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
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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()
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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)
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