Description Usage Arguments Details Examples

Creates a scaler object containing column means and standard deviations so that it can be used to predict on a similar dataset

1 |

`data` |
(numeric matrix or numeric dataframe) The dataset |

`center` |
(flag) whether to center the columns or not |

`scale` |
(flag) whether to scale the columns or not |

This computes means and standard deviations of each columns and stores it for a prediction on a dataset using predict method. If scale is TRUE, the columns are automatically centered even if center is set to FALSE.

The scaler class provides a model-predict interface to scale and unscale matrices and dataframes. This predict method supports type argument - scale or unscale. The scaler_ function is used to construct scaler object by providing centering vector(alias for means of columns, ex: columnwise medians) and scaling vector (alias for column standard deviations, ex: columnwise mean absolute deviations). scaler class is meant to aid analysis, for performance critical work use Rfast::standardize()

1 2 3 4 5 6 7 8 9 10 11 | ```
set.seed(1)
n_70 = round(nrow(mtcars) * 0.7)
index = sample(1:nrow(mtcars), n_70)
mtcars_A = mtcars[index, ]
mtcars_B = mtcars[index, ]
model = scaler(mtcars_A) # creates model based on mtcars_A
mtcars_1 = predict(model, newdata = mtcars_A) # scale mtcars_A
mtcars_2 = predict(model, newdata = mtcars_B) # scale mtcars_B using model
class(mtcars_2) # does not convert to matrix
mtcars_2_B = predict(model, newdata = mtcars_2, type = "unscale")
all.equal(mtcars_2_B, mtcars_B)
``` |

talegari/sidekicks documentation built on Sept. 26, 2018, 4:18 p.m.

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