R help - Sep 2015 - Handling NA's when you write your own code

Hello,
I have a dataset that have a couple of missing values and I DO NOT want to
delete the observations with the missing values. I have read about na.action in
dealing with missing values but I do not know how it applies to user-specific
written code.

Is there a code you can use with your dataset so that subsequent analysis will
automatically detect missing values?

The following are some of the things I will be doing so that you can advice me
appropriately

Computing OLS estimates using the matrix approach
Write objective and gradient function subroutines which will now be used in the
Optimx for nonlinear optimization.

Thank you

Not quite sure what you mean with "dealing with NAs in use-specific
code".

Some functions handle these automatically, like lm().  If you write your
own code, and want to handle NA-s somehow, you have to implement such
checks yourself.  You may also use the "complete.cases" function (but
that
does not detect Inf-s).

Typically your own handling means excluding NA-s in one way or another as
most of the math operations are not defined on these.

Cheers,
Ott

On Wed, Sep 9, 2015 at 12:03 PM, Olu Ola via R-help <r-help at
r-project.org>
wrote:
> Hello,
> I have a dataset that have a couple of missing values and I DO NOT want to
> delete the observations with the missing values. I have read about
> na.action in dealing with missing values but I do not know how it applies
> to user-specific written code.
>
> Is there a code you can use with your dataset so that subsequent analysis
> will automatically detect missing values?
>
> The following are some of the things I will be doing so that you can
> advice me appropriately
>
> Computing OLS estimates using the matrix approach
> Write objective and gradient function subroutines which will now be used
> in the Optimx for nonlinear optimization.
>
> Thank you
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>


-- 
Ott Toomet

Visiting Researcher
School of Information
Mary Gates Hall, Suite 310
University of Washington
Seattle, WA 98195

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