Chaosheng Zhang wrote:
A few points about log transforms. See below.
> The reason why I care about this issue is that there are at least two
> problems related to data transformation (in order to follow the normal
> (1) The measurement scale is reduced. The orignal ratio/interval scale may
> be reduced to the lower level of ordinal, even close to nominal, which
> results in loss of raw information.
There shouldn't be any loss of information since the log transformation
is a one-to-one mapping. The sample variance is much smaller in the log
scale but the log transform is often used precisely for that purpose.
> (2) Artificial relationship is introduced. We know that the lognormal
> distribution is widely accepted. In correlation analysis, if the
> log-transformed data are used, the correlation becomes the "log-log"
> relationship, not the oginal linear relationship. In bivariate regression
> analysis, the original function is:
> y = a x + b
> However, for the log-transformed data, the function becomes:
> log(y) = a log(x) + b
> or y = exp (a log(x) + b)
> In many cases, it is not clear if the relationship should be linear or
> "log-linear". However, the artificially introduced "log-linear" relationship
> need to be proved.
Rather, when you apply the log transform you a-priori assume the
existence of what you call the 'artificial relation', and this
assumption refer to the algebraic form of the error term rather than to
the relation between y and x. Say E(y)=f(x) is the model for y versus x,
where E is the expectation operator. If you assume an additive error
structure y_i=f(x_i)+e_i, where i indexes observation, and you consider
the e_i's as iid normal random variates, then there is no reason to
apply the log tranform. On the other hand if you assume a multiplicative
error structure such as y_i=f(x_i)*e_i, and you assume that the e_i are
iid lognormal random variates, then the log transform yields
ln(y_i)=lnf(x_i)+ln(e_i) and now the ln(e_i) are iid normal random
variates (with a much smaller variance than the e_i). The theory of the
lognormal is well developed so that there isn't actually much need to
transform the data to make it normal (e.g. Crow and Shimizu, 1988,
Lognormal distributions, Dekker Inc, NY).
> The most difficult situation is that if scientifically the relationship
> between x and y is linear, should the data transformation still be carried
> out (just to satisfy the statistical requirement)?
When the relation between x and y is linear such as in E(y)=a*x+b, the
errors are assumed to be additive (people usually do not believe that
y_i=(a*x_i+b)*e_i but rather y_i=(a*x_i+b)+e_i), so applying a log
transform to such case does not satisfy statistical requirements. On the
contrary, it goes against statistical advice.
The multiplicative error structure arises in models of the form
E(y)=a*x^b or E(y)=a*exp(b*x), or in general, in all multiplicative
As there is a central limit theoren for additive processes leading to
the normal, there is a central limit theorem for multiplicative
processes leading to the lognormal.
In the case of geostatistics, as pointed out by other people, the
kriging equations do not require distributional assumptions (though the
fitting of the model variogram to the moment-based Matheron variogram
does). If the frequency distribution of the regionalised variable looks
lognormal, it means that there is un underlying mechanism which is
multiplicative, but still i don't see why the variable should be
transformed for geostatistical analysis, except perhaps for fitting the
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