Studdy Missing Data, differentiate between a percent with in the valid answers and with in the different missing answers

On 3/3/08 8:21 PM, Ericka Lundstr?m wrote:

 > I'm trying to emigrate from SPSS to R, thou I have some 

problems whit > getting R to distinguish between the different 
kind of missing. ... > Is there a smart way in R to 
differentiate between missing and valid > and at the same time 
treat both the categories within missing and > valid as 
answers (like SPSS did above)

The Hmisc package has some support for special missing values, 
for instance when reading in SAS datasets using sas.get. I 
don't believe spss.get offers the same facility, though.

You can define special missing values for a variable manually, 
which might seem a bit involved, but this could easily be 
automated. For your example, try:

special <- dataFrame$TWO %in% c("?","X")
attr(dataFrame$TWO, "special.miss") <-
     list(codes=as.character(dataFrame$TWO[special]),
     obs=(1:length(dataFrame$TWO))[special])
class(dataFrame$TWO) <- c("factor", "special.miss")
is.na(dataFrame$TWO) <- special

# Then describe gives new percentages

describe(dataFrame$TWO)
dataFrame$TWO
       n missing       ?       X  unique
       3       4       2       2       2

No (2, 67%), yes (1, 33%)