# Chapter 7 Standardized Argument Names

## 7.1 Dot Usage

If there is a possibility of argument name conflicts between the function and any arguments passed down through

`...`

, it is strongly suggested that the argument names to the main function be prefixed with a dot (e.g.`.data`

,`.x`

, etc.)When defining the order of arguments in a function, try to keep the

`...`

as far to the left as possible to coerce users to explicitly name all arguments to the right of`...`

.

## 7.2 Data Arguments

`na_rm`

: missing data handling.`new_data`

: data to be predicted.`weights`

: case weights.For

`.data.frame`

methods:`x`

: predictors or generic data objects.`y`

: outcome data.

For

`.formula`

methods:`formula`

: a`y ~ x`

formula specifying the outcome and predictors.`data`

: the data.frame to pull formula variables from.

## 7.3 Numerical Arguments

`times`

: the number of bootstraps, simulations, or other replications.

## 7.4 Statistical Quantities

`direction`

: the type of hypothesis test alternative.`level`

: interval levels (e.g., confidence, credible, prediction, and so on).`link`

: link functions for generalized linear models.

## 7.5 Tuning Parameters

`activation`

: the type of activation function between network layers.`cost`

: a cost value for SVM models.`Cp`

: The cost-complexity parameter in classical CART models.`deg_free`

: a parameter for the degrees of freedom.`degree`

: the polynomial degree.`dropout`

: the parameter dropout rate.`epochs`

: the number of iterations of training.`hidden_units`

: the number of hidden units in a network layer.`Laplace`

: the Laplace correction used to smooth low-frequency counts.`learn_rate`

: the rate at which the boosting algorithm adapts from iteration-to-iteration.`loss_reduction`

: The reduction in the loss function required to split further.`min_n`

: The minimum number of data points in a node that are required for the node to be split further.`mixture`

: the proportion of L1 regularization in the model.`mtry`

: The number of predictors that will be randomly sampled at each split when creating the tree models.`neighbors`

: a parameter for the number of neighbors used in a prototype model.`num_comp`

: the number of components in a model (e.g. PCA or PLS components).`num_terms`

: a nonspecific parameter for the number of terms in a model. This can be used with models that include feature selection, such as MARS.`prod_degree`

: the number of terms to combine into interactions. A value of 1 implies an additive model. Useful for MARS models and some linear models.`prune`

: a logical for whether a tree or set of rules should be pruned.`rbf_sigma`

: the sigma parameters of a radial basis function.`penalty`

: The amount of regularization used. In cases where different penalty types require to be differentiated, the names`L1`

and`L2`

are recommended.`sample_size`

: the size of the data set used for modeling within an iteration of the modeling algorithm, such as stochastic gradient boosting.`surv_dist`

: the statistical distribution of the data in a survival analysis model.`tree_depth`

: The maximum depth of the tree (i.e. number of splits).`trees`

: The number of trees contained in a random forest or boosted ensemble. In the latter case, this is equal to the number of boosting iterations.`weight_func`

: The type of kernel function that weights the distances between samples (e.g. in a K-nearest neighbors model).

## 7.6 Others

`fn`

and`fns`

when a single or multiple functions are passed as arguments (respectively).