` list argument. To supply engine-specific arguments that are documented in [xgboost::xgb.train()] as arguments to be passed via `params`, supply the list elements directly as named arguments to [set_engine()] rather than as elements in `params`. For example, pass a non-default evaluation metric like this: -```r +``` r # good boost_tree() %>% set_engine("xgboost", eval_metric = "mae") @@ -146,7 +146,7 @@ boost_tree() %>% ...rather than this: -```r +``` r # bad boost_tree() %>% set_engine("xgboost", params = list(eval_metric = "mae")) diff --git a/man/rmd/cubist_rules_Cubist.md b/man/rmd/cubist_rules_Cubist.md index 4ce1e2f33..3a009cad6 100644 --- a/man/rmd/cubist_rules_Cubist.md +++ b/man/rmd/cubist_rules_Cubist.md @@ -21,7 +21,7 @@ This model has 3 tuning parameters: The **rules** extension package is required to fit this model. -```r +``` r library(rules) cubist_rules( diff --git a/man/rmd/decision_tree_C5.0.md b/man/rmd/decision_tree_C5.0.md index f2b2a78c2..59e4a767e 100644 --- a/man/rmd/decision_tree_C5.0.md +++ b/man/rmd/decision_tree_C5.0.md @@ -14,7 +14,7 @@ This model has 1 tuning parameters: ## Translation from parsnip to the original package (classification) -```r +``` r decision_tree(min_n = integer()) %>% set_engine("C5.0") %>% set_mode("classification") %>% diff --git a/man/rmd/decision_tree_partykit.md b/man/rmd/decision_tree_partykit.md index 65eeca527..8fa6c53af 100644 --- a/man/rmd/decision_tree_partykit.md +++ b/man/rmd/decision_tree_partykit.md @@ -1,7 +1,7 @@ -For this engine, there are multiple modes: censored regression, regression, and classification +For this engine, there are multiple modes: regression, classification, and censored regression ## Tuning Parameters @@ -9,10 +9,10 @@ For this engine, there are multiple modes: censored regression, regression, and This model has 2 tuning parameters: -- `tree_depth`: Tree Depth (type: integer, default: see below) - - `min_n`: Minimal Node Size (type: integer, default: 20L) +- `tree_depth`: Tree Depth (type: integer, default: see below) + The `tree_depth` parameter defaults to `0` which means no restrictions are applied to tree depth. An engine-specific parameter for this model is: @@ -24,7 +24,7 @@ An engine-specific parameter for this model is: The **bonsai** extension package is required to fit this model. -```r +``` r library(bonsai) decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% @@ -53,7 +53,7 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% The **bonsai** extension package is required to fit this model. -```r +``` r library(bonsai) decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% @@ -84,7 +84,7 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% The **censored** extension package is required to fit this model. -```r +``` r library(censored) decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% diff --git a/man/rmd/decision_tree_rpart.md b/man/rmd/decision_tree_rpart.md index ea3a36d12..10a1e788d 100644 --- a/man/rmd/decision_tree_rpart.md +++ b/man/rmd/decision_tree_rpart.md @@ -18,7 +18,7 @@ This model has 3 tuning parameters: ## Translation from parsnip to the original package (classification) -```r +``` r decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% set_engine("rpart") %>% set_mode("classification") %>% @@ -45,7 +45,7 @@ decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = dou ## Translation from parsnip to the original package (regression) -```r +``` r decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% set_engine("rpart") %>% set_mode("regression") %>% @@ -74,7 +74,7 @@ decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = dou The **censored** extension package is required to fit this model. -```r +``` r library(censored) decision_tree( diff --git a/man/rmd/decision_tree_spark.md b/man/rmd/decision_tree_spark.md index 4fa02ec15..43c6a3617 100644 --- a/man/rmd/decision_tree_spark.md +++ b/man/rmd/decision_tree_spark.md @@ -16,7 +16,7 @@ This model has 2 tuning parameters: ## Translation from parsnip to the original package (classification) -```r +``` r decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% set_engine("spark") %>% set_mode("classification") %>% @@ -42,7 +42,7 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% ## Translation from parsnip to the original package (regression) -```r +``` r decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% set_engine("spark") %>% set_mode("regression") %>% diff --git a/man/rmd/discrim_flexible_earth.md b/man/rmd/discrim_flexible_earth.md index edfdc7337..7dc2f3ce7 100644 --- a/man/rmd/discrim_flexible_earth.md +++ b/man/rmd/discrim_flexible_earth.md @@ -22,7 +22,7 @@ The default value of `num_terms` depends on the number of columns (`p`): `min(20 The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_flexible( diff --git a/man/rmd/discrim_linear_MASS.md b/man/rmd/discrim_linear_MASS.md index deb6efb09..4448e1577 100644 --- a/man/rmd/discrim_linear_MASS.md +++ b/man/rmd/discrim_linear_MASS.md @@ -12,7 +12,7 @@ This engine has no tuning parameters. The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_linear() %>% diff --git a/man/rmd/discrim_linear_mda.md b/man/rmd/discrim_linear_mda.md index 3bd4cdcdf..0217c09c5 100644 --- a/man/rmd/discrim_linear_mda.md +++ b/man/rmd/discrim_linear_mda.md @@ -17,7 +17,7 @@ This model has 1 tuning parameter: The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_linear(penalty = numeric(0)) %>% diff --git a/man/rmd/discrim_linear_sda.md b/man/rmd/discrim_linear_sda.md index fdab652d6..ccf745e9a 100644 --- a/man/rmd/discrim_linear_sda.md +++ b/man/rmd/discrim_linear_sda.md @@ -22,7 +22,7 @@ However, there are a few engine-specific parameters that can be set or optimized The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_linear() %>% diff --git a/man/rmd/discrim_linear_sparsediscrim.md b/man/rmd/discrim_linear_sparsediscrim.md index 69eaf780b..53adc38fd 100644 --- a/man/rmd/discrim_linear_sparsediscrim.md +++ b/man/rmd/discrim_linear_sparsediscrim.md @@ -23,7 +23,7 @@ The possible values of this parameter, and the functions that they execute, are: The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_linear(regularization_method = character(0)) %>% @@ -41,7 +41,7 @@ discrim_linear(regularization_method = character(0)) %>% ## ## Model fit template: ## discrim::fit_regularized_linear(x = missing_arg(), y = missing_arg(), -## method = character(0)) +## regularization_method = character(0)) ``` ## Preprocessing requirements diff --git a/man/rmd/discrim_quad_MASS.md b/man/rmd/discrim_quad_MASS.md index 26fcfb940..3607036e7 100644 --- a/man/rmd/discrim_quad_MASS.md +++ b/man/rmd/discrim_quad_MASS.md @@ -12,7 +12,7 @@ This engine has no tuning parameters. The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_quad() %>% diff --git a/man/rmd/discrim_quad_sparsediscrim.md b/man/rmd/discrim_quad_sparsediscrim.md index 055b4c825..5751b1314 100644 --- a/man/rmd/discrim_quad_sparsediscrim.md +++ b/man/rmd/discrim_quad_sparsediscrim.md @@ -22,7 +22,7 @@ The possible values of this parameter, and the functions that they execute, are: The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_quad(regularization_method = character(0)) %>% @@ -40,7 +40,7 @@ discrim_quad(regularization_method = character(0)) %>% ## ## Model fit template: ## discrim::fit_regularized_quad(x = missing_arg(), y = missing_arg(), -## method = character(0)) +## regularization_method = character(0)) ``` ## Preprocessing requirements diff --git a/man/rmd/discrim_regularized_klaR.md b/man/rmd/discrim_regularized_klaR.md index e5fcc0d3e..473f7f635 100644 --- a/man/rmd/discrim_regularized_klaR.md +++ b/man/rmd/discrim_regularized_klaR.md @@ -27,7 +27,7 @@ Some special cases for the RDA model: The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) %>% diff --git a/man/rmd/gen_additive_mod_mgcv.md b/man/rmd/gen_additive_mod_mgcv.md index 69b00ca78..2e46173f5 100644 --- a/man/rmd/gen_additive_mod_mgcv.md +++ b/man/rmd/gen_additive_mod_mgcv.md @@ -18,7 +18,7 @@ This model has 2 tuning parameters: ## Translation from parsnip to the original package (regression) -```r +``` r gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% set_engine("mgcv") %>% set_mode("regression") %>% @@ -42,7 +42,7 @@ gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% ## Translation from parsnip to the original package (classification) -```r +``` r gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% set_engine("mgcv") %>% set_mode("classification") %>% @@ -69,7 +69,7 @@ This model should be used with a model formula so that smooth terms can be speci -```r +``` r library(mgcv) gen_additive_mod() %>% set_engine("mgcv") %>% @@ -99,7 +99,7 @@ The smoothness of the terms will need to be manually specified (e.g., using `s(x When using a workflow, pass the _model formula_ to [workflows::add_model()]'s `formula` argument, and a simplified _preprocessing formula_ elsewhere. -```r +``` r spec <- gen_additive_mod() %>% set_engine("mgcv") %>% diff --git a/man/rmd/glmnet-details.md b/man/rmd/glmnet-details.md index 3c76f8f11..dba57c29b 100644 --- a/man/rmd/glmnet-details.md +++ b/man/rmd/glmnet-details.md @@ -21,7 +21,7 @@ When the `predict()` method is called, it automatically uses the penalty that wa -```r +``` r library(tidymodels) fit <- @@ -45,7 +45,7 @@ predict(fit, mtcars[1:3,]) However, any penalty values can be predicted simultaneously using the `multi_predict()` method: -```r +``` r # predict at c(0.00, 0.01) multi_predict(fit, mtcars[1:3,], penalty = c(0.00, 0.01)) ``` @@ -59,7 +59,7 @@ multi_predict(fit, mtcars[1:3,], penalty = c(0.00, 0.01)) ## 3 ``` -```r +``` r # unnested: multi_predict(fit, mtcars[1:3,], penalty = c(0.00, 0.01)) %>% add_rowindex() %>% @@ -83,7 +83,7 @@ multi_predict(fit, mtcars[1:3,], penalty = c(0.00, 0.01)) %>% It may appear odd that the `lambda` value does not get used in the fit: -```r +``` r linear_reg(penalty = 1) %>% set_engine("glmnet") %>% translate() @@ -117,7 +117,7 @@ For example, we have found that if you want a fully ridge regression model (i.e. If we want to use our own path, the argument is passed as an engine-specific option: -```r +``` r coef_path_values <- c(0, 10^seq(-5, 1, length.out = 7)) fit_ridge <- @@ -132,7 +132,7 @@ all.equal(sort(fit_ridge$fit$lambda), coef_path_values) ## [1] TRUE ``` -```r +``` r # predict at penalty = 1 predict(fit_ridge, mtcars[1:3,]) ``` @@ -155,7 +155,7 @@ predict(fit_ridge, mtcars[1:3,]) When parsnip makes a model, it gives it an extra class. Use the `tidy()` method on the object, it produces coefficients for the penalty that was originally requested: -```r +``` r tidy(fit) ``` @@ -175,7 +175,7 @@ tidy(fit) Note that there is a `tidy()` method for `glmnet` objects in the `broom` package. If this is used directly on the underlying `glmnet` object, it returns _all of coefficients on the path_: -```r +``` r # Use the basic tidy() method for glmnet all_tidy_coefs <- broom:::tidy.glmnet(fit$fit) all_tidy_coefs @@ -194,7 +194,7 @@ all_tidy_coefs ## # i 634 more rows ``` -```r +``` r length(unique(all_tidy_coefs$lambda)) ``` diff --git a/man/rmd/linear_reg_brulee.md b/man/rmd/linear_reg_brulee.md index ee1fea6f3..926ae5fc1 100644 --- a/man/rmd/linear_reg_brulee.md +++ b/man/rmd/linear_reg_brulee.md @@ -28,7 +28,7 @@ Other engine arguments of interest: ## Translation from parsnip to the original package (regression) -```r +``` r linear_reg(penalty = double(1)) %>% set_engine("brulee") %>% translate() diff --git a/man/rmd/linear_reg_gee.md b/man/rmd/linear_reg_gee.md index 01aaab16b..1eef0675f 100644 --- a/man/rmd/linear_reg_gee.md +++ b/man/rmd/linear_reg_gee.md @@ -12,7 +12,7 @@ This model has no formal tuning parameters. It may be beneficial to determine th The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) linear_reg() %>% diff --git a/man/rmd/linear_reg_glm.md b/man/rmd/linear_reg_glm.md index 552984dde..cff01bf81 100644 --- a/man/rmd/linear_reg_glm.md +++ b/man/rmd/linear_reg_glm.md @@ -10,7 +10,7 @@ This engine has no tuning parameters but you can set the `family` parameter (and ## Translation from parsnip to the original package -```r +``` r linear_reg() %>% set_engine("glm") %>% translate() @@ -29,7 +29,7 @@ linear_reg() %>% To use a non-default `family` and/or `link`, pass in as an argument to `set_engine()`: -```r +``` r linear_reg() %>% set_engine("glm", family = stats::poisson(link = "sqrt")) %>% translate() diff --git a/man/rmd/linear_reg_glmer.md b/man/rmd/linear_reg_glmer.md index 36f4de928..0ec98d6d5 100644 --- a/man/rmd/linear_reg_glmer.md +++ b/man/rmd/linear_reg_glmer.md @@ -12,7 +12,7 @@ This model has no tuning parameters. