transfo_transformback: Backtransform variables based on the output of 'transfo'.

In cellWise: Analyzing Data with Cellwise Outliers

Backtransform variables based on the output of transfo.

Description

Based on the output of transfo, backtransform the variables to their original shape through the inverse Yeo-Johnson and/or Box-Cox transformations with the previusly estimated parameters and standardization.

Usage

transfo_transformback(Ynew, transfo.out)

Arguments

Ynew	A data matrix with d columns, which contain the variables to be backtransformed. The number of columns must be the same as the output Y of the run of transfo on the original data. The number of rows may be different.
transfo.out	The output of a call to transfo.

Value

Returns a matrix with backtransformed variables.

Author(s)

J. Raymaekers and P.J. Rousseeuw

References

J. Raymaekers and P.J. Rousseeuw (2021). Transforming variables to central normality. Machine Learning. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10994-021-05960-5")}(link to open access pdf)

See Also

transfo

Examples

set.seed(123); x <- matrix(rnorm(2000), ncol = 2)
y <- sqrt(abs(0.3 * x[, 1] + 0.5 * x[, 2] + 4))
ty.out <- transfo(y, type = "BC")
ty.out$lambdahats
ty <- ty.out$Y
lm.out <- lm(ty ~ x)
yhat <- transfo_transformback(lm.out$fitted.values, ty.out)
plot(y, yhat); abline(0, 1)

cellWise documentation built on Oct. 25, 2023, 5:07 p.m.