LogSt | R Documentation |
Transforms the data by a log transformation, modifying small and zero observations such that the transformation is linear for x <= threshold
and logarithmic for x > threshold. So the transformation yields finite values and is continuously differentiable.
LogSt(x, base = 10, calib = x, threshold = NULL, mult = 1)
LogStInv(x, base = NULL, threshold = NULL)
x |
a vector or matrix of data, which is to be transformed |
base |
a positive or complex number: the base with respect to which logarithms are computed. Defaults to 10. Use=exp(1) for natural log. |
calib |
a vector or matrix of data used to calibrate the transformation(s), i.e., to determine
the constant |
threshold |
constant |
mult |
a tuning constant affecting the transformation of small values, see |
In order to avoid log(x) = -\infty
for x=0
in log-transformations there's often a constant added to the variable before taking the log
. This is not always a pleasable strategy.
The function
LogSt
handles this problem based on the following ideas:
The modification should only affect the values for "small" arguments.
What "small" is should be determined in connection with the non-zero values of the original variable, since it should behave well (be equivariant) with respect to a change in the "unit of measurement".
The function must remain monotone, and it should remain (weakly) convex.
These criteria are implemented here as follows: The shape is determined by a
threshold c
at which - coming from above - the log function switches to a linear function with the same slope at this point.
This is obtained by
g(x) =
\left\{\begin{array}{ll}
log_{10}(x) &\textup{for }x \ge c\\
log_{10}(c) - \frac{c - x}{c \cdot log(10)} &\textup{for } x < c
\end{array}\right.
Small values are determined by the threshold c
. If not given by the argument threshold
, it is determined by the quartiles q_1
and q_3
of the non-zero data as those smaller than c = \frac{q_1^{1+r}}{q_3^r}
where r
can be set by the argument mult
.
The rationale is, that, for lognormal data, this constant identifies 2 percent of the data as small.
Beyond this limit, the transformation continues linear with the derivative of the log curve at this point.
Another idea for choosing the threshold c
was: median(x) / (median(x)/quantile(x, 0.25))^2.9)
The function chooses log_{10}
rather than natural logs by default because they can be backtransformed relatively easily in mind.
A generalized log (see: Rocke 2003) can be calculated in order to stabilize the variance as:
function (x, a) { return(log((x + sqrt(x^2 + a^2)) / 2)) }
the transformed data. The value c
used for the transformation and needed for inverse transformation is returned as attr(.,"threshold")
and the used base as attr(.,"base")
.
Werner A. Stahel, ETH Zurich
slight modifications Andri Signorell <andri@signorell.net>
Rocke, D M, Durbin B (2003): Approximate variance-stabilizing transformations for gene-expression microarray data, Bioinformatics. 22;19(8):966-72.
log
, log10
dd <- c(seq(0,1,0.1), 5 * 10^rnorm(100, 0, 0.2))
dd <- sort(dd)
r.dl <- LogSt(dd)
plot(dd, r.dl, type="l")
abline(v=attr(r.dl, "threshold"), lty=2)
x <- rchisq(df=3, n=100)
# should give 0 (or at least something small):
LogStInv(LogSt(x)) - x
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