From 6f562b65a45a1b63abb8b615ef4d3dd0b4a11ab8 Mon Sep 17 00:00:00 2001 From: irkaal Date: Sun, 30 Aug 2020 12:01:10 -0700 Subject: [PATCH] Help documentation updates --- R/aaa_models.R | 2 +- R/boost_tree.R | 20 ++++++++++---------- R/decision_tree.R | 6 +++--- R/descriptors.R | 4 ++-- R/fit.R | 6 +++--- R/linear_reg.R | 2 +- R/logistic_reg.R | 2 +- R/mars.R | 4 ++-- R/mlp.R | 2 +- R/model_object_docs.R | 6 +++--- R/multinom_reg.R | 2 +- R/nearest_neighbor.R | 2 +- R/nullmodel.R | 2 +- R/predict_interval.R | 2 +- R/surv_reg.R | 2 +- R/svm_poly.R | 4 ++-- R/svm_rbf.R | 4 ++-- man/boost_tree.Rd | 18 +++++++++--------- man/decision_tree.Rd | 4 ++-- man/descriptors.Rd | 4 ++-- man/fit.Rd | 6 +++--- man/linear_reg.Rd | 2 +- man/logistic_reg.Rd | 2 +- man/mars.Rd | 4 ++-- man/mlp.Rd | 2 +- man/model_fit.Rd | 4 ++-- man/model_spec.Rd | 2 +- man/multinom_reg.Rd | 2 +- man/nearest_neighbor.Rd | 2 +- man/nullmodel.Rd | 2 +- man/other_predict.Rd | 2 +- man/rpart_train.Rd | 2 +- man/set_new_model.Rd | 2 +- man/surv_reg.Rd | 2 +- man/svm_poly.Rd | 2 +- man/svm_rbf.Rd | 2 +- man/xgb_train.Rd | 2 +- 37 files changed, 70 insertions(+), 70 deletions(-) diff --git a/R/aaa_models.R b/R/aaa_models.R index 29a879411..d814b1ecc 100644 --- a/R/aaa_models.R +++ b/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 diff --git a/R/boost_tree.R b/R/boost_tree.R index a005dbb64..517ac6dee 100644 --- a/R/boost_tree.R +++ b/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. diff --git a/R/decision_tree.R b/R/decision_tree.R index 015501971..7216ca95f 100644 --- a/R/decision_tree.R +++ b/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. diff --git a/R/descriptors.R b/R/descriptors.R index 0f47415ef..82300712b 100644 --- a/R/descriptors.R +++ b/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 diff --git a/R/fit.R b/R/fit.R index 6b1acc10f..5c81f1f1a 100644 --- a/R/fit.R +++ b/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 diff --git a/R/linear_reg.R b/R/linear_reg.R index 0839f4132..809cf6b2b 100644 --- a/R/linear_reg.R +++ b/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 diff --git a/R/logistic_reg.R b/R/logistic_reg.R index c8bf93c6e..439b189cd 100644 --- a/R/logistic_reg.R +++ b/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 diff --git a/R/mars.R b/R/mars.R index 276ff82f7..54337a4c5 100644 --- a/R/mars.R +++ b/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 diff --git a/R/mlp.R b/R/mlp.R index bee75a322..469a69790 100644 --- a/R/mlp.R +++ b/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 diff --git a/R/model_object_docs.R b/R/model_object_docs.R index f7006a922..aebf95730 100644 --- a/R/model_object_docs.R +++ b/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 diff --git a/R/multinom_reg.R b/R/multinom_reg.R index 1bcd4525b..74df8e448 100644 --- a/R/multinom_reg.R +++ b/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 diff --git a/R/nearest_neighbor.R b/R/nearest_neighbor.R index 37b953596..67e591cfb 100644 --- a/R/nearest_neighbor.R +++ b/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 diff --git a/R/nullmodel.R b/R/nullmodel.R index fbb557cd8..6e0006ca9 100644 --- a/R/nullmodel.R +++ b/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 diff --git a/R/predict_interval.R b/R/predict_interval.R index da5ae6220..7dd8562c1 100644 --- a/R/predict_interval.R +++ b/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 diff --git a/R/surv_reg.R b/R/surv_reg.R index d775ff399..deda279db 100644 --- a/R/surv_reg.R +++ b/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. diff --git a/R/svm_poly.R b/R/svm_poly.R index 087af9b79..b7f92c5fd 100644 --- a/R/svm_poly.R +++ b/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_: diff --git a/R/svm_rbf.R b/R/svm_rbf.R index e64abee70..e8f3875ee 100644 --- a/R/svm_rbf.R +++ b/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_: diff --git a/man/boost_tree.Rd b/man/boost_tree.Rd index d3904ca93..d5837e207 100644 --- a/man/boost_tree.Rd +++ b/man/boost_tree.Rd @@ -45,7 +45,7 @@ only).} the ensemble.} \item{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.} \item{tree_depth}{An integer for the maximum depth of the tree (i.e. number of splits) (\code{xgboost} only).} @@ -54,14 +54,14 @@ of splits) (\code{xgboost} only).} from iteration-to-iteration (\code{xgboost} only).} \item{loss_reduction}{A number for the reduction in the loss function required -to split further (\code{xgboost} only).} +to split further (\code{xgboost} only).} \item{sample_size}{A number for the number (or proportion) of data that is -exposed to the fitting routine. For \code{xgboost}, the sampling is done at at +exposed to the fitting routine. For \code{xgboost}, the sampling is done at each iteration while \code{C5.0} samples once during training.} \item{stop_iter}{The number of iterations without improvement before -stopping (\code{xgboost} only).} +stopping (\code{xgboost} only).} \item{object}{A boosted tree model specification.