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
cras...@lsu.edu
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