Hello R-people!
I have a general statistical question about regressions. I just want
to describe my case:
I have got a dataset of around 150 observations and 1 dependent and 2
independent variables.
The dependent variable is of metric nature (in my case meters in a
range from around 0.5-10000 m). The first independent is also metric
(in mm ranging from 50-700 mm) and it is assumed to be in a linear
relation with the dependend one. So that is not a problem at all to
do a typicall linear regression on that.
No there is the second independent variable. This is also of metric
nature and gives information on time (ranging from 1 day to 800 days)
but here sometimes is this variable not exactly clear, I know for
example a range (1-2 days) or less than x days etc. So my dataset
could look like this:
measured dependent variable in days:
1
15
7-9
<2
<9
24
4
4-7
So my question: Is there a general method to include such types of
variables into a regression analysis?
Secondly I assume that there is not a linear relation given, it is
more of a logarithmic nature so that the influence of the time on the
dependent variable decreases with increasing size.
So in short my questions:
* How can I use variable values like <5 or 4-5 in a regression
* Is it possible to combine the linear relationship with a
logarithmic one in a multiple regression
*How can that be done in R, are there any special packages you'd
recommend?
Thank you very much
best regards
Johannes
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