R help - Mar 2013 - How many samples ACTUALLY used in regression?

Dear All,

is there a simple way that covers all regression models to extract the number of
samples from a data frame/matrix actually used in a regression model?

For instance I might have a data of 100 rows and 4 colums (1 response + 3
explanatory variables).  If 3 samples have one or more NAs in the explanatory
variable columns these samples will be dropped in any model:

my.model = lm(y ~ x + w + z, my.data)
my.model = glm(y ~ x + w + z, my.data, family = binomial)
my.model = polr(y ~ x + w + z, my.data)
?

I don't seem to be able to find one single method that works in the exact
same way -- irrespective of the model type -- to interrogate my.model to see how
many samples of my.data were actually used.  Is there such function or do I need
to hack something together?

Best wishes

Federico

Federico Calboli <f.calboli <at> imperial.ac.uk> writes:
> is there a simple way that covers all regression models to extract 
> the number of samples from a data
> frame/matrix actually used in a regression model?
> 
> For instance I might have a data of 100 rows and 4 colums 
> (1 response + 3 explanatory variables).  If 3 samples
> have one or more NAs in the explanatory variable columns 
> these samples will be dropped in any model:
my.model = lm(y ~ x + w + z, my.data)
my.model = glm(y ~ x + w + z, my.data, family = binomial)
my.model = polr(y ~ x + w + z, my.data)
> I don't seem to be able to find one single method that works 
> in the exact same way -- irrespective of the model
> type -- to interrogate my.model to see how many samples of 
> my.data were actually used.  Is there such
> function or do I need to hack something together?
  I haven't tested it (don't want to bother to put together the
test data), but does nrow(model.frame(my.model)) work ?

On Mar 18, 2013, at 7:36 AM, Federico Calboli <f.calboli at
imperial.ac.uk> wrote:
> Dear All,
> 
> is there a simple way that covers all regression models to extract the
number of samples from a data frame/matrix actually used in a regression model?
> 
> For instance I might have a data of 100 rows and 4 colums (1 response + 3
explanatory variables).  If 3 samples have one or more NAs in the explanatory
variable columns these samples will be dropped in any model:
> 
> my.model = lm(y ~ x + w + z, my.data)
> my.model = glm(y ~ x + w + z, my.data, family = binomial)
> my.model = polr(y ~ x + w + z, my.data)
> ?
> 
> I don't seem to be able to find one single method that works in the
exact same way -- irrespective of the model type -- to interrogate my.model to
see how many samples of my.data were actually used.  Is there such function or
do I need to hack something together?
> 
> Best wishes
> 
> Federico

I don't know that this would be universal to all possible R model
implementations, but should work for those that at least abide by "certain
standards"[1] relative to the internal use of ?model.frame.

In the case where model functions use 'model = TRUE' as the default in
their call (eg. lm(),  glm() and MASS::polr()), the returned model object will
have a component called 'model', such that:

  nrow(my.model$model)

returns the number of rows in the internally created data frame.

Note that 'model = TRUE' is not the default for many functions, for
example Terry's coxph() in survival or Frank's lrm() in rms.

Note also that the value of 'na.action' in the modeling function call
may have a potential effect on whether the number of rows in the retained
'model' data frame is really the correct value.

You can also use model.frame(), independently matching arguments passed to the
model function, to replicate what takes place internally in many modeling
functions. The result of model.frame() will be a data frame, again, subject to
similar limitations as above.

Regards,

Marc Schwartz

[1]: http://developer.r-project.org/model-fitting-functions.txt