Help documentation updates

Author: irkaal

R/aaa_models.R

@@ -329,7 +329,7 @@ check_interface_val <- function(x) {
 #' @keywords internal
 #' @details These functions are available for users to add their
 #'  own models or engines (in package or otherwise) so that they can
-#'  be accessed using `parsnip`. This are more thoroughly documented
+#'  be accessed using `parsnip`. This is more thoroughly documented
 #'  on the package web site (see references below).
 #'
 #' In short, `parsnip` stores an environment object that contains

R/boost_tree.R

@@ -11,7 +11,7 @@
 #'   randomly sampled at each split when creating the tree models.
 #'   \item \code{trees}: The number of trees contained in the ensemble.
 #'   \item \code{min_n}: The minimum number of data points in a node
-#'   that are required for the node to be split further.
+#'   that is required for the node to be split further.
 #'   \item \code{tree_depth}: The maximum depth of the tree (i.e. number of
 #'  splits).
 #'   \item \code{learn_rate}: The rate at which the boosting algorithm adapts
@@ -23,10 +23,10 @@
 #'   stopping.
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
-#'  set using the  `set_engine()` function. If left to their defaults
+#'  time that the model is fit. Other options and arguments can be
+#'  set using the `set_engine()` function. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
-#'  functions.  If parameters need to be modified, `update()` can be used
+#'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
 #'
 #' @param mode A single character string for the type of model.
@@ -38,18 +38,18 @@
 #' @param trees An integer for the number of trees contained in
 #'  the ensemble.
 #' @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.
+#'  in a node that is required for the node to be split further.
 #' @param tree_depth An integer for the maximum depth of the tree (i.e. number
 #'  of splits) (`xgboost` only).
 #' @param learn_rate A number for the rate at which the boosting algorithm adapts
 #'   from iteration-to-iteration (`xgboost` only).
 #' @param loss_reduction A number for the reduction in the loss function required
-#'   to split further  (`xgboost` only).
+#'   to split further (`xgboost` only).
 #' @param sample_size A number for the number (or proportion) of data that is
-#'  exposed to the fitting routine. For `xgboost`, the sampling is done at at
+#'  exposed to the fitting routine. For `xgboost`, the sampling is done at
 #'  each iteration while `C5.0` samples once during training.
 #' @param stop_iter The number of iterations without improvement before
-#'   stopping  (`xgboost` only).
+#'   stopping (`xgboost` only).
 #' @details
 #' The data given to the function are not saved and are only used
 #'  to determine the _mode_ of the model. For `boost_tree()`, the
@@ -58,7 +58,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"xgboost"` (the default), `"C5.0"`
+#' \item \pkg{R}: `"xgboost"` (the default), `"C5.0"`
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'
@@ -278,7 +278,7 @@ check_args.boost_tree <- function(object) {
 #' of training set samples use for these purposes.
 #' @param early_stop An integer or `NULL`. If not `NULL`, it is the number of
 #' training iterations without improvement before stopping. If `validation` is
-#' used, performance is base on the validation set; otherwise the training set
+#' used, performance is base on the validation set; otherwise, the training set
 #' is used.
 #' @param ... Other options to pass to `xgb.train`.
 #' @return A fitted `xgboost` object.

R/decision_tree.R

@@ -15,7 +15,7 @@
 #'   that are required for the node to be split further.
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used
@@ -34,7 +34,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"rpart"` (the default) or `"C5.0"` (classification only)
+#' \item \pkg{R}: `"rpart"` (the default) or `"C5.0"` (classification only)
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'
@@ -206,7 +206,7 @@ check_args.decision_tree <- function(object) {
 #'  this means that the overall R-squared must increase by `cp` at
 #'  each step. The main role of this parameter is to save computing
 #'  time by pruning off splits that are obviously not worthwhile.
-#'  Essentially,the user informs the program that any split which
+#'  Essentially, the user informs the program that any split which
 #'  does not improve the fit by `cp` will likely be pruned off by
 #'  cross-validation, and that hence the program need not pursue it.
 #' @param weights Optional case weights.

R/descriptors.R

@@ -10,11 +10,11 @@
 #' Existing functions:
 #'   \itemize{
 #'   \item `.obs()`: The current number of rows in the data set.
-#'   \item `.preds()`: The number of columns in the data set that are
+#'   \item `.preds()`: The number of columns in the data set that is
 #'     associated with the predictors prior to dummy variable creation.
 #'   \item `.cols()`: The number of predictor columns available after dummy
 #'     variables are created (if any).
-#'   \item `.facts()`: The number of factor predictors in the dat set.
+#'   \item `.facts()`: The number of factor predictors in the data set.
 #'   \item `.lvls()`: If the outcome is a factor, this is a table
 #'     with the counts for each level (and `NA` otherwise).
 #'   \item `.x()`: The predictors returned in the format given. Either a