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) linear_reg() %>% @@ -48,7 +48,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: diff --git a/man/rmd/linear_reg_glmnet.md b/man/rmd/linear_reg_glmnet.md index b2f74d885..ec44b1eb2 100644 --- a/man/rmd/linear_reg_glmnet.md +++ b/man/rmd/linear_reg_glmnet.md @@ -20,7 +20,7 @@ The `penalty` parameter has no default and requires a single numeric value. For ## Translation from parsnip to the original package -```r +``` r linear_reg(penalty = double(1), mixture = double(1)) %>% set_engine("glmnet") %>% translate() diff --git a/man/rmd/linear_reg_gls.md b/man/rmd/linear_reg_gls.md index d845d0081..59e4ae45f 100644 --- a/man/rmd/linear_reg_gls.md +++ b/man/rmd/linear_reg_gls.md @@ -12,7 +12,7 @@ This model has no tuning parameters. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) linear_reg() %>% @@ -42,7 +42,7 @@ The model can accept case weights. With parsnip, we suggest using the _fixed effects_ formula method when fitting, but the details of the correlation structure should be passed to `set_engine()` since it is an irregular (but required) argument: -```r +``` r library(tidymodels) # load nlme to be able to use the `cor*()` functions library(nlme) diff --git a/man/rmd/linear_reg_h2o.md b/man/rmd/linear_reg_h2o.md index 54f2cd9b3..be50cc620 100644 --- a/man/rmd/linear_reg_h2o.md +++ b/man/rmd/linear_reg_h2o.md @@ -24,7 +24,7 @@ The choice of `mixture` depends on the engine parameter `solver`, which is autom -```r +``` r linear_reg(penalty = 1, mixture = 0.5) %>% set_engine("h2o") %>% translate() diff --git a/man/rmd/linear_reg_keras.md b/man/rmd/linear_reg_keras.md index 50c0cfac8..798139d65 100644 --- a/man/rmd/linear_reg_keras.md +++ b/man/rmd/linear_reg_keras.md @@ -16,7 +16,7 @@ For `penalty`, the amount of regularization is _only_ L2 penalty (i.e., ridge or ## Translation from parsnip to the original package -```r +``` r linear_reg(penalty = double(1)) %>% set_engine("keras") %>% translate() diff --git a/man/rmd/linear_reg_lm.md b/man/rmd/linear_reg_lm.md index 9fa851bc0..0bf21f18d 100644 --- a/man/rmd/linear_reg_lm.md +++ b/man/rmd/linear_reg_lm.md @@ -10,7 +10,7 @@ This engine has no tuning parameters. ## Translation from parsnip to the original package -```r +``` r linear_reg() %>% set_engine("lm") %>% translate() @@ -39,7 +39,7 @@ The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that e _However_, the documentation in [stats::lm()] assumes that is specific type of case weights are being used: "Non-NULL weights can be used to indicate that different observations have different variances (with the values in weights being inversely proportional to the variances); or equivalently, when the elements of weights are positive integers `w_i`, that each response `y_i` is the mean of `w_i` unit-weight observations (including the case that there are w_i observations equal to `y_i` and the data have been summarized). However, in the latter case, notice that within-group variation is not used. Therefore, the sigma estimate and residual degrees of freedom may be suboptimal; in the case of replication weights, **even wrong**. Hence, standard errors and analysis of variance tables should be treated with care" (emphasis added) -Depending on your application, the degrees of freedown for the model (and other statistics) might be incorrect. +Depending on your application, the degrees of freedom for the model (and other statistics) might be incorrect. ## Saving fitted model objects diff --git a/man/rmd/linear_reg_lme.md b/man/rmd/linear_reg_lme.md index 27d3db2f3..c939889b7 100644 --- a/man/rmd/linear_reg_lme.md +++ b/man/rmd/linear_reg_lme.md @@ -12,7 +12,7 @@ This model has no tuning parameters. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) linear_reg() %>% diff --git a/man/rmd/linear_reg_lmer.md b/man/rmd/linear_reg_lmer.md index d28b4f37e..96a92b6cb 100644 --- a/man/rmd/linear_reg_lmer.md +++ b/man/rmd/linear_reg_lmer.md @@ -12,7 +12,7 @@ This model has no tuning parameters. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) linear_reg() %>% @@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: diff --git a/man/rmd/linear_reg_spark.md b/man/rmd/linear_reg_spark.md index 2bd15afed..755802e44 100644 --- a/man/rmd/linear_reg_spark.md +++ b/man/rmd/linear_reg_spark.md @@ -22,7 +22,7 @@ For `penalty`, the amount of regularization includes both the L1 penalty (i.e., ## Translation from parsnip to the original package -```r +``` r linear_reg(penalty = double(1), mixture = double(1)) %>% set_engine("spark") %>% translate() diff --git a/man/rmd/linear_reg_stan.md b/man/rmd/linear_reg_stan.md index 8da583a1b..d1f539f2e 100644 --- a/man/rmd/linear_reg_stan.md +++ b/man/rmd/linear_reg_stan.md @@ -23,7 +23,7 @@ See [rstan::sampling()] and [rstanarm::priors()] for more information on these a ## Translation from parsnip to the original package -```r +``` r linear_reg() %>% set_engine("stan") %>% translate() diff --git a/man/rmd/linear_reg_stan_glmer.md b/man/rmd/linear_reg_stan_glmer.md index edd456ce2..4a0554335 100644 --- a/man/rmd/linear_reg_stan_glmer.md +++ b/man/rmd/linear_reg_stan_glmer.md @@ -25,7 +25,7 @@ See `?rstanarm::stan_glmer` and `?rstan::sampling` for more information. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) linear_reg() %>% @@ -53,7 +53,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: diff --git a/man/rmd/logistic_reg_LiblineaR.md b/man/rmd/logistic_reg_LiblineaR.md index 6da4a8430..f4ac4f861 100644 --- a/man/rmd/logistic_reg_LiblineaR.md +++ b/man/rmd/logistic_reg_LiblineaR.md @@ -20,7 +20,7 @@ Be aware that the `LiblineaR` engine regularizes the intercept. Other regularize ## Translation from parsnip to the original package -```r +``` r logistic_reg(penalty = double(1), mixture = double(1)) %>% set_engine("LiblineaR") %>% translate() diff --git a/man/rmd/logistic_reg_brulee.md b/man/rmd/logistic_reg_brulee.md index 9573a98fa..29823ff49 100644 --- a/man/rmd/logistic_reg_brulee.md +++ b/man/rmd/logistic_reg_brulee.md @@ -29,7 +29,7 @@ Other engine arguments of interest: ## Translation from parsnip to the original package (classification) -```r +``` r logistic_reg(penalty = double(1)) %>% set_engine("brulee") %>% translate() diff --git a/man/rmd/logistic_reg_gee.md b/man/rmd/logistic_reg_gee.md index 4ca4e3371..bf2a4b4fe 100644 --- a/man/rmd/logistic_reg_gee.md +++ b/man/rmd/logistic_reg_gee.md @@ -12,7 +12,7 @@ This model has no formal tuning parameters. It may be beneficial to determine th The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) logistic_reg() %>% @@ -47,7 +47,7 @@ Both `gee:gee()` and `gee:geepack()` specify the id/cluster variable using an ar gee(breaks ~ tension, id = wool, data = warpbreaks, corstr = "exchangeable") ``` -With parsnip, we suggest using the formula method when fitting: +With `parsnip`, we suggest using the formula method when fitting: ```r library(tidymodels) diff --git a/man/rmd/logistic_reg_glmer.md b/man/rmd/logistic_reg_glmer.md index 1bd43f722..5df1028d3 100644 --- a/man/rmd/logistic_reg_glmer.md +++ b/man/rmd/logistic_reg_glmer.md @@ -12,7 +12,7 @@ This model has no tuning parameters. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) logistic_reg() %>% @@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: diff --git a/man/rmd/logistic_reg_glmnet.md b/man/rmd/logistic_reg_glmnet.md index d4c19eff0..e616b20ee 100644 --- a/man/rmd/logistic_reg_glmnet.md +++ b/man/rmd/logistic_reg_glmnet.md @@ -22,7 +22,7 @@ The `penalty` parameter has no default and requires a single numeric value. For ## Translation from parsnip to the original package -```r +``` r logistic_reg(penalty = double(1), mixture = double(1)) %>% set_engine("glmnet") %>% translate() diff --git a/man/rmd/logistic_reg_h2o.md b/man/rmd/logistic_reg_h2o.md index 121f9f875..11f452a60 100644 --- a/man/rmd/logistic_reg_h2o.md +++ b/man/rmd/logistic_reg_h2o.md @@ -25,7 +25,7 @@ The choice of `mixture` depends on the engine parameter `solver`, which is autom [agua::h2o_train_glm()] for `logistic_reg()` is a wrapper around [h2o::h2o.glm()]. h2o will automatically picks the link function and distribution family or binomial responses. -```r +``` r logistic_reg() %>% set_engine("h2o") %>% translate() @@ -44,7 +44,7 @@ logistic_reg() %>% To use a non-default argument in [h2o::h2o.glm()], pass in as an engine argument to `set_engine()`: -```r +``` r logistic_reg() %>% set_engine("h2o", compute_p_values = TRUE) %>% translate() diff --git a/man/rmd/logistic_reg_keras.md b/man/rmd/logistic_reg_keras.md index cf3ea76df..bba345c40 100644 --- a/man/rmd/logistic_reg_keras.md +++ b/man/rmd/logistic_reg_keras.md @@ -16,7 +16,7 @@ For `penalty`, the amount of regularization is _only_ L2 penalty (i.e., ridge or ## Translation from parsnip to the original package -```r +``` r logistic_reg(penalty = double(1)) %>% set_engine("keras") %>% translate() diff --git a/man/rmd/logistic_reg_spark.md b/man/rmd/logistic_reg_spark.md index feed4a39b..7b9b200a6 100644 --- a/man/rmd/logistic_reg_spark.md +++ b/man/rmd/logistic_reg_spark.md @@ -22,7 +22,7 @@ For `penalty`, the amount of regularization includes both the L1 penalty (i.e., ## Translation from parsnip to the original package -```r +``` r logistic_reg(penalty = double(1), mixture = double(1)) %>% set_engine("spark") %>% translate() diff --git a/man/rmd/logistic_reg_stan.md b/man/rmd/logistic_reg_stan.md index 7587d8db2..dbcfe2177 100644 --- a/man/rmd/logistic_reg_stan.md +++ b/man/rmd/logistic_reg_stan.md @@ -23,7 +23,7 @@ See [rstan::sampling()] and [rstanarm::priors()] for more information on these a ## Translation from parsnip to the original package -```r +``` r logistic_reg() %>% set_engine("stan") %>% translate() diff --git a/man/rmd/logistic_reg_stan_glmer.md b/man/rmd/logistic_reg_stan_glmer.md index 78ef38853..a2c770739 100644 --- a/man/rmd/logistic_reg_stan_glmer.md +++ b/man/rmd/logistic_reg_stan_glmer.md @@ -25,7 +25,7 @@ See `?rstanarm::stan_glmer` and `?rstan::sampling` for more information. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) logistic_reg() %>% @@ -52,7 +52,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: diff --git a/man/rmd/mars_earth.md b/man/rmd/mars_earth.md index 7d552d76b..1ed5ee796 100644 --- a/man/rmd/mars_earth.md +++ b/man/rmd/mars_earth.md @@ -15,12 +15,12 @@ This model has 3 tuning parameters: - `prune_method`: Pruning Method (type: character, default: 'backward') -The default value of `num_terms` depends on the number of predictor columns. For a data frame `x`, the default is `min(200, max(20, 2 * ncol(x))) + 1` (see [earth::earth()] and the reference below). +Parsnip changes the default range for `num_terms` to `c(50, 500)`. ## Translation from parsnip to the original package (regression) -```r +``` r mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>% set_engine("earth") %>% set_mode("regression") %>% @@ -46,7 +46,7 @@ mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character( ## Translation from parsnip to the original package (classification) -```r +``` r mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>% set_engine("earth") %>% set_mode("classification") %>% diff --git a/man/rmd/mlp_brulee.md b/man/rmd/mlp_brulee.md index 77b03ffda..f8580b094 100644 --- a/man/rmd/mlp_brulee.md +++ b/man/rmd/mlp_brulee.md @@ -32,11 +32,12 @@ Other engine arguments of interest: - `class_weights()`: Numeric class weights. See [brulee::brulee_mlp()]. - `stop_iter()`: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L). +Parsnip changes the default range for `learn_rate` to `c(-2.5, -0.5)`. ## Translation from parsnip to the original package (regression) -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), @@ -74,7 +75,7 @@ Note that parsnip automatically sets linear activation in the last layer. ## Translation from parsnip to the original package (classification) -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), diff --git a/man/rmd/mlp_h2o.md b/man/rmd/mlp_h2o.md index 469fb4119..410ca15b9 100644 --- a/man/rmd/mlp_h2o.md +++ b/man/rmd/mlp_h2o.md @@ -38,7 +38,7 @@ Other engine arguments of interest: [agua::h2o_train_mlp] is a wrapper around [h2o::h2o.deeplearning()]. -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), @@ -75,7 +75,7 @@ mlp( ## Translation from parsnip to the original package (classification) -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), diff --git a/man/rmd/mlp_keras.md b/man/rmd/mlp_keras.md index 85e2c9bb1..be3722b94 100644 --- a/man/rmd/mlp_keras.md +++ b/man/rmd/mlp_keras.md @@ -22,7 +22,7 @@ This model has 5 tuning parameters: ## Translation from parsnip to the original package (regression) -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), @@ -56,7 +56,7 @@ mlp( ## Translation from parsnip to the original package (classification) -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), diff --git a/man/rmd/mlp_nnet.md b/man/rmd/mlp_nnet.md index d404e0136..3cf9f1459 100644 --- a/man/rmd/mlp_nnet.md +++ b/man/rmd/mlp_nnet.md @@ -21,7 +21,7 @@ Note that, in [nnet::nnet()], the maximum number of parameters is an argument wi ## Translation from parsnip to the original package (regression) -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), @@ -52,7 +52,7 @@ Note that parsnip automatically sets linear activation in the last layer. ## Translation from parsnip to the original package (classification) -```r +``` r mlp( hidden_units = integer(1), penalty = double(1), diff --git a/man/rmd/multinom_reg_brulee.md b/man/rmd/multinom_reg_brulee.md index 20166fac6..8a32c1961 100644 --- a/man/rmd/multinom_reg_brulee.md +++ b/man/rmd/multinom_reg_brulee.md @@ -29,7 +29,7 @@ Other engine arguments of interest: ## Translation from parsnip to the original package (classification) -```r +``` r multinom_reg(penalty = double(1)) %>% set_engine("brulee") %>% translate() diff --git a/man/rmd/multinom_reg_glmnet.md b/man/rmd/multinom_reg_glmnet.md index 1914e0860..389c066e8 100644 --- a/man/rmd/multinom_reg_glmnet.md +++ b/man/rmd/multinom_reg_glmnet.md @@ -22,7 +22,7 @@ The `penalty` parameter has no default and requires a single numeric value. For ## Translation from parsnip to the original package -```r +``` r multinom_reg(penalty = double(1), mixture = double(1)) %>% set_engine("glmnet") %>% translate() diff --git a/man/rmd/multinom_reg_h2o.md b/man/rmd/multinom_reg_h2o.md index 4b4c5e7de..8a06045ea 100644 --- a/man/rmd/multinom_reg_h2o.md +++ b/man/rmd/multinom_reg_h2o.md @@ -23,7 +23,7 @@ The choice of `mixture` depends on the engine parameter `solver`, which is autom [agua::h2o_train_glm()] for `multinom_reg()` is a wrapper around [h2o::h2o.glm()] with `family = 'multinomial'`. -```r +``` r multinom_reg(penalty = double(1), mixture = double(1)) %>% set_engine("h2o") %>% translate() diff --git a/man/rmd/multinom_reg_keras.md b/man/rmd/multinom_reg_keras.md index d24a59427..7836e3c06 100644 --- a/man/rmd/multinom_reg_keras.md +++ b/man/rmd/multinom_reg_keras.md @@ -16,7 +16,7 @@ For `penalty`, the amount of regularization is _only_ L2 penalty (i.e., ridge or ## Translation from parsnip to the original package -```r +``` r multinom_reg(penalty = double(1)) %>% set_engine("keras") %>% translate() diff --git a/man/rmd/multinom_reg_nnet.md b/man/rmd/multinom_reg_nnet.md index e5aaadc52..3ebf0f0de 100644 --- a/man/rmd/multinom_reg_nnet.md +++ b/man/rmd/multinom_reg_nnet.md @@ -16,7 +16,7 @@ For `penalty`, the amount of regularization includes only the L2 penalty (i.e., ## Translation from parsnip to the original package -```r +``` r multinom_reg(penalty = double(1)) %>% set_engine("nnet") %>% translate() diff --git a/man/rmd/multinom_reg_spark.md b/man/rmd/multinom_reg_spark.md index 35d30c7cf..28668e65d 100644 --- a/man/rmd/multinom_reg_spark.md +++ b/man/rmd/multinom_reg_spark.md @@ -22,7 +22,7 @@ For `penalty`, the amount of regularization includes both the L1 penalty (i.e., ## Translation from parsnip to the original package -```r +``` r multinom_reg(penalty = double(1), mixture = double(1)) %>% set_engine("spark") %>% translate() diff --git a/man/rmd/naive_Bayes_h2o.md b/man/rmd/naive_Bayes_h2o.md index e25b4d348..12a877a77 100644 --- a/man/rmd/naive_Bayes_h2o.md +++ b/man/rmd/naive_Bayes_h2o.md @@ -30,7 +30,7 @@ The **agua** extension package is required to fit this model. [agua::h2o_train_nb()] is a wrapper around [h2o::h2o.naiveBayes()]. -```r +``` r naive_Bayes(Laplace = numeric(0)) %>% set_engine("h2o") %>% translate() diff --git a/man/rmd/naive_Bayes_klaR.md b/man/rmd/naive_Bayes_klaR.md index 961d2ad15..4af515369 100644 --- a/man/rmd/naive_Bayes_klaR.md +++ b/man/rmd/naive_Bayes_klaR.md @@ -21,7 +21,7 @@ Note that the engine argument `usekernel` is set to `TRUE` by default when using The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) %>% diff --git a/man/rmd/naive_Bayes_naivebayes.md b/man/rmd/naive_Bayes_naivebayes.md index 63fcec9a0..6c491c62a 100644 --- a/man/rmd/naive_Bayes_naivebayes.md +++ b/man/rmd/naive_Bayes_naivebayes.md @@ -21,7 +21,7 @@ Note that the engine argument `usekernel` is set to `TRUE` by default when using The **discrim** extension package is required to fit this model. -```r +``` r library(discrim) naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) %>% diff --git a/man/rmd/nearest_neighbor_kknn.md b/man/rmd/nearest_neighbor_kknn.md index ac6862d69..224a406d0 100644 --- a/man/rmd/nearest_neighbor_kknn.md +++ b/man/rmd/nearest_neighbor_kknn.md @@ -15,10 +15,13 @@ This model has 3 tuning parameters: - `dist_power`: Minkowski Distance Order (type: double, default: 2.0) +Parsnip changes the default range for `neighbors` to `c(1, 15)` and `dist_power` to `c(1/10, 2)`. + + ## Translation from parsnip to the original package (regression) -```r +``` r nearest_neighbor( neighbors = integer(1), weight_func = character(1), @@ -49,7 +52,7 @@ nearest_neighbor( ## Translation from parsnip to the original package (classification) -```r +``` r nearest_neighbor( neighbors = integer(1), weight_func = character(1), diff --git a/man/rmd/null-model.md b/man/rmd/null-model.md index 85ac9eb85..57e49bcda 100644 --- a/man/rmd/null-model.md +++ b/man/rmd/null-model.md @@ -6,7 +6,7 @@ For this type of model, the template of the fit calls are below: ## parsnip -```r +``` r null_model() %>% set_engine("parsnip") %>% set_mode("regression") %>% @@ -23,7 +23,7 @@ null_model() %>% ``` -```r +``` r null_model() %>% set_engine("parsnip") %>% set_mode("classification") %>% diff --git a/man/rmd/one-hot.md b/man/rmd/one-hot.md index cb221c248..b05a49315 100644 --- a/man/rmd/one-hot.md +++ b/man/rmd/one-hot.md @@ -5,7 +5,7 @@ By default, `model.matrix()` generates binary indicator variables for factor pre For example, `species` and `island` both have three levels but `model.matrix()` creates two indicator variables for each: -```r +``` r library(dplyr) library(modeldata) data(penguins) @@ -17,7 +17,7 @@ levels(penguins$species) ## [1] "Adelie" "Chinstrap" "Gentoo" ``` -```r +``` r levels(penguins$island) ``` @@ -25,7 +25,7 @@ levels(penguins$island) ## [1] "Biscoe" "Dream" "Torgersen" ``` -```r +``` r model.matrix(~ species + island, data = penguins) %>% colnames() ``` @@ -38,7 +38,7 @@ model.matrix(~ species + island, data = penguins) %>% For a formula with no intercept, the first factor is expanded to indicators for _all_ factor levels but all other factors are expanded to all but one (as above): -```r +``` r model.matrix(~ 0 + species + island, data = penguins) %>% colnames() ``` @@ -53,7 +53,7 @@ For inference, this hybrid encoding can be problematic. To generate all indicators, use this contrast: -```r +``` r # Switch out the contrast method old_contr <- options("contrasts")$contrasts new_contr <- old_contr @@ -69,7 +69,7 @@ model.matrix(~ species + island, data = penguins) %>% ## [5] "islandBiscoe" "islandDream" "islandTorgersen" ``` -```r +``` r options(contrasts = old_contr) ``` diff --git a/man/rmd/pls_mixOmics.md b/man/rmd/pls_mixOmics.md index a4f3c7621..5041c00b5 100644 --- a/man/rmd/pls_mixOmics.md +++ b/man/rmd/pls_mixOmics.md @@ -19,7 +19,7 @@ This model has 2 tuning parameters: The **plsmod** extension package is required to fit this model. -```r +``` r library(plsmod) pls(num_comp = integer(1), predictor_prop = double(1)) %>% @@ -54,7 +54,7 @@ pls(num_comp = integer(1), predictor_prop = double(1)) %>% The **plsmod** extension package is required to fit this model. -```r +``` r library(plsmod) pls(num_comp = integer(1), predictor_prop = double(1)) %>% @@ -84,7 +84,7 @@ In this case, [plsmod::pls_fit()] has the same role as above but eventually targ This package is available via the Bioconductor repository and is not accessible via CRAN. You can install using: -```r +``` r if (!require("remotes", quietly = TRUE)) { install.packages("remotes") } diff --git a/man/rmd/poisson_reg_gee.md b/man/rmd/poisson_reg_gee.md index fff3b0503..2a21ef5d0 100644 --- a/man/rmd/poisson_reg_gee.md +++ b/man/rmd/poisson_reg_gee.md @@ -12,7 +12,7 @@ This model has no formal tuning parameters. It may be beneficial to determine th The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) poisson_reg(engine = "gee") %>% diff --git a/man/rmd/poisson_reg_glm.md b/man/rmd/poisson_reg_glm.md index 39aa91889..333228561 100644 --- a/man/rmd/poisson_reg_glm.md +++ b/man/rmd/poisson_reg_glm.md @@ -12,7 +12,7 @@ This engine has no tuning parameters. The **poissonreg** extension package is required to fit this model. -```r +``` r library(poissonreg) poisson_reg() %>% diff --git a/man/rmd/poisson_reg_glmer.md b/man/rmd/poisson_reg_glmer.md index f610f5eac..fc87257fe 100644 --- a/man/rmd/poisson_reg_glmer.md +++ b/man/rmd/poisson_reg_glmer.md @@ -12,7 +12,7 @@ This model has no tuning parameters. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) poisson_reg(engine = "glmer") %>% @@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: diff --git a/man/rmd/poisson_reg_glmnet.md b/man/rmd/poisson_reg_glmnet.md index 8bde6966f..485ebfa65 100644 --- a/man/rmd/poisson_reg_glmnet.md +++ b/man/rmd/poisson_reg_glmnet.md @@ -24,7 +24,7 @@ The `penalty` parameter has no default and requires a single numeric value. For The **poissonreg** extension package is required to fit this model. -```r +``` r library(poissonreg) poisson_reg(penalty = double(1), mixture = double(1)) %>% diff --git a/man/rmd/poisson_reg_h2o.md b/man/rmd/poisson_reg_h2o.md index 807e5e477..44670bce4 100644 --- a/man/rmd/poisson_reg_h2o.md +++ b/man/rmd/poisson_reg_h2o.md @@ -25,7 +25,7 @@ The choice of `mixture` depends on the engine parameter `solver`, which is autom The **agua** extension package is required to fit this model. -```r +``` r library(poissonreg) poisson_reg(penalty = double(1), mixture = double(1)) %>% diff --git a/man/rmd/poisson_reg_hurdle.md b/man/rmd/poisson_reg_hurdle.md index 0d23a9d7f..d1fd006f6 100644 --- a/man/rmd/poisson_reg_hurdle.md +++ b/man/rmd/poisson_reg_hurdle.md @@ -12,7 +12,7 @@ This engine has no tuning parameters. The **poissonreg** extension package is required to fit this model. -```r +``` r library(poissonreg) poisson_reg() %>% @@ -43,7 +43,7 @@ When fitting a parsnip model with this engine directly, the formula method is re -```r +``` r library(tidymodels) tidymodels_prefer() @@ -72,7 +72,7 @@ poisson_reg() %>% However, when using a workflow, the best approach is to avoid using [workflows::add_formula()] and use [workflows::add_variables()] in conjunction with a model formula: -```r +``` r data("bioChemists", package = "pscl") spec <- poisson_reg() %>% diff --git a/man/rmd/poisson_reg_stan.md b/man/rmd/poisson_reg_stan.md index 73dda6395..eea6becd8 100644 --- a/man/rmd/poisson_reg_stan.md +++ b/man/rmd/poisson_reg_stan.md @@ -25,7 +25,7 @@ See [rstan::sampling()] and [rstanarm::priors()] for more information on these a The **poissonreg** extension package is required to fit this model. -```r +``` r library(poissonreg) poisson_reg() %>% diff --git a/man/rmd/poisson_reg_stan_glmer.md b/man/rmd/poisson_reg_stan_glmer.md index a02cf3d43..38c5eac37 100644 --- a/man/rmd/poisson_reg_stan_glmer.md +++ b/man/rmd/poisson_reg_stan_glmer.md @@ -25,7 +25,7 @@ See `?rstanarm::stan_glmer` and `?rstan::sampling` for more information. The **multilevelmod** extension package is required to fit this model. -```r +``` r library(multilevelmod) poisson_reg(engine = "stan_glmer") %>% @@ -52,7 +52,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: diff --git a/man/rmd/poisson_reg_zeroinfl.md b/man/rmd/poisson_reg_zeroinfl.md index d976c857b..ccc82d1a1 100644 --- a/man/rmd/poisson_reg_zeroinfl.md +++ b/man/rmd/poisson_reg_zeroinfl.md @@ -12,7 +12,7 @@ This engine has no tuning parameters. The **poissonreg** extension package is required to fit this model. -```r +``` r library(poissonreg) poisson_reg() %>% @@ -44,7 +44,7 @@ When fitting a parsnip model with