document that main arguments are captured as quosures

R/auto_ml.R

@@ -17,6 +17,7 @@
 #' @param engine A single character string specifying what computational engine
 #'  to use for fitting.
 #'
+#' @templateVar modeltype auto_ml
 #' @template spec-details
 #'
 #' @template spec-references

R/bag_mars.R

@@ -14,6 +14,7 @@
 #'
 #' @inheritParams mars
 #'
+#' @templateVar modeltype bag_mars
 #' @template spec-details
 #'
 #' @template spec-references

R/bag_mlp.R

@@ -12,6 +12,7 @@
 #'
 #' @inheritParams mlp
 #'
+#' @templateVar modeltype bag_mlp
 #' @template spec-details
 #'
 #' @template spec-references

R/bag_tree.R

@@ -16,6 +16,7 @@
 #' factor is the minority class. If this is not the case, values between zero
 #' and one can be used to bias to the second level of the factor.
 #'
+#' @templateVar modeltype bag_tree
 #' @template spec-details
 #'
 #' @template spec-references

R/bart.R

@@ -35,6 +35,7 @@
 #' terminal node for different values of these parameters.
 #'
 #'
+#' @templateVar modeltype bart
 #' @template spec-details
 #'
 #' @template spec-references

R/boost_tree.R

@@ -39,6 +39,7 @@
 #' @param stop_iter The number of iterations without improvement before
 #'   stopping (specific engines only).
 #'
+#' @templateVar modeltype boost_tree
 #' @template spec-details
 #'
 #' @template spec-references

R/c5_rules.R

@@ -30,6 +30,8 @@
 #'
 #' @references Quinlan R (1993). _C4.5: Programs for Machine Learning_. Morgan
 #' Kaufmann Publishers.
+#'
+#' @templateVar modeltype C5_rules
 #' @template spec-details
 #'
 #' @template spec-references

R/cubist_rules.R

@@ -50,6 +50,7 @@
 #' where `t` is the training set prediction and `w` is a weight that is inverse
 #'  to the distance to the neighbor.
 #'
+#' @templateVar modeltype cubist_rules
 #' @template spec-details
 #'
 #' @template spec-references

R/decision_tree.R

@@ -19,6 +19,7 @@
 #' @param min_n An integer for the minimum number of data points
 #'  in a node that are required for the node to be split further.
 #'
+#' @templateVar modeltype decision_tree
 #' @template spec-details
 #'
 #' @template spec-references

R/discrim_flexible.R

@@ -18,6 +18,7 @@
 #' @param prod_degree The highest possible interaction degree.
 #' @param prune_method The pruning method.
 #'
+#' @templateVar modeltype discrim_flexible
 #' @template spec-details
 #'
 #' @template spec-references

R/discrim_linear.R

@@ -22,6 +22,7 @@
 #'  estimation. Possible values are: "`diagonal`", "`min_distance`",
 #'  "`shrink_cov`", and "`shrink_mean`" (`sparsediscrim` engine only).
 #'
+#' @templateVar modeltype discrim_linear
 #' @template spec-details
 #'
 #' @template spec-references

R/discrim_quad.R

@@ -20,6 +20,7 @@
 #'  estimation. Possible values are: "`diagonal`", "`shrink_cov`", and
 #'  "`shrink_mean`" (`sparsediscrim` engine only).
 #'
+#' @templateVar modeltype discrim_quad
 #' @template spec-details
 #'
 #' @template spec-references

R/discrim_regularized.R

@@ -33,6 +33,7 @@
 #'  used. Other regularization methods can be used with [discrim_linear()] and
 #'  [discrim_quad()] can used via the `sparsediscrim` engine for those functions.
 #'
+#' @templateVar modeltype discrim_regularized
 #' @template spec-details
 #'
 #' @template spec-references

R/gen_additive_mod.R

@@ -17,6 +17,7 @@
 #' @param adjust_deg_free If `select_features = TRUE`, then acts as a multiplier
 #'  for smoothness. Increase this beyond 1 to produce smoother models.
 #'
+#' @templateVar modeltype gen_additive_mod
 #' @template spec-details
 #'
 #' @template spec-references

R/linear_reg.R

@@ -26,6 +26,7 @@
 #'
 #'  Available for specific engines only.
 #'
+#' @templateVar modeltype linear_reg
 #' @template spec-details
 #'
 #' @template spec-references

R/logistic_reg.R

@@ -30,6 +30,7 @@
 #'  Available for specific engines only. For `LiblineaR` models, `mixture` must
 #'  be exactly 1 or 0 only.
 #'
+#' @templateVar modeltype logistic_reg
 #' @template spec-details
 #'
 #' @details This model fits a classification model for binary outcomes; for

