Be careful!! The plots could be potentially misleading. The problem is the
nature of the missingness. The approach you are taking is based on assuming
MCAR missingness (look it up, if necessary). If that is not the case --
e.g. if there is censoring, MAR, or informative missingness -- the plots
may be completely misleading.
Missingness in longitudinal data is a very difficult issue. If this is
something you don't know about already, I strongly suggest that you consult
a statistician who does -- not all of us do (I know almost nothing, for
example).
-- Bert
On Thu, Jul 5, 2012 at 10:34 AM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> On Jul 5, 2012, at 1:25 PM, Eiko Fried wrote:
>
> Hello,
>>
>> I have many hundred variables in my longitudinal dataset and lots of
>> missings. In order to plot data I need to remove missings.
>>
>> If I do
>>
>>> data <- na.omit(data)
>>>
>> that will reduce my dataset to 2% of its original size ;)
>>
>> So I only need to listwise delete missings on 3 variables (the ones I am
>> plotting).
>>
>> data$variable1 <-na.omit(data$variable1)
>>
>
> ?complete.cases # returns a logical vector
>
> data[ complete.cases( data[ , c("var1", "var2", "var3"]) , ]
>
>
> David Winsemius, MD
> West Hartford, CT
>
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>
--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
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