R6 Class for scaling (and un-scaling) design matrices

Scales a design matrix so that all non-categorical columns have a mean of 0 and an standard deviation of 1.

Details

The object initialisation is used to determine the relevant means and standard deviations to scale by and then the scaling (and un-scaling) itself is performed by the relevant object methods.

Un-scaling is done on linear model Beta and Sigma coefficients. For this purpose the first column on the dataset to be scaled is assumed to be the outcome variable with all other variables assumed to be post-transformation predictor variables (i.e. all dummy variables have already been expanded).

Public fields

centre: Vector of column means. The first value is the outcome variable, all other variables are the predictors.
scales: Vector of column standard deviations. The first value is the outcome variable, all other variables are the predictors.

Methods

`scaler$new()`

Uses dat to determine the relevant column means and standard deviations to use when scaling and un-scaling future datasets. Implicitly assumes that new datasets have the same column order as dat

Usage

scaler$new(dat)

Arguments

dat: A data.frame or matrix. All columns must be numeric (i.e dummy variables, must have already been expanded out).

Details

Categorical columns (as determined by those who's values are entirely 1 or 0) will not be scaled. This is achieved by setting the corresponding values of centre to 0 and scale to 1.

`scaler$scale()`

Scales a dataset so that all continuous variables have a mean of 0 and a standard deviation of 1.

Usage

scaler$scale(dat)

Arguments

dat: A data.frame or matrix whose columns are all numeric (i.e. dummy variables have all been expanded out) and whose columns are in the same order as the dataset used in the initialization function.

`scaler$unscale_sigma()`

Unscales a sigma value (or matrix) as estimated by a linear model using a design matrix scaled by this object. This function only works if the first column of the initialisation data.frame was the outcome variable.

Usage

scaler$unscale_sigma(sigma)

Arguments

sigma: A numeric value or matrix.

Returns

A numeric value or matrix

`scaler$unscale_beta()`

Unscales a beta value (or vector) as estimated by a linear model using a design matrix scaled by this object. This function only works if the first column of the initialization data.frame was the outcome variable.

Usage

scaler$unscale_beta(beta)

Arguments

beta: A numeric vector of beta coefficients as estimated from a linear model.

Returns

A numeric vector.

`scaler$clone()`

The objects of this class are cloneable with this method.

Usage

scaler$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.