Thank you both of you, Kenneth and Ted:-). Log1p(x) is not what I asked
for, but it is better:-D
And Ted, thanks for your thoughts on funcional form. I'm just starting
out with R, and feel like I've barely scratched the surface of the
program. I have never in my life done a non-linear regression, but that
will soon change:-)
(Ted Harding) wrote:
On 16-Feb-05 Terji Petersen wrote:
Hi,
I'm trying to do a regression like this:
wage.r = lm( log(WAGE) ~ log(EXPER)
where EXPER is an integer that goes from 0 to about 50.
EXPER contains some zeros, so you can't take its log,
and the above regression therefore fails. I would like
to make R accept log(0) as 0, is that possible?
Or do I have first have to turn the 0's into 1's to be
able to do the above regression?
If treating log(0) as 0 would do your business, then a
preliminary pass to turn 0 into 1 would be the simplest
method. It only takes 1 line.
This is a bit of a Catch-22. It looks like you're trying
to fit a power law
WAGE = A*(EXPER^B)
(where I guess EXPER means experience) and you've got
some cases with no experience. Whether your work-round
is appropriate depends in part on the unit of experience.
If it's in years, then a case with 3 months experience
would have log(EXPER) = -1.39, thereby weighing in with
a lesser value than someone with zero experience, on your
proposal.
On the other hand, if it's in days, then
log(EXPER) = log(91) = 4.51
and even someone with only a week has log(EXPER) = 1.95
But your log(0) = 0 data would be sitting there all the
time, whatever the scale of EXPER, and so would have an
influence on your regression which depended on this scale.
You might have to consider using log(0) -- const
where the const is such as to give reasonable results,
given what comes out of the rest of the data (where EXPER0).
The fundamental problem is that your power law predicts
zero wage for zero experience, which is rarely the case.
You might do better to try a non-linear fit
WAGE = W0 + A*(EXPER^B)
for which sort of thing there are several resources in R,
perhaps the simplest being 'nls'.
For what you have in your installed packages, try a
help.search(nonlinear)
Once you open this door, you can try perhaps more realistic
non-linear models, including what can be found amongst the
SS. (Self-Starting) models in nls -- have a look
at what's listed by
library(help=nls)
as well as what is allowed according to ?nls.
Such models would allow an initial (zero-experience) wage,
perhaps not changing much for some time, then rising more
rapidly once an experience threshold is passed, then
flattening out to a lower slope over a longer time (something
which many of us have experience of). And even ultimately
ending to decrease ...
Hoping this helps,
Ted.
E-Mail: (Ted Harding) [EMAIL PROTECTED]
Fax-to-email: +44 (0)870 094 0861
Date: 16-Feb-05 Time: 13:26:53
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