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 EXPER>0).
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.
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