} @@ -88,7 +88,7 @@ model are: 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 @@ -100,10 +100,10 @@ to split further. 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 \code{set_engine()} function. If left to their defaults +time that the model is fit. Other options and arguments can be +set using the \code{set_engine()} function. If left to their defaults here (\code{NULL}), the values are taken from the underlying model -functions. If parameters need to be modified, \code{update()} can be used +functions. If parameters need to be modified, \code{update()} can be used in lieu of recreating the object from scratch. } \details{ @@ -114,7 +114,7 @@ possible modes are "regression" and "classification". The model can be created using the \code{fit()} function using the following \emph{engines}: \itemize{ -\item \pkg{R}: \code{"xgboost"} (the default), \code{"C5.0"} +\item \pkg{R}: \code{"xgboost"} (the default), \code{"C5.0"} \item \pkg{Spark}: \code{"spark"} } diff --git a/man/decision_tree.Rd b/man/decision_tree.Rd index 88c495752..a996c6f65 100644 --- a/man/decision_tree.Rd +++ b/man/decision_tree.Rd @@ -61,7 +61,7 @@ used by CART models (\code{rpart} only). 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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used @@ -71,7 +71,7 @@ in lieu of recreating the object from scratch. The model can be created using the \code{fit()} function using the following \emph{engines}: \itemize{ -\item \pkg{R}: \code{"rpart"} (the default) or \code{"C5.0"} (classification only) +\item \pkg{R}: \code{"rpart"} (the default) or \code{"C5.0"} (classification only) \item \pkg{Spark}: \code{"spark"} } diff --git a/man/descriptors.Rd b/man/descriptors.Rd index 4ad7ccf18..ad70a5324 100644 --- a/man/descriptors.Rd +++ b/man/descriptors.Rd @@ -39,11 +39,11 @@ function can be used. See Details below. Existing functions: \itemize{ \item \code{.obs()}: The current number of rows in the data set. -\item \code{.preds()}: The number of columns in the data set that are +\item \code{.preds()}: The number of columns in the data set that is associated with the predictors prior to dummy variable creation. \item \code{.cols()}: The number of predictor columns available after dummy variables are created (if any). -\item \code{.facts()}: The number of factor predictors in the dat set. +\item \code{.facts()}: The number of factor predictors in the data set. \item \code{.lvls()}: If the outcome is a factor, this is a table with the counts for each level (and \code{NA} otherwise). \item \code{.x()}: The predictors returned in the format given. Either a diff --git a/man/fit.Rd b/man/fit.Rd index 63da34ea9..a940a98f4 100644 --- a/man/fit.Rd +++ b/man/fit.Rd @@ -57,11 +57,11 @@ routine. } \details{ \code{fit()} and \code{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 \code{x}/\code{y}) and these functions translate -between the interface used when \code{fit()} or \code{fit_xy()} were invoked and the one +between the interface used when \code{fit()} or \code{fit_xy()} was invoked and the one required by the underlying model. When possible, these functions attempt to avoid making copies of the diff --git a/man/linear_reg.Rd b/man/linear_reg.Rd index 5eb262d0f..eeb6ab5c8 100644 --- a/man/linear_reg.Rd +++ b/man/linear_reg.Rd @@ -55,7 +55,7 @@ in the model. Note that this must be zero for some engines. 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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used diff --git a/man/logistic_reg.Rd b/man/logistic_reg.Rd index 4bba6a7ac..b01c447ae 100644 --- a/man/logistic_reg.Rd +++ b/man/logistic_reg.Rd @@ -55,7 +55,7 @@ in the model. Note that this must be zero for some engines. 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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used diff --git a/man/mars.Rd b/man/mars.Rd index b8534ce28..fab28d9a0 100644 --- a/man/mars.Rd +++ b/man/mars.Rd @@ -55,13 +55,13 @@ model are: \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 \code{?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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used diff --git a/man/mlp.Rd b/man/mlp.Rd index 6b5e00a01..0f5060e63 100644 --- a/man/mlp.Rd +++ b/man/mlp.Rd @@ -81,7 +81,7 @@ in lieu of recreating the object from scratch. } \details{ 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 \code{set_engine()}. If left to their defaults here (see above), the values are taken from the underlying model functions. One exception is \code{hidden_units} when \code{nnet::nnet} is used; that diff --git a/man/model_fit.Rd b/man/model_fit.Rd index d65137008..83b88820a 100644 --- a/man/model_fit.Rd +++ b/man/model_fit.Rd @@ -11,7 +11,7 @@ information about a model that has been fit to the data. The main elements of the object are: \itemize{ \item \code{lvl}: A vector of factor levels when the outcome is -is a factor. This is \code{NULL} when the outcome is not a factor +a factor. This is \code{NULL} when the outcome is not a factor vector. \item \code{spec}: A \code{model_spec} object. \item \code{fit}: The object produced by the fitting function. @@ -32,7 +32,7 @@ example below). This is a result of the use of quosures in the 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. } \examples{ diff --git a/man/model_spec.Rd b/man/model_spec.Rd index f61c9aba0..c1e3cca66 100644 --- a/man/model_spec.Rd +++ b/man/model_spec.Rd @@ -11,7 +11,7 @@ information about a model that will be fit. The main elements of the object are: \itemize{ \item \code{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 \code{glmnet} model, the argument name for the amount of the penalty is called "penalty" instead of "lambda" to make it more general diff --git a/man/multinom_reg.Rd b/man/multinom_reg.Rd index 954dd3089..84a205092 100644 --- a/man/multinom_reg.Rd +++ b/man/multinom_reg.Rd @@ -55,7 +55,7 @@ in the model. Note that this must be zero for some engines. 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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used diff --git a/man/nearest_neighbor.Rd b/man/nearest_neighbor.Rd index 86513d55a..9f259dd71 100644 --- a/man/nearest_neighbor.Rd +++ b/man/nearest_neighbor.Rd @@ -43,7 +43,7 @@ distance. This corresponds to the Manhattan distance with \code{dist_power = 1} and the Euclidean distance with \code{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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used diff --git a/man/nullmodel.Rd b/man/nullmodel.Rd index 8efe1af67..6c1a431e5 100644 --- a/man/nullmodel.Rd +++ b/man/nullmodel.Rd @@ -42,7 +42,7 @@ otherwise). The column for the most prevalent class has the proportion of the training samples with that class (the other columns are zero). } \item{n }{the number of elements in \code{y}} -\code{predict.nullmodel()} returns a either a factor or numeric vector +\code{predict.nullmodel()} returns either a factor or numeric vector depending on the class of \code{y}. All predictions are always the same. } \description{ diff --git a/man/other_predict.Rd b/man/other_predict.Rd index d9c680a4d..d5247e21b 100644 --- a/man/other_predict.Rd +++ b/man/other_predict.Rd @@ -48,7 +48,7 @@ and "pred_int". Default value is \code{FALSE}. \item{level}{A single numeric value between zero and one for the interval estimates.} -\item{std_error}{A single logical for wether the standard error should be +\item{std_error}{A single logical for whether the standard error should be returned (assuming that the model can compute it).} \item{quant}{A vector of numbers between 0 and 1 for the quantile being diff --git a/man/rpart_train.Rd b/man/rpart_train.Rd index a55440b08..227d55a36 100644 --- a/man/rpart_train.Rd +++ b/man/rpart_train.Rd @@ -27,7 +27,7 @@ that does not decrease the overall lack of fit by a factor of this means that the overall R-squared must increase by \code{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 \code{cp} will likely be pruned off by cross-validation, and that hence the program need not pursue it.} diff --git a/man/set_new_model.Rd b/man/set_new_model.Rd index ec3c42201..ec869e899 100644 --- a/man/set_new_model.Rd +++ b/man/set_new_model.Rd @@ -104,7 +104,7 @@ package. \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 \code{parsnip}. This are more thoroughly documented +be accessed using \code{parsnip}. This is more thoroughly documented on the package web site (see references below). In short, \code{parsnip} stores an environment object that contains diff --git a/man/surv_reg.Rd b/man/surv_reg.Rd index a087ef1c2..239c1d31a 100644 --- a/man/surv_reg.Rd +++ b/man/surv_reg.Rd @@ -37,7 +37,7 @@ model is: \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 \code{set_engine()}. If left to its default here (\code{NULL}), the value is taken from the underlying model functions. diff --git a/man/svm_poly.Rd b/man/svm_poly.Rd index 489fb477b..f8fb2c631 100644 --- a/man/svm_poly.Rd +++ b/man/svm_poly.Rd @@ -65,7 +65,7 @@ wrong side of the margin. (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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used diff --git a/man/svm_rbf.Rd b/man/svm_rbf.Rd index 53827252d..7af6569b1 100644 --- a/man/svm_rbf.Rd +++ b/man/svm_rbf.Rd @@ -56,7 +56,7 @@ function. (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 \code{set_engine()}. If left to their defaults here (\code{NULL}), the values are taken from the underlying model functions. If parameters need to be modified, \code{update()} can be used diff --git a/man/xgb_train.Rd b/man/xgb_train.Rd index 854b1147e..986fa13ef 100644 --- a/man/xgb_train.Rd +++ b/man/xgb_train.Rd @@ -47,7 +47,7 @@ of training set samples use for these purposes.} \item{early_stop}{An integer or \code{NULL}. If not \code{NULL}, it is the number of training iterations without improvement before stopping. If \code{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.} \item{...}{Other options to pass to \code{xgb.train}.}