Dear R-List,

My questions concerns missing values. Specifically, is is possible to 
use different "types" of missingness in a dataset and not a 
one-size-fits-all NA?
For example, data may be missing because of an outright refusal by a 
respondent to answer a question, or because she didn't know an answer, 
or because the item simply did not apply. In later analysis it is 
sometimes useful to be able to distinguish between the cases, but 
nonetheless have them all treated as missing when using, say, lm( ).
In Stata this is possible by using different missing value indicators. 
The standard one is a period '.' whereas '.a' and '.b' etc are treated 
as missing too, but can all be distinguished from another (they are even 
ordinal such that . < .a < .b).
To give a simplistic example in R, let

 > dat <- data.frame(
+ hours = c(36, 40, 40, 0, 37.5, 0, 36, 20, 40),
+ wage = c( 15.5, 7.5, 8, -1, 17.5, -1, -2, 13, -2))
 > dat
   hours wage
1  36.0 15.5
2  40.0  7.5
3  40.0  8.0
4   0.0 -1.0
5  37.5 17.5
6   0.0 -1.0
7  36.0 -2.0
8  20.0 13.0
9  40.0 -2.0


where for wages -1 indicates "didn't work" and -2 indicates "refused to 
respond". How could I replace the negative values for wages with 
missingness indicators to use the data frame in for instance lm( ), but 
later operate only on those observations who "refused to respond"?
Of course I can always work around this somehow, especially in this easy 
example, but as data frames get larger and cases more complex the 
workarounds seem more and more klutzy to me.
So, if there is an easy way to do this that I have overlooked, I would 
be grateful for any advice or references.

Best,
Christian

-- 
Christian Raschke
Department of Economics
and
ISDS Research Lab (HSRG)
Louisiana State University
Patrick Taylor Hall, Rm 2128
Baton Rouge, LA 70803
crasch2@lsu.edu


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