This sounds like you need a mixed effects model (e.g. lme or lmer
instead of lm) instead of the possibly spurious adhocery that you
describe. I suggest you post your message on the SIG-mixed-effects
list and/or get help from a local statistician
Cheers,
Bert
On Thu, Sep 15, 2011 at 1:49 AM, RCull
Many thanks to all of you! AV plots are what I am trying to plot. Perhaps to
reduce confusion I can give you an example of what I am doing:
I am looking at behaviour of re-sighted individuals over two time points. I
use lm() on these data and obtain the residuals.
Then I am interested to know wh
Dear S.,
On Wed, 14 Sep 2011 16:48:55 +0100
S Ellison wrote:
> > From: John Fox [mailto:j...@mcmaster.ca]
> > Subject: Re: [R] Force regression line to a 1:1 relationship
> > If I understand correctly what you said, a plot of residuals
> > against an X is not an added
> From: John Fox [mailto:j...@mcmaster.ca]
> Subject: Re: [R] Force regression line to a 1:1 relationship
> If I understand correctly what you said, a plot of residuals
> against an X is not an added-variable plot. An added variable
'Added variable plots' was the phras
On 09/14/2011 11:36 AM, David Winsemius wrote:
On Sep 14, 2011, at 10:52 AM, Patrick Breheny wrote:
The latter type of plot is called a "partial regression plot" or
"added variable plot". They are discussed in any regression
textbook, as well as wikipedia and probably dozens of other web sites.
On Sep 14, 2011, at 10:52 AM, Patrick Breheny wrote:
On 09/13/2011 04:27 PM, David Winsemius wrote:
...
Also should be plotting against fitted() rather than regressors.
...
Both types of plots (vs. fitted values and vs. regressors) are very
common. The former is a default plot in R, the f
Dear S Ellison,
If I understand correctly what you said, a plot of residuals against an X is
not an added-variable plot. An added variable plot is constructed by regressing
Y on all the Xs but the focal X, and regressing the focal X on all the other
Xs; then the residuals from the first regress
> -Original Message-
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Patrick Breheny
>
> The latter type of plot is called a "partial regression plot"
> or "added variable plot". They are discussed in any
> regression textbook, as well as wi
On 09/13/2011 04:27 PM, David Winsemius wrote:
...
Also should be plotting against fitted() rather than regressors.
...
Both types of plots (vs. fitted values and vs. regressors) are very
common. The former is a default plot in R, the first one that appears
if you submit:
fit <- lm(y~X)
pl
On Sep 13, 2011, at 11:56 AM, David Winsemius wrote:
On Sep 13, 2011, at 11:44 AM, David Winsemius wrote:
On Sep 13, 2011, at 9:43 AM, RCulloch wrote:
Dear John,
Thank you for that, and for explaining why the abline() command
wont/dosen't
work. The approach is based on reviewers commen
On Sep 13, 2011, at 11:44 AM, David Winsemius wrote:
On Sep 13, 2011, at 9:43 AM, RCulloch wrote:
Dear John,
Thank you for that, and for explaining why the abline() command
wont/dosen't
work. The approach is based on reviewers comments that I am a tad
sceptical
about myself but yet curi
On Sep 13, 2011, at 9:43 AM, RCulloch wrote:
Dear John,
Thank you for that, and for explaining why the abline() command wont/
dosen't
work. The approach is based on reviewers comments that I am a tad
sceptical
about myself but yet curious enough to test their suggestion..I
don't
think
David & JC,
Excellent point, of course it does - and of course that is (should have
been) obvious!!! That is what I get for taking a reviewers
comment/suggestion as gospel without applying a bit of thought!
I'm off to go and kick myself.
Cheers,
Ross
--
View this message in context:
htt
Dear John,
Thank you for that, and for explaining why the abline() command wont/dosen't
work. The approach is based on reviewers comments that I am a tad sceptical
about myself but yet curious enough to test their suggestion..I don't
think it is very straightforward to explain; however, it in
Dear Ross,
But the residuals are just y - x.
Best,
John
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of RCulloch
> Sent: September-13-11 9:19 AM
> To: r-help@r-project.org
> Subject: Re: [R] Force regres
Dear JC,
> -Original Message-
> From: Jean-Christophe BOUËTTÉ [mailto:jcboue...@gmail.com]
> Sent: September-13-11 9:35 AM
> To: John Fox
> Cc: RCulloch; r-help@r-project.org
> Subject: Re: [R] Force regression line to a 1:1 relationship
>
> And you can easily get
..@r-project.org [mailto:r-help-bounces@r-
project.org] On Behalf Of RCulloch
Sent: September-13-11 7:03 AM
To: r-help@r-project.org
Subject: [R] Force regression line to a 1:1 relationship
Hello,
I appreciate this is likely to be an easy question. I am trying to
obtain the residuals from a linear
y
> McMaster University
> Hamilton, Ontario, Canada
> http://socserv.mcmaster.ca/jfox
>
>
>
>> -Original Message-
>> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
>> project.org] On Behalf Of RCulloch
>> Sent: September-13-11 7:03 AM
>> To: r-help
yes, that is correct. The idea being that I want to know the residuals of the
data points compared to a 1:1 line (as shown in the plot), if that makes
sense? I appreciate that this might not be considered a typical approach,
and it would probably take a while to explain (defend) why I am doing it!
t.org] On Behalf Of RCulloch
> Sent: September-13-11 7:03 AM
> To: r-help@r-project.org
> Subject: [R] Force regression line to a 1:1 relationship
>
> Hello,
>
> I appreciate this is likely to be an easy question. I am trying to
> obtain the residuals from a linear regression
Just to clarify things before trying to answer: by a "1:1 relationship" do
you mean you want the regression slope to be equal to 1 "no matter what"?
Michael
On Tue, Sep 13, 2011 at 7:03 AM, RCulloch wrote:
> Hello,
>
> I appreciate this is likely to be an easy question. I am trying to obtain
>
Hello,
I appreciate this is likely to be an easy question. I am trying to obtain
the residuals from a linear regression where the line is forced to have a
1:1 relationship.
An example of the data:
A<-c(0.9803922, 1.3850416, 0.8241758, 0.000, 0.4672897, 1.1904762,
0.000, 0.9456265,
1.51
22 matches
Mail list logo