Dear R users,
We are trying to analyse microarray data. The experiment compares two
conditions (light vs. dark). we would like to use a mixed-model ANOVA but we
don't know much about it. could you please help?
the experiment test the effect of light on a tissue and compare tissues
maintained
Dear experts,
I'm trying to transfer a mixed model developed in SAS to R. This it what
it looks like in SAS:
proc mixed method=ml;
class a b c subj;
model y = a|b|c;
repeated /subject=subj type=ar(1);
I tried something like this in R:
mixed - lme(y ~ a + b + c + a*b + a*c + b*c +
Dear R-list,
I thought that I would let some of you know of a free R package, glmm.ADMB, that
can handle mixed models for overdispersed and zero-inflated count data
(negativebinomial and poisson).
It was built using AD Model Builder software (Otter Research) for random effects
modeling and is
Thanks
Most thoughtful...
Regards
S
From: Spencer Graves [mailto:[EMAIL PROTECTED]
Sent: Mon 27/06/2005 19:52
To: Stephen
Cc: Douglas Bates; r-help@stat.math.ethz.ch
Subject: Re: [R] Mixed model
I often think carefully about what I want and store
To: Stephen
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Mixed model
On 6/26/05, Stephen [EMAIL PROTECTED] wrote:
Hi All,
I am currently conducting a mixed model. I have 7 repeated measures on
a
simulated clinical trial. If I understand the model correctly, the
outcome is the measure
Thanks and comments appreciated
Regards
Stephen
From: Douglas Bates [mailto:[EMAIL PROTECTED]
Sent: Sun 26/06/2005 17:01
To: Stephen
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Mixed model
On 6/26/05, Stephen [EMAIL PROTECTED] wrote:
Hi All
Hi All,
I am currently conducting a mixed model. I have 7 repeated measures on a
simulated clinical trial. If I understand the model correctly, the
outcome is the measure (as a factor) the predictors are clinical group
and trial (1-7). The fixed factors are the measure and group. The
On 6/26/05, Stephen [EMAIL PROTECTED] wrote:
Hi All,
I am currently conducting a mixed model. I have 7 repeated measures on a
simulated clinical trial. If I understand the model correctly, the
outcome is the measure (as a factor) the predictors are clinical group
and trial (1-7).
@stat.math.ethz.ch
Subject: Re: [R] Mixed model
On 6/26/05, Stephen [EMAIL PROTECTED] wrote:
Hi All,
I am currently conducting a mixed model. I have 7 repeated measures on
a
simulated clinical trial. If I understand the model correctly, the
outcome is the measure (as a factor) the predictors
Hi Doug and Spencer,
Many thanks - Excellent!
All worked out nicely
Regards
Stephen
From: Spencer Graves [mailto:[EMAIL PROTECTED]
Sent: Mon 20/06/2005 17:54
To: Stephen
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Mixed model
(comments in line
Dear Fellow R users,
I am fairly new to R and am currently conducting a mixed model.
I have 7 repeated measures on a simulated clinical trial
If I understand the model correctly, the outcome is the measure (as a
factor) the predictors are clinical group and trial (1-7). The fixed
(comments in line)
Stephen wrote:
Dear Fellow R users,
I am fairly new to R and am currently conducting a mixed model.
I have 7 repeated measures on a simulated clinical trial
If I understand the model correctly, the outcome is the measure (as a
factor) the predictors
On 6/20/05, Spencer Graves [EMAIL PROTECTED] wrote:
(comments in line)
Stephen wrote:
Dear Fellow R users,
I am fairly new to R and am currently conducting a mixed model.
I have 7 repeated measures on a simulated clinical trial
If I understand the model correctly, the
Hi,
I am new to this list as a poster, but a reader for some time.
I've using R for several weeks now, and I have a lot of questions about
certain procedures. Here I go:
I want to test if there are differences in the time spent by pollinators
visiting flowers of a given plant species,
Hi,
I am new to this list as a poster, but a reader for some time.
I've using R for several weeks now, and I have a lot of questions about
certain procedures. Here I go:
I want to test if there are differences in the time spent by pollinators
visiting flowers of a given plant species,
Have you also tried lmer in library(lme4)? This is newer and
better in many ways. Unfortunately, I see only one example in the Help
file, but you might be able to figure out how to use lmer from the help
file and from the 125 hits on RSiteSearch(lmer).
The definitive work
and the newest R-new in the www.r-project.org has an article about how to use
the lmer function.
On Fri, 17 Jun 2005 08:43:41 -0700
Spencer Graves [EMAIL PROTECTED] wrote:
Have you also tried lmer in library(lme4)? This is newer and
better in many ways. Unfortunately, I see only
Hello all,
I have problem with setting up random effects.
I have a model:
y=x1+x2+x1*x2+z1+z1*x2
where x1, x2, x1*x2 are fixed effects
and z1, z1*x2 are random effects (crossed effects)
I use library(nlme) 'lme' function.
My question is: how I should set up random effects?
I did
the most recent version of R news for more info on this topic.
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of NATALIA F
TCHETCHERINA
Sent: Wednesday, May 25, 2005 11:50 AM
To: r-help@stat.math.ethz.ch
Subject: [R] mixed model
Hello all,
I have problem
NATALIA F TCHETCHERINA wrote:
Hello all,
I have problem with setting up random effects.
I have a model:
y=x1+x2+x1*x2+z1+z1*x2
where x1, x2, x1*x2 are fixed effects
and z1, z1*x2 are random effects (crossed effects)
I use library(nlme) 'lme' function.
My question is: how I should
PM
Subject: [R] mixed model question
I am trying to fit a linear mixed model of the form
y_ij = X_ij \beta + delta_i + e_ij
where e_ij ~N(0,s^2_ij) with s_ij known
and delta_i~N(0,tau^2)
I looked at the ecme routine in package:pan, but this routine
does not allow for different Vi (variance
I am trying to fit a linear mixed model of the form
y_ij = X_ij \beta + delta_i + e_ij
where e_ij ~N(0,s^2_ij) with s_ij known
and delta_i~N(0,tau^2)
I looked at the ecme routine in package:pan, but this routine
does not allow for different Vi (variance covariance matrix of
the e_i vector)
Dear all,
I've got a big problem. I try to analyse my data using R with a mixed model
ANOVA without useful results and success.
My data are as follows:
3 factors (Treatment, Site, Subsite) with 'Subsite' as random factor and
nested into 'Site'.
I want to analyse the effects of the three main
Try the book Mixed-effects model in S and S-PLus by Pinheiro and Bates.
They have pretty good examples of code and analysis techniques to tackle
your data.
Francisco
From: Lars Peters [EMAIL PROTECTED]
To: [EMAIL PROTECTED]
Subject: [R] Mixed model ANOVA with a nested design
Date: Fri, 9 Jul
Hi there,
I try to compare the mixed model package lme by Splus and R. I used the
dataset Ovary and the following code assuming AR(1) model for the error term:
lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data=Ovary, random =
pdDiag(~sin(2*pi*Time) ) , correlation=corAR1() )
But I got
Lei Liu [EMAIL PROTECTED] writes:
I try to compare the mixed model package lme by Splus and R. I used the
dataset Ovary and the following code assuming AR(1) model for the error term:
lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data=Ovary, random =
pdDiag(~sin(2*pi*Time) ) ,
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