Generate a new dataset in a format that is accepted by `compare`

. Dummy variables are
created for categorical variables.

1 | ```
datashape(dataset, y, x, subs)
``` |

`dataset` |
a dataframe or matrix containing user data. |

`y` |
the column number of the outcome variable in dataset d. |

`x` |
a vector containing the explanatory or predictor variables of interest. The new data matrix will only contain these variables and the outcome variable. |

`subs` |
a vector describing a subset of rows from dataset d to include in the returned data matrix. |

This function can be used to prepare a dataset before applying the
`compare`

function. The outcome column number must be
specified, and specific predictors and observation subsets may be specified. 2-level
categorical variables will be converted to binary, while dummy variables will be
created for categorical predictors with greater than two levels.

The "datashaped" dataset should be saved to a new object.

This function returns a matrix conforming to the specifications supplied to the datashape function.

`datashape`

will not function if missing values are present.

@examples ## Preparing the iris dataset data(iris) iris.shaped <- datashape(dataset = iris, y = 4) head(iris.shaped) ## Creating a copy of iris with sepal-related predictors and a subset of observations. iris.sub <- datashape(dataset = iris, y = 4, x = c(1,2), subs = c(1:20, 50:70)) head(iris.sub)

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