this engine directly, the formula method is re -```r +``` r library(tidymodels) tidymodels_prefer() @@ -73,7 +73,7 @@ poisson_reg() %>% However, when using a workflow, the best approach is to avoid using [workflows::add_formula()] and use [workflows::add_variables()] in conjunction with a model formula: -```r +``` r data("bioChemists", package = "pscl") spec <- poisson_reg() %>% diff --git a/man/rmd/proportional_hazards_glmnet.md b/man/rmd/proportional_hazards_glmnet.md index 424e33406..7d941eb99 100644 --- a/man/rmd/proportional_hazards_glmnet.md +++ b/man/rmd/proportional_hazards_glmnet.md @@ -24,7 +24,7 @@ The `penalty` parameter has no default and requires a single numeric value. For The **censored** extension package is required to fit this model. -```r +``` r library(censored) proportional_hazards(penalty = double(1), mixture = double(1)) %>% @@ -68,7 +68,7 @@ For example, in this model, the numeric column `rx` is used to estimate two diff -```r +``` r library(survival) library(censored) library(dplyr) diff --git a/man/rmd/proportional_hazards_survival.md b/man/rmd/proportional_hazards_survival.md index f50970545..90b94e77d 100644 --- a/man/rmd/proportional_hazards_survival.md +++ b/man/rmd/proportional_hazards_survival.md @@ -12,7 +12,7 @@ This model has no tuning parameters. The **censored** extension package is required to fit this model. -```r +``` r library(censored) proportional_hazards() %>% @@ -42,7 +42,7 @@ The model formula can include _special_ terms, such as [survival::strata()]. The For example, in this model, the numeric column `rx` is used to estimate two different baseline hazards for each value of the column: -```r +``` r library(survival) proportional_hazards() %>% diff --git a/man/rmd/rand_forest_aorsf.md b/man/rmd/rand_forest_aorsf.md index df77774b8..3dd2d5b1d 100644 --- a/man/rmd/rand_forest_aorsf.md +++ b/man/rmd/rand_forest_aorsf.md @@ -1,7 +1,7 @@ -For this engine, there are multiple modes: censored regression, classification, and regression +For this engine, there are multiple modes: classification, regression, and censored regression ## Tuning Parameters @@ -9,12 +9,12 @@ For this engine, there are multiple modes: censored regression, classification, This model has 3 tuning parameters: +- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors))) + - `trees`: # Trees (type: integer, default: 500L) - `min_n`: Minimal Node Size (type: integer, default: 5L) -- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors))) - Additionally, this model has one engine-specific tuning parameter: * `split_min_stat`: Minimum test statistic required to split a node. Defaults are `3.841459` for censored regression (which is roughly a p-value of 0.05) and `0` for classification and regression. For classification, this tuning parameter should be between 0 and 1, and for regression it should be greater than or equal to 0. Higher values of this parameter cause trees grown by `aorsf` to have less depth. @@ -24,7 +24,7 @@ Additionally, this model has one engine-specific tuning parameter: The **censored** extension package is required to fit this model. -```r +``` r library(censored) rand_forest() %>% @@ -47,7 +47,7 @@ rand_forest() %>% The **bonsai** extension package is required to fit this model. -```r +``` r library(bonsai) rand_forest() %>% @@ -71,7 +71,7 @@ rand_forest() %>% The **bonsai** extension package is required to fit this model. -```r +``` r library(bonsai) rand_forest() %>% diff --git a/man/rmd/rand_forest_h2o.md b/man/rmd/rand_forest_h2o.md index 4a5bc5b95..5f32cb475 100644 --- a/man/rmd/rand_forest_h2o.md +++ b/man/rmd/rand_forest_h2o.md @@ -22,7 +22,7 @@ This model has 3 tuning parameters: [agua::h2o_train_rf()] is a wrapper around [h2o::h2o.randomForest()]. -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), @@ -54,7 +54,7 @@ rand_forest( ## Translation from parsnip to the original package (classification) -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), diff --git a/man/rmd/rand_forest_partykit.md b/man/rmd/rand_forest_partykit.md index c1d87bd3e..7204c7c97 100644 --- a/man/rmd/rand_forest_partykit.md +++ b/man/rmd/rand_forest_partykit.md @@ -1,7 +1,7 @@ -For this engine, there are multiple modes: censored regression, regression, and classification +For this engine, there are multiple modes: regression, classification, and censored regression ## Tuning Parameters @@ -9,18 +9,18 @@ For this engine, there are multiple modes: censored regression, regression, and This model has 3 tuning parameters: -- `trees`: # Trees (type: integer, default: 500L) - - `min_n`: Minimal Node Size (type: integer, default: 20L) - `mtry`: # Randomly Selected Predictors (type: integer, default: 5L) +- `trees`: # Trees (type: integer, default: 500L) + ## Translation from parsnip to the original package (regression) The **bonsai** extension package is required to fit this model. -```r +``` r library(bonsai) rand_forest() %>% @@ -44,7 +44,7 @@ rand_forest() %>% The **bonsai** extension package is required to fit this model. -```r +``` r library(bonsai) rand_forest() %>% @@ -70,7 +70,7 @@ rand_forest() %>% The **censored** extension package is required to fit this model. -```r +``` r library(censored) rand_forest() %>% diff --git a/man/rmd/rand_forest_randomForest.md b/man/rmd/rand_forest_randomForest.md index bd0f76427..0d4b04213 100644 --- a/man/rmd/rand_forest_randomForest.md +++ b/man/rmd/rand_forest_randomForest.md @@ -22,7 +22,7 @@ This model has 3 tuning parameters: ## Translation from parsnip to the original package (regression) -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), @@ -54,7 +54,7 @@ rand_forest( ## Translation from parsnip to the original package (classification) -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), diff --git a/man/rmd/rand_forest_ranger.md b/man/rmd/rand_forest_ranger.md index c4e20f1d1..50420f527 100644 --- a/man/rmd/rand_forest_ranger.md +++ b/man/rmd/rand_forest_ranger.md @@ -22,7 +22,7 @@ This model has 3 tuning parameters: ## Translation from parsnip to the original package (regression) -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), @@ -55,7 +55,7 @@ rand_forest( ## Translation from parsnip to the original package (classification) -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), diff --git a/man/rmd/rand_forest_spark.md b/man/rmd/rand_forest_spark.md index 3753b3a3f..30d7fbc4c 100644 --- a/man/rmd/rand_forest_spark.md +++ b/man/rmd/rand_forest_spark.md @@ -20,7 +20,7 @@ This model has 3 tuning parameters: ## Translation from parsnip to the original package (regression) -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), @@ -53,7 +53,7 @@ rand_forest( ## Translation from parsnip to the original package (classification) -```r +``` r rand_forest( mtry = integer(1), trees = integer(1), diff --git a/man/rmd/rule_fit_h2o.md b/man/rmd/rule_fit_h2o.md index 06c5d3d37..bffacfb44 100644 --- a/man/rmd/rule_fit_h2o.md +++ b/man/rmd/rule_fit_h2o.md @@ -35,7 +35,7 @@ Other engine arguments of interest: The **agua** extension package is required to fit this model. -```r +``` r library(rules) rule_fit( @@ -73,7 +73,7 @@ rule_fit( The **agua** extension package is required to fit this model. -```r +``` r rule_fit( trees = integer(1), tree_depth = integer(1), diff --git a/man/rmd/rule_fit_xrf.md b/man/rmd/rule_fit_xrf.md index 60f7caa35..39cd4919e 100644 --- a/man/rmd/rule_fit_xrf.md +++ b/man/rmd/rule_fit_xrf.md @@ -31,7 +31,7 @@ This model has 8 tuning parameters: The **rules** extension package is required to fit this model. -```r +``` r library(rules) rule_fit( @@ -78,7 +78,7 @@ The **rules** extension package is required to fit this model. -```r +``` r library(rules) rule_fit( diff --git a/man/rmd/survival_reg_flexsurv.md b/man/rmd/survival_reg_flexsurv.md index c436a2e44..f99705d92 100644 --- a/man/rmd/survival_reg_flexsurv.md +++ b/man/rmd/survival_reg_flexsurv.md @@ -16,7 +16,7 @@ This model has 1 tuning parameters: The **censored** extension package is required to fit this model. -```r +``` r library(censored) survival_reg(dist = character(1)) %>% diff --git a/man/rmd/survival_reg_flexsurvspline.md b/man/rmd/survival_reg_flexsurvspline.md index bb9cbee66..b7bfb41cb 100644 --- a/man/rmd/survival_reg_flexsurvspline.md +++ b/man/rmd/survival_reg_flexsurvspline.md @@ -14,7 +14,7 @@ This model has one engine-specific tuning parameter: The **censored** extension package is required to fit this model. -```r +``` r library(censored) survival_reg() %>% diff --git a/man/rmd/survival_reg_survival.md b/man/rmd/survival_reg_survival.md index bbd45bcec..ea6c8eabe 100644 --- a/man/rmd/survival_reg_survival.md +++ b/man/rmd/survival_reg_survival.md @@ -16,7 +16,7 @@ This model has 1 tuning parameters: The **censored** extension package is required to fit this model. -```r +``` r library(censored) survival_reg(dist = character(1)) %>% @@ -49,7 +49,7 @@ The model formula can include _special_ terms, such as [survival::strata()]. The For example, in this model, the numeric column `rx` is used to estimate two different scale parameters for each value of the column: -```r +``` r library(survival) survival_reg() %>% diff --git a/man/rmd/svm_linear_LiblineaR.md b/man/rmd/svm_linear_LiblineaR.md index 72ff0f300..03be20d7e 100644 --- a/man/rmd/svm_linear_LiblineaR.md +++ b/man/rmd/svm_linear_LiblineaR.md @@ -15,10 +15,12 @@ This model has 2 tuning parameters: This engine fits models that are L2-regularized for L2-loss. In the [LiblineaR::LiblineaR()] documentation, these are types 1 (classification) and 11 (regression). +Parsnip changes the default range for `cost` to `c(-10, 5)`. + ## Translation from parsnip to the original package (regression) -```r +``` r svm_linear( cost = double(1), margin = double(1) @@ -45,7 +47,7 @@ svm_linear( ## Translation from parsnip to the original package (classification) -```r +``` r svm_linear( cost = double(1) ) %>% diff --git a/man/rmd/svm_linear_kernlab.md b/man/rmd/svm_linear_kernlab.md index 02a41ef31..ecf2ceac8 100644 --- a/man/rmd/svm_linear_kernlab.md +++ b/man/rmd/svm_linear_kernlab.md @@ -13,10 +13,12 @@ This model has 2 tuning parameters: - `margin`: Insensitivity Margin (type: double, default: 0.1) +Parsnip changes the default range for `cost` to `c(-10, 5)`. + ## Translation from parsnip to the original package (regression) -```r +``` r svm_linear( cost = double(1), margin = double(1) @@ -43,7 +45,7 @@ svm_linear( ## Translation from parsnip to the original package (classification) -```r +``` r svm_linear( cost = double(1) ) %>% diff --git a/man/rmd/svm_poly_kernlab.md b/man/rmd/svm_poly_kernlab.md index cdc80cc9b..857909eb4 100644 --- a/man/rmd/svm_poly_kernlab.md +++ b/man/rmd/svm_poly_kernlab.md @@ -17,10 +17,12 @@ This model has 4 tuning parameters: - `margin`: Insensitivity Margin (type: double, default: 0.1) +Parsnip changes the default range for `cost` to `c(-10, 5)`. + ## Translation from parsnip to the original package (regression) -```r +``` r svm_poly( cost = double(1), degree = integer(1), @@ -52,7 +54,7 @@ svm_poly( ## Translation from parsnip to the original package (classification) -```r +``` r svm_poly( cost = double(1), degree = integer(1), diff --git a/man/rmd/svm_rbf_kernlab.md b/man/rmd/svm_rbf_kernlab.md index dc311a7c1..1e199475a 100644 --- a/man/rmd/svm_rbf_kernlab.md +++ b/man/rmd/svm_rbf_kernlab.md @@ -17,10 +17,12 @@ This model has 3 tuning parameters: There is no default for the radial basis function kernel parameter. kernlab estimates it from the data using a heuristic method. See [kernlab::sigest()]. This method uses random numbers so, without setting the seed before fitting, the model will not be reproducible. +Parsnip changes the default range for `cost` to `c(-10, 5)`. + ## Translation from parsnip to the original package (regression) -```r +``` r svm_rbf( cost = double(1), rbf_sigma = double(1), @@ -49,7 +51,7 @@ svm_rbf( ## Translation from parsnip to the original package (classification) -```r +``` r svm_rbf( cost = double(1), rbf_sigma = double(1) diff --git a/man/rmd/template-no-pooling.Rmd b/man/rmd/template-no-pooling.Rmd index 35263c68f..b6b8ef2f1 100644 --- a/man/rmd/template-no-pooling.Rmd +++ b/man/rmd/template-no-pooling.Rmd @@ -6,7 +6,7 @@ This model can use subject-specific coefficient estimates to make predictions (i \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1} ``` -where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. +where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: