Merge pull request #294 from tidymodels/no-sexpr

removed Sexpr and use includeRmd

Author: topepo

.travis.yml

@@ -59,38 +59,7 @@
 #' }
 #'
 #'
-#' @section Engine Details:
-#'
-#' The standardized parameter names in parsnip can be mapped to their original
-#' names in each engine:
-#'
-#' ```{r echo = FALSE}
-#' convert_args("boost_tree")
-#' ```
-#'
-#' Engines may have pre-set default arguments when executing the
-#'  model fit call.  For this type of model, the template of the
-#'  fit calls are:
-#'
-#' \pkg{xgboost} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "xgboost")}
-#'
-#' \pkg{xgboost} regression
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "regression"), "xgboost")}
-#'
-#' \pkg{C5.0} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "C5.0")}
-#'
-#' \pkg{spark} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "spark")}
-#'
-#' \pkg{spark} regression
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "regression"), "spark")}
+#' @includeRmd man/rmd/boost-tree.Rmd details
 #'
 #' @note For models created using the spark engine, there are
 #'  several differences to consider. First, only the formula
@@ -106,7 +75,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()], [set_engine()]
+#' @seealso [fit()], [set_engine()]
 #' @examples
 #' boost_tree(mode = "classification", trees = 20)
 #' # Parameters can be represented by a placeholder:

R/boost_tree.R

@@ -44,38 +44,7 @@
 #'  greater than 30 `rpart` will give nonsense results on 32-bit
 #'  machines.
 #'
-#' @section Engine Details:
-#'
-#' The standardized parameter names in parsnip can be mapped to their original
-#' names in each engine:
-#'
-#' ```{r echo = FALSE}
-#' convert_args("decision_tree")
-#' ```
-#'
-#' Engines may have pre-set default arguments when executing the
-#'  model fit call. For this type of
-#'  model, the template of the fit calls are::
-#'
-#' \pkg{rpart} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "rpart")}
-#'
-#' \pkg{rpart} regression
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "regression"), "rpart")}
-#'
-#' \pkg{C5.0} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "C5.0")}
-#'
-#' \pkg{spark} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "spark")}
-#'
-#' \pkg{spark} regression
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "regression"), "spark")}
+#' @includeRmd man/rmd/decision-tree.Rmd details
 #'
 #' @note For models created using the spark engine, there are
 #'  several differences to consider. First, only the formula
@@ -91,7 +60,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' decision_tree(mode = "classification", tree_depth = 5)
 #' # Parameters can be represented by a placeholder:

R/decision_tree.R

@@ -42,57 +42,7 @@
 #' \item \pkg{keras}: `"keras"`
 #' }
 #'
-#' @section Engine Details:
-#'
-#' The standardized parameter names in parsnip can be mapped to their original
-#' names in each engine:
-#'
-#' ```{r echo = FALSE}
-#' convert_args("linear_reg")
-#' ```
-#'
-#' Engines may have pre-set default arguments when executing the
-#'  model fit call. For this type of
-#'  model, the template of the fit calls are:
-#'
-#' \pkg{lm}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "lm")}
-#'
-#' \pkg{glmnet}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "glmnet")}
-#'
-#' \pkg{stan}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "stan")}
-#'
-#' (note that the `refresh` default prevents logging of the estimation process. Change this value in `set_engine()` will show the logs)
-#'
-#' \pkg{spark}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "spark")}
-#'
-#' \pkg{keras}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "keras")}
-#'
-#' For `glmnet` models, the full regularization path is always fit regardless
-#' of the value given to `penalty`. Also, there is the option to pass
-#'  multiple values (or no values) to the `penalty` argument. When using the
-#'  `predict()` method in these cases, the return value depends on
-#'  the value of `penalty`. When using `predict()`, only a single
-#'  value of the penalty can be used. When predicting on multiple
-#'  penalties, the `multi_predict()` function can be used. It
-#'  returns a tibble with a list column called `.pred` that contains
-#'  a tibble with all of the penalty results.
-#'
-#' For prediction, the `stan` engine can compute posterior
-#'  intervals analogous to confidence and prediction intervals. In
-#'  these instances, the units are the original outcome and when
-#'  `std_error = TRUE`, the standard deviation of the posterior
-#'  distribution (or posterior predictive distribution as
-#'  appropriate) is returned.
+#' @includeRmd man/rmd/linear-reg.Rmd details
 #'
 #' @note For models created using the spark engine, there are
 #'  several differences to consider. First, only the formula

R/linear_reg.R

@@ -40,58 +40,7 @@
 #' \item \pkg{keras}: `"keras"`
 #' }
 #'
-#' @section Engine Details:
-#'
-#' The standardized parameter names in parsnip can be mapped to their original
-#' names in each engine:
-#'
-#' ```{r echo = FALSE}
-#' convert_args("logistic_reg")
-#' ```
-#'
-#' Engines may have pre-set default arguments when executing the
-#'  model fit call.  For this type of
-#'  model, the template of the fit calls are:
-#'
-#' \pkg{glm}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "glm")}
-#'
-#' \pkg{glmnet}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "glmnet")}
-#'
-#' \pkg{stan}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "stan")}
-#'
-#' (note that the `refresh` default prevents logging of the estimation process. Change this value in `set_engine()` will show the logs)
-#'
-#' \pkg{spark}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "spark")}
-#'
-#' \pkg{keras}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "keras")}
-#'
-#' For `glmnet` models, the full regularization path is always fit regardless
-#' of the value given to `penalty`. Also, there is the option to pass
-#'  multiple values (or no values) to the `penalty` argument. When using the
-#'  `predict()` method in these cases, the return value depends on
-#'  the value of `penalty`. When using `predict()`, only a single
-#'  value of the penalty can be used. When predicting on multiple
-#'  penalties, the `multi_predict()` function can be used. It
-#'  returns a tibble with a list column called `.pred` that contains
-#'  a tibble with all of the penalty results.
-#'
-#' For prediction, the `stan` engine can compute posterior
-#'  intervals analogous to confidence and prediction intervals. In
-#'  these instances, the units are the original outcome and when
-#'  `std_error = TRUE`, the standard deviation of the posterior
-#'  distribution (or posterior predictive distribution as
-#'  appropriate) is returned. For `glm`, the standard error is in logit units
-#'  while the intervals are in probability units.
+#' @includeRmd man/rmd/logistic-reg.Rmd details
 #'
 #' @note For models created using the spark engine, there are
 #'  several differences to consider. First, only the formula

R/logistic_reg.R

@@ -36,30 +36,7 @@
 #' \item \pkg{R}:  `"earth"`  (the default)
 #' }
 #'
-#' @section Engine Details:
-#'
-#' The standardized parameter names in parsnip can be mapped to their original
-#' names in each engine:
-#'
-#' ```{r echo = FALSE}
-#' convert_args("mars")
-#' ```
-#'
-#' Engines may have pre-set default arguments when executing the
-#'  model fit call.  For this type of
-#'  model, the template of the fit calls are:
-#'
-#' \pkg{earth} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mars(mode = "classification"), "earth")}
-#'
-#' \pkg{earth} regression
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mars(mode = "regression"), "earth")}
-#'
-#' Note that, when the model is fit, the \pkg{earth} package only has its
-#'  namespace loaded. However, if `multi_predict` is used, the package is
-#'  attached.
+#' @includeRmd man/rmd/mars.Rmd details
 #'
 #' @importFrom purrr map_lgl
 #' @seealso [fit()]

R/mars.R

@@ -51,37 +51,7 @@
 #' \item \pkg{keras}: `"keras"`
 #' }
 #'
-#'  An error is thrown if both `penalty` and `dropout` are specified for
-#'  `keras` models.
-#'
-#' @section Engine Details:
-#'
-#' The standardized parameter names in parsnip can be mapped to their original
-#' names in each engine:
-#'
-#' ```{r echo = FALSE}
-#' convert_args("mlp")
-#' ```
-#'
-#' Engines may have pre-set default arguments when executing the
-#'  model fit call. For this type of
-#'  model, the template of the fit calls are:
-#'
-#' \pkg{keras} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "classification"), "keras")}
-#'
-#' \pkg{keras} regression
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "keras")}
-#'
-#' \pkg{nnet} classification
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "classification"), "nnet")}
-#'
-#' \pkg{nnet} regression
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "nnet")}
+#' @includeRmd man/rmd/mlp.Rmd details
 #'
 #' @importFrom purrr map_lgl
 #' @seealso [fit()]

R/mlp.R

@@ -39,44 +39,7 @@
 #' \item \pkg{keras}: `"keras"`
 #' }
 #'
-#' @section Engine Details:
-#'
-#' The standardized parameter names in parsnip can be mapped to their original
-#' names in each engine:
-#'
-#' ```{r echo = FALSE}
-#' convert_args("multinom_reg")
-#' ```
-#'
-#' Engines may have pre-set default arguments when executing the
-#'  model fit call.  For this type of
-#'  model, the template of the fit calls are:
-#'
-#' \pkg{glmnet}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "glmnet")}
-#'
-#' \pkg{nnet}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "nnet")}
-#'
-#' \pkg{spark}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "spark")}
-#'
-#' \pkg{keras}
-#'
-#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "keras")}
-#'
-#' For `glmnet` models, the full regularization path is always fit regardless
-#' of the value given to `penalty`. Also, there is the option to pass
-#'  multiple values (or no values) to the `penalty` argument. When using the
-#'  `predict()` method in these cases, the return value depends on
-#'  the value of `penalty`. When using `predict()`, only a single
-#'  value of the penalty can be used. When predicting on multiple
-#'  penalties, the `multi_predict()` function can be used. It
-#'  returns a tibble with a list column called `.pred` that contains
-#'  a tibble with all of the penalty results.
+#' @includeRmd man/rmd/multinom-reg.Rmd details
 #'
 #' @note For models created using the spark engine, there are
 #'  several differences to consider. First, only the formula