[R] log-transformed linear regression

[servet cizmeli]

Hello,

I have a basic question. Sorry if it is so evident....

I have the following data file :
http://ekumen.homelinux.net/mydata.txt

I need to model Y~X-1 (simple linear regression through the origin) with
these data :

load(file="mydata.txt")
X=k[,1]
Y=k[,2]

aa=lm(Y~X-1)
dev.new()
plot(X,Y,log="xy")
abline(aa,untf=T)
abline(b=0.0235, a=0,col="red",untf=T)
abline(b=0.031, a=0,col="green",untf=T)

Other people did the same kind of analysis with their data and found the
regression coefficients of 0.0235 (red line) and 0.031 (green line).

Regression with my own data, though, yields a slope of 0.0458 (black
line) which is too high. Clearly my regression is too much influenced by
the single point with high values (X>100). I would not like to discard
this point, though, because I know that the measurement is correct. I
just would like to give it less weight...

When I log-transform X and Y data, I obtain :

dev.new()
plot(log10(X),log10(Y))
abline(v=0,h=0,col="cyan")
bb=lm(log10(Y)~log10(X))
abline(bb,col="blue")
bb

I am happy with this regression. Now the slope is at the log-log domain.
I have to convert it back so that I can obtain a number comparable with
the literature (0.0235 and 0.031).  How to do it? I can't force the
second regression through the origin as the log-transformed data does
not go through the origin anymore.

at first it seemed like an easy problem but I am at loss :o((
thanks a lot for your kindly help
servet