R/fit.R

@@ -24,11 +24,11 @@
 #'  ignored. Other options required to fit the model should be
 #'  passed using `set_engine()`.
 #' @details  `fit()` and `fit_xy()` substitute the current arguments in the model
-#'  specification into the computational engine's code, checks them
-#'  for validity, then fits the model using the data and the
+#'  specification into the computational engine's code, check them
+#'  for validity, then fit the model using the data and the
 #'  engine-specific code. Different model functions have different
 #'  interfaces (e.g. formula or `x`/`y`) and these functions translate
-#'  between the interface used when `fit()` or `fit_xy()` were invoked and the one
+#'  between the interface used when `fit()` or `fit_xy()` was invoked and the one
 #'  required by the underlying model.
 #'
 #' When possible, these functions attempt to avoid making copies of the

R/linear_reg.R

@@ -11,7 +11,7 @@
 #'   regularization (see below). Note that this will be ignored for some engines.
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used

R/logistic_reg.R

@@ -11,7 +11,7 @@
 #'   regularization (see below). Note that this will be ignored for some engines.
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used

R/mars.R

@@ -10,13 +10,13 @@
 #'   \item \code{num_terms}: The number of features that will be retained in the
 #'    final model.
 #'   \item \code{prod_degree}: The highest possible degree of interaction between
-#'    features. A value of 1 indicates and additive model while a value of 2
+#'    features. A value of 1 indicates an additive model while a value of 2
 #'    allows, but does not guarantee, two-way interactions between features.
 #'   \item \code{prune_method}: The type of pruning. Possible values are listed
 #'    in `?earth`.
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used

R/mlp.R

@@ -17,7 +17,7 @@
 #' }
 #'
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (see above), the values are taken from the underlying model
 #'  functions. One exception is `hidden_units` when `nnet::nnet` is used; that

R/model_object_docs.R

@@ -7,7 +7,7 @@
 #' The main elements of the object are:
 #'
 #' * `args`: A vector of the main arguments for the model. The
-#'  names of these arguments may be different form their
+#'  names of these arguments may be different from their
 #'  counterparts n the underlying model function. For example, for a
 #'  `glmnet` model, the argument name for the amount of the penalty
 #'  is called "penalty" instead of "lambda" to make it more general
@@ -146,7 +146,7 @@ NULL
 #' The main elements of the object are:
 #'
 #'   * `lvl`: A vector of factor levels when the outcome is
-#'  is a factor. This is `NULL` when the outcome is not a factor
+#'  a factor. This is `NULL` when the outcome is not a factor
 #'  vector.
 #'
 #'   * `spec`: A `model_spec` object.
@@ -169,7 +169,7 @@ NULL
 #'  specification.
 #'
 #' This class and structure is the basis for how \pkg{parsnip}
-#'  stores model objects after to seeing the data and applying a model.
+#'  stores model objects after seeing the data and applying a model.
 #' @rdname model_fit
 #' @name model_fit
 #' @examples

R/multinom_reg.R

@@ -11,7 +11,7 @@
 #'   regularization (see below). Note that this will be ignored for some engines.
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used

R/nearest_neighbor.R

@@ -18,7 +18,7 @@
 #'   and the Euclidean distance with `dist_power = 2`.
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used

R/nullmodel.R

@@ -29,7 +29,7 @@
 #' the training samples with that class (the other columns are zero). } \item{n
 #' }{the number of elements in \code{y}}
 #'
-#' `predict.nullmodel()` returns a either a factor or numeric vector
+#' `predict.nullmodel()` returns either a factor or numeric vector
 #' depending on the class of \code{y}. All predictions are always the same.
 #' @keywords models
 #' @examples

R/predict_interval.R

@@ -2,7 +2,7 @@
 #' @rdname other_predict
 #' @param level A single numeric value between zero and one for the
 #'  interval estimates.
-#' @param std_error A single logical for wether the standard error should be
+#' @param std_error A single logical for whether the standard error should be
 #'  returned (assuming that the model can compute it).
 #' @inheritParams predict.model_fit
 #' @method predict_confint model_fit

R/surv_reg.R

@@ -8,7 +8,7 @@
 #'   \item \code{dist}: The probability distribution of the outcome.
 #' }
 #' This argument is converted to its specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to its default
 #'  here (`NULL`), the value is taken from the underlying model
 #'  functions.

R/svm_poly.R

@@ -13,7 +13,7 @@
 #'    (regression only)
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used
@@ -28,7 +28,7 @@
 #' @param degree A positive number for polynomial degree.
 #' @param scale_factor A positive number for the polynomial scaling factor.
 #' @param margin A positive number for the epsilon in the SVM insensitive
-#'   loss function (regression only)
+#'  loss function (regression only)
 #' @details
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:

R/svm_rbf.R

@@ -13,7 +13,7 @@
 #'    (regression only)
 #' }
 #' These arguments are converted to their specific names at the
-#'  time that the model is fit. Other options and argument can be
+#'  time that the model is fit. Other options and arguments can be
 #'  set using `set_engine()`. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used
@@ -27,7 +27,7 @@
 #'  or on the wrong side of the margin
 #' @param rbf_sigma A positive number for radial basis function.
 #' @param margin A positive number for the epsilon in the SVM insensitive
-#'   loss function (regression only)
+#'  loss function (regression only)
 #' @details
 #' The model can be created using the `fit()` function using the
 #'  following _engines_: