I just had a manuscript returned with the biggest problem being the
analysis. Instead of using principal components in a regression I've
been asked to analyze a few variables separately. So that's what I'm
doing.
I pulled a feather from young birds and we quantified certain aspects of
the color
Jeffrey Stratford [EMAIL PROTECTED] writes:
I just had a manuscript returned with the biggest problem being the
analysis. Instead of using principal components in a regression I've
been asked to analyze a few variables separately. So that's what I'm
doing.
I pulled a feather from young
Peter and list,
Thanks for the response. A did add box as a factor (box -
factor(box)). Julian should be linear - bluebird chicks are bluer as
the season progresses from March to August.
I did try the following
rtot.lme - lmer(rtot ~ sex +(purban|box:chick) + (purban|box), data=bb,
: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of
Jeffrey Stratford
Sent: Thursday, October 05, 2006 9:27 AM
To: [EMAIL PROTECTED]
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] glm with nesting
Peter and list,
Thanks for the response. A did add box as a factor (box -
factor
Harold and list,
I've changed a few things since the last time so I'm really starting
from scratch.
I start with
bbmale - read.csv(c:\\eabl\\2004\\feathers\\male_feathers2.csv,
header=TRUE)
box -factor(box)
chick - factor(chick)
Here's a sample of the data
PROTECTED]; r-help@stat.math.ethz.ch
Subject: Re: [R] glm with nesting
Harold and list,
I've changed a few things since the last time so I'm really starting
from scratch.
I start with
bbmale - read.csv(c:\\eabl\\2004\\feathers\\male_feathers2.csv,
header=TRUE)
box -factor(box