The constant to add to x (defaults to max(0, -min(x) + eps))
b
The base of the log (defaults to 10)
standardize
If TRUE, the transformed values are also centered and
scaled, such that the transformation attempts a standard normal
eps
The allowed error in the expression for the selected a
warn
Should a warning result from infinite values?
object
an object of class 'log_x'
newdata
a vector of data to be (potentially reverse) transformed
inverse
if TRUE, performs reverse transformation
...
additional arguments
Value
A list of class log_x with elements
x.t
transformed
original data
x
original data
mean
mean after transformation but prior to standardization
sd
sd after transformation but prior to standardization
a
estimated a value
b
estimated base b value
n
number of nonmissing observations
norm_stat
Pearson's P / degrees of freedom
standardize
was the transformation standardized
The predict function returns the numeric value of the transformation
performed on new data, and allows for the inverse transformation as well.
Details
log_x performs a simple log transformation in the context of
bestNormalize, such that it creates a transformation that can be estimated
and applied to new data via the predict function. The parameter a is
essentially estimated by the training set by default (estimated as the minimum
possible to some extent epsilon), while the base must be
specified beforehand.