> On Nov 11, 2012 9:10 AM, "peter dalgaard" <mailto:pda...@gmail.com>> wrote:
> >
> >
> > On Nov 11, 2012, at 16:57 , Gustaf Granath wrote:
> >
> > >
> > >
> > > M
> > > Påopklpnlbyjönvnmm
> > > M. Öplppkbk
M
Påopklpnlbyjönvnmm
M. Öplppkbkvöökä knbnnåöllpööåååtx hikkkhgxxx vj julöl
Sent from my iPhonejukujöbömjl jnmnmmm
Sorry for keeping things short
Gustaf Granath (phd)
Plant Ecology
Uppsala University
__
R-help@r-project.org mailing list
th relatively few data. You could set up a factor variable for the
site:time interaction (i.e. one level for each combination of site and time)
but that would be pushing it a bit for this number of data.
Simon
On Wednesday 22 October 2008 13:05, Gustaf Granath wrote:
R fellows,
I
odel without site
mod.no.site<-gam(y~s(x),data=mydata)
#Model including the factor Site. id=1 to get the same smothing
parameter at each factor level.
mod.with.site<-gam(y~Site+s(x,by=Site,id=1),data=mydata)
#AIC for the two models
AIC(mod.no.site,mod.with.site)
Thanks,
Gust
udging by your answers.
Thanks again for your help,
Gustaf
John Fox wrote:
> Dear Gustaf,
>
>
>> -Original Message-
>> From: Gustaf Granath [mailto:[EMAIL PROTECTED]
>> Sent: February-17-08 4:18 PM
>> To: John Fox
>> Cc: 'Prof Brian Ripley'
sity
> Hamilton, Ontario, Canada L8S 4M4
> 905-525-9140x23604
> http://socserv.mcmaster.ca/jfox
>
>
>> -Original Message-
>> From: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
>> Sent: February-17-08 6:42 AM
>> To: Gustaf Granath
>> Cc: John Fox;
er.ca/jfox
>
>
>> -Original Message-
>> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
>> project.org] On Behalf Of Gustaf Granath
>> Sent: February-16-08 11:43 AM
>> To: r-help@r-project.org
>> Subject: [R] Weird SEs with effect()
>>
>> H
e exp(). To be
honest, I get the feeling that Im on the wrong track here.
Basically, I want to know how SE is calculated in effect() (all I know
is that the reported standard errors are for the fitted values) and if
anyone knows what is going on here.
but I guess that
should not be a problem.
For code and data, see below.
Cheers,
Gustaf Granath, phd student
My code so far:
#Creating data
c(6.34,13.38,17.87)->y1
c(0.85,1.88,2.33)->y2
c(0,1.5,3)->x
cbind(y1,y2,x)->mydata
data.frame(mydata)->mydata
with(mydata, tapply(y1,x,mean)
t A1 and B1 combined as one mean
("the baseline")? or is it something else? Does this number actually
tell me anything
useful (2.716)??
What does the model (y = intercept + ??) look like then? I can't understand
how both factors (A and B) can have the same intercept?
Thanks in ad
table would give me a
quite similar result because I only have two levels (of the infl.treat
factor) and no interactions in the model.
## I'm afraid I have missed something trivial though so please, be gentle ;)
Cheers,
Gustaf Granath
--
Gustaf Granath (PhD student)
Dept of Plant Ecology
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