R/mars.R

@@ -18,6 +18,7 @@
 #' @param prod_degree The highest possible interaction degree.
 #' @param prune_method The pruning method.
 #'
+#' @templateVar modeltype mars
 #' @template spec-details
 #'
 #' @template spec-references

R/mlp.R

@@ -23,6 +23,7 @@
 #'  "linear" or "softmax" depending on the type of outcome. Possible values are:
 #'  "linear", "softmax", "relu", and "elu"
 #'
+#' @templateVar modeltype mlp
 #' @template spec-details
 #'
 #' @template spec-references

R/multinom_reg.R

@@ -30,6 +30,7 @@
 #'
 #'  Available for specific engines only.
 #'
+#' @templateVar modeltype multinom_reg
 #' @template spec-details
 #'
 #' @details This model fits a classification model for multiclass outcomes; for

R/naive_Bayes.R

@@ -20,6 +20,7 @@
 #' @param Laplace A non-negative value for the Laplace correction to smoothing
 #' low-frequency counts.
 #'
+#' @templateVar modeltype naive_Bayes
 #' @template spec-details
 #'
 #' @template spec-references

R/nearest_neighbor.R

@@ -22,6 +22,7 @@
 #' @param dist_power A single number for the parameter used in
 #' calculating Minkowski distance.
 #'
+#' @templateVar modeltype nearest_neighbor
 #' @template spec-details
 #'
 #' @template spec-references

R/pls.R

@@ -16,6 +16,7 @@
 #'  This value is used for all PLS components for X.
 #' @param num_comp The number of PLS components to retain.
 #'
+#' @templateVar modeltype pls
 #' @template spec-details
 #'
 #' @template spec-references

R/poisson_reg.R

@@ -24,6 +24,7 @@
 #'
 #'  Available for `glmnet` and `spark` only.
 #'
+#' @templateVar modeltype poisson_reg
 #' @template spec-details
 #'
 #' @template spec-references

R/proportional_hazards.R

@@ -15,6 +15,7 @@
 #' @param mode A single character string for the prediction outcome mode.
 #'  The only possible value for this model is "censored regression".
 #'
+#' @templateVar modeltype proportional_hazards
 #' @template spec-details
 #'
 #' @template spec-survival

R/rand_forest.R

@@ -20,6 +20,7 @@
 #' @param min_n An integer for the minimum number of data points
 #'  in a node that are required for the node to be split further.
 #'
+#' @templateVar modeltype rand_forest
 #' @template spec-details
 #'
 #' @template spec-references

R/rule_fit.R

@@ -22,6 +22,7 @@
 #' @references Friedman, J. H., and Popescu, B. E. (2008). "Predictive learning
 #' via rule ensembles." _The Annals of Applied Statistics_, 2(3), 916-954.
 #'
+#' @templateVar modeltype rule_fit
 #' @template spec-details
 #'
 #' @template spec-references

R/surv_reg.R

@@ -20,6 +20,7 @@
 #' @param dist A character string for the probability distribution of the
 #'  outcome. The default is "weibull".
 #'
+#' @templateVar modeltype surv_reg
 #' @template spec-details
 #'
 #' @template spec-survival

R/survival_reg.R

@@ -16,6 +16,7 @@
 #'  outcome. The default is "weibull".
 #'
 #'
+#' @templateVar modeltype survival_reg
 #' @template spec-details
 #'
 #' @template spec-survival

R/svm_linear.R

@@ -19,6 +19,7 @@
 #' @param margin A positive number for the epsilon in the SVM insensitive
 #'  loss function (regression only)
 #'
+#' @templateVar modeltype svm_linear
 #' @template spec-details
 #'
 #' @template spec-references

R/svm_poly.R

@@ -22,6 +22,7 @@
 #' @param margin A positive number for the epsilon in the SVM insensitive
 #'  loss function (regression only)
 #'
+#' @templateVar modeltype svm_poly
 #' @template spec-details
 #'
 #' @template spec-references

R/svm_rbf.R

@@ -24,6 +24,7 @@
 #' @param margin A positive number for the epsilon in the SVM insensitive
 #'  loss function (regression only)
 #'
+#' @templateVar modeltype svm_rbf
 #' @template spec-details
 #'
 #' @template spec-references

man-roxygen/spec-details.R

@@ -6,3 +6,12 @@
 #'
 #' The model is not trained or fit until the [`fit()`][fit.model_spec()] function is used
 #' with the data.
+#'
+#' Each of the arguments in this function other than `mode` and `engine` are
+#' captured as [quosures][rlang::`topic-quosure`]. To pass values
+#' programmatically, use the [injection operator][rlang::`!!`] like so:
+#'
+#' ``` r
+#' value <- 1
+#' <%= modeltype %>(argument = !!value)
+#' ```