Hi Michael,
Try the attached. The only change to your script is in the first line
where I explicitly tell hatvalues to use methods (the "infmlm" class
stays). I also commented out all your TESTME at the end.
source('mlminfl-testHELP.R')
Now this should have worked for you too. Let me know. Sorry
On 2/11/2012 12:00 PM, ilai wrote:
You are setting a new class ("inflmlm") at the end of mlm.influence.
Remove that second to last line and enjoy your new S3 method.
Thanks for the suggestion, but it doesn't help -- I still get the same
behavior whether mlm.influence returns a classed object or
You are setting a new class ("inflmlm") at the end of mlm.influence.
Remove that second to last line and enjoy your new S3 method.
I'm not sure, but I think it is just the new class "inflmlm" applied
to inf in the formals of hatvalues.mlm confused the dispatch
mechanism. You would think the error
On 2/10/2012 4:09 PM, Henrik Bengtsson wrote:
So people may prefer to do the following:
hatvalues.mlm<- function(model, m=1, infl, ...)
{
if (missing(infl)) {
infl<- mlm.influence(model, m=m, do.coef=FALSE);
}
hat<- infl$H
m<- infl$m
names(hat)<- if(m==1) infl$subsets
For these type of setups, I typically turn to "default" values, e.g.
hatvalues.mlm <- function(model, m=1, infl=NULL, ...)
{
if (is.null(infl)) {
infl <- mlm.influence(model, m=m, do.coef=FALSE);
}
hat <- infl$H
m <- infl$m
names(hat) <- if(m==1) infl$subsets else apply(infl$s
On 2/9/2012 6:24 PM, ilai wrote:
You do not provide mlm.influence() so your code can't be reproduced.
Or did you mean to put lm.influence() in the formals to your hatvalues.mlm ?
If yes, then 1) you have a typo 2) lm.influence doesn't allow you to
pass on arguments, maybe try influence.lm inste
You do not provide mlm.influence() so your code can't be reproduced.
Or did you mean to put lm.influence() in the formals to your hatvalues.mlm ?
If yes, then 1) you have a typo 2) lm.influence doesn't allow you to
pass on arguments, maybe try influence.lm instead.
Elai
On Thu, Feb 9, 2012 at 1
I'm trying to write some functions extending influence measures to
multivariate linear models and also
allow subsets of size m>=1 to be considered for deletion diagnostics.
I'd like these to work roughly parallel
to those functions for the univariate lm where only single case deletion
(m=1) dia
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