Your friends are:
na.omit
and
is.na
The easiest way (I think) will be to have your data.frame organized so you
have a row for each subject. and on the row, you check the outcome variables
with "is.na" and create a subset of your data from that.
And on that data, run your tests...
Tal
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----------------------------------------------------------------------------------------------
On Wed, Feb 24, 2010 at 11:19 AM, <denis.ay...@unibas.ch> wrote:
> Hi everyone
>
> I have the following problem: My dataframe has 3 variables: ID, Year and
> and an outcome variable. The dataframe contains repeated measurements
> because the subjects filled out a questionnaire every year. The time span
> covers 2 years.
>
> Now I want to check if there is a significant change in the outcome over
> the 2 years with a paired wilcox.test. The problem: Not every subject
> completed both questionnaires. Thus, some subjects have the outcome only
> for year1 and others only for year2.
>
> If I try wilcox.test(Outcome~Year, paired=T), I receive an error "not the
> same length".
>
> Is there a fast method to remove those subjects with missing outcomes?
>
> Thanks for any help.
>
> Regards,
> Denis Aydin
>
> _____________________________________________
>
> Denis Aydin, MSc
>
> Swiss Tropical and Public Health Institute
> Socinstrasse 57, P.O. Box
> 4002 Basel, Switzerland
>
> T +41 (0)61 270 22 04
> F +41 (0)61 270 22 25
> @ denis.ay...@unibas.ch
>
> _____________________________________________
>
> Postal address:
>
> Denis Aydin, MSc
> Swiss Tropical and Public Health Institute
> Socinstrasse 57, P.O. Box
> 4002 Basel, Switzerland
>
> Visitors address:
>
> Kanonengasse 27
> 4051 Basel, Switzerland
>
> _____________________________________________
>
> Swiss TPH website: www.swisstph.ch
>
> Former ISPM: www.ispm-unibas.ch
> Former STI: www.sti.ch
>
>
>
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