In R-devel
na.action(GLM)
will work as the extractor. The problem with attr(GLM$model, "na.action")
is that the 'model' component is optional, and with
model.frame(ModelObject) that if the 'model' component has been omitted it
will try to recreate the model frame from the currently visible objects of
the name originally used. (Because that is error-prone, we switched to
model=TRUE as the default.)
In earlier versions of R, GLM$na.action is the copy you want.
However, I think if you care about omitted rows, you should use
na.action=na.exclude, for then most auxiliary functions will give you
results for all the rows.
On Tue, 26 Aug 2008, Marc Schwartz wrote:
on 08/26/2008 07:31 PM (Ted Harding) wrote:
On 26-Aug-08 23:49:37, hadley wickham wrote:
On Tue, Aug 26, 2008 at 6:45 PM, Ted Harding
<[EMAIL PROTECTED]> wrote:
Hi Folks,
This tip is probably lurking somewhere already, but I've just
discovered it the hard way, so it is probably worth passing
on for the benefit of those who might otherwise hack their
way along the same path.
Say (for example) you want to do a logistic regression of a
binary response Y on variables X1, X2, X3, X4:
GLM <- glm(Y ~ X1 + X2 + X3 + X4)
Say there are 1000 cases in the data. Because of missing values
(NAs) in the variables, the number of complete cases retained
for the regression is, say, 600. glm() does this automatically.
QUESTION: Which cases are they?
You can of course find out "by hand" on the lines of
ix <- which( (!is.na(Y))&(!is.na(X1))&...&(!is.na(X4)) )
but one feels that GLM already knows -- so how to get it to talk?
ANSWER: (e.g.)
ix <- as.integer(names(GLM$fit))
This is a partial match to 'fitted', and will only work if default row
names were used.
Alternatively, you can use:
attr(GLM$model, "na.action")
Hadley
Thanks! I can see that it works -- though understanding how
requires a deeper knowledge of "R internals". However, since
you've approached it from that direction, simply
GLM$model
is a dataframe of the retained cases (with corresponding
row-names), all variables at once, and that is possibly an
even simpler approach!
Or just use:
model.frame(ModelObject)
as the extractor function... :-)
Another 'a priori' approach would be to use na.omit() or one of its
brethren on the data frame before creating the model. Which function is
used depends upon how 'na.action' is set.
The returned value, or more specifically the 'na.action' attribute as
appropriate, would yield information similar to Hadley's approach
relative to which records were excluded.
For example, using the simple data frame in ?na.omit:
DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA))
DF
x y
1 1 0
2 2 10
3 3 NA
DF.na <- na.omit(DF)
DF.na
x y
1 1 0
2 2 10
attr(DF.na, "na.action")
3
3
attr(,"class")
[1] "omit"
So you can see that record 3 was removed from the original data frame
due to the NA for 'y'.
HTH,
Marc Schwartz
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--
Brian D. Ripley, [EMAIL PROTECTED]
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, UK Fax: +44 1865 272595
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
