[R] na.omit leaves cases with NA's intact

2009-10-31 Thread David L. Van Brunt, Ph.D.
Strange, this normally works, but in a recent run, I have a data set in an
xts format, that has a lot of NA's in a few of the variables in the leading
and trailing positions, due to some lagging calculations. Before running an
analysis, I use

newdata-na.omit(orginaldata)

and normally a

dim(newdata)

shows the fewer rows. Now, for some reason I do this operation and see that
hundreds of rows SHOULD be removed, (I can plainly see the NAs in there) and
even test is.na(orginaldata$variable) and get a clear TRUE, but the case
still remains after the na.omit operation. Yes, I'm spelling it right.

I'm doing this with many sets of data, and it works great except for this
one data set

Any idea if there are limits on when this function works, or more
importantly, if there is a manual way to do it as a workaround?

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Re: [R] na.omit leaves cases with NA's intact

2009-10-31 Thread Prof Brian Ripley

What class is 'orginaldata' (and its columns if a data frame)?
Note that packge xts defines methods for na.omit:


library(xts)

...

methods(na.omit)

[1] na.omit.data.frame* na.omit.default*na.omit.ts*
[4] na.omit.xts*

so this is possibly something peculiar to package xts.  See the footer 
of this message: a reproducible example would help enormously in 
finding the root cause here.


On Sat, 31 Oct 2009, David L. Van Brunt, Ph.D. wrote:


Strange, this normally works, but in a recent run, I have a data set in an
xts format, that has a lot of NA's in a few of the variables in the leading
and trailing positions, due to some lagging calculations. Before running an
analysis, I use

newdata-na.omit(orginaldata)

and normally a

dim(newdata)

shows the fewer rows. Now, for some reason I do this operation and see that
hundreds of rows SHOULD be removed, (I can plainly see the NAs in there) and
even test is.na(orginaldata$variable) and get a clear TRUE, but the case
still remains after the na.omit operation. Yes, I'm spelling it right.

I'm doing this with many sets of data, and it works great except for this
one data set

Any idea if there are limits on when this function works, or more
importantly, if there is a manual way to do it as a workaround?

[[alternative HTML version deleted]]

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--
Brian D. Ripley,  rip...@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel:  +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UKFax:  +44 1865 272595

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