1. A mess, because you failed to read and follow the posting guide:
This is a **plain text** mailing list, which means that html can get
mangled, as you have demonstrated.
2. And wrong list: the r-sig-mixed-models list is where this would be
more suitable.
Cheers,
Bert
Bert Gunter
"The trouble
Hi,I have tumor growth curve data for a bunch of different mice in various
groups. I want to compare the growth curves of the different groups to see if
timing of drug delivery changed tumor growth.I am trying to run a mixed models
with repeated measures over time with each mouse as a random eff
Greetings David,
I am new to R and neglected to check vigorously for missing values.
Apologize for posting without checking and finding the one NA.
I appreciate your help.
Thanks.
James F. Henson
On Fri, May 27, 2016 at 1:49 PM, David Winsemius wrote:
>
>> On May 27, 2016, at 10:07 AM, James Hen
> On May 27, 2016, at 10:07 AM, James Henson wrote:
>
> Greetings Jeff,
> You are correct that the unequal number of levels is not the problem.
> I revised the data frame so that the number of levels was equal and
> the same error message occurred. The code is below, and the
> Eboni2.txt file i
Greetings Jeff,
You are correct that the unequal number of levels is not the problem.
I revised the data frame so that the number of levels was equal and
the same error message occurred. The code is below, and the
Eboni2.txt file is attached. This problem baffles me. I appreciate
any help.
Best r
Please keep the mailing list in the loop by using reply-all.
I don't think there is a requirement that the number of levels is equal, but
there may be problems if you don't have the minimum number of records
corresponding to each combination of levels specified in your model.
You can change th
You forgot to show the commands to us that you used to read the data in with
(your example is not "reproducible"). This step can make all the difference in
the world as to whether your analysis commands will work or not.
--
Sent from my phone. Please excuse my brevity.
On May 25, 2016 11:59:06
Greetings R community,
My aim is to analyze a mixed-effects model with temporal pseudo-replication
(repeated measures on the same experimental unit) using ‘nlme’. However,
my code returns the error message “Error in na.fail.default’, even though
the data frame does not contain missing values. My
Karina Charest Castro gmail.com> writes:
>
> Hi!
This question is more appropriate for r-sig-mixed-mod...@r-project.org.
Please repost there (I will add a few questions/comments below that
you should probably address when you repost)
> I am trying to do a glmer.nb but get this error:
> Error
Hi!
I am trying to do a glmer.nb but get this error:
Error in eval(expr, envir, enclos) :
..2 used in an incorrect context, no ... to look in
My data structure is data.frame
Here my data set: d1_2
Year ID Age Reproductive_status Rank_Residuals Asso.Y1 TotalY1
1994 109 8 Lactating 0.23947902 9 4
Hello everybody,
I have count data and with these data, I would like to build a mixed
model by using the function glmer(). In a first time, I calculated the c-hat of
a simple model with glm() to verify overdispersion and I found a c-hat = 18. I
also verified overdispersion in the mixed model
This has basically nothing to do with R, so please don't post here.
You may wish to try the r-sig-mixed-models list, however. They are
more sympathetic to such questions -- and what are likely to be the
torrent from you that follows.
-- Bert
On Fri, Feb 15, 2013 at 9:17 AM, Bone, Jonathan
wrote
Hi,
I am creating a mixed model based on a experiment where each subject has 2
repeats. In some instances though there is only data for one of a given
subjects repeats for most there is data for both. Can I still justify having
subject as a random effect?
Thanks,
Jonathan
[X]
[X]
[X]
[X]
[X]
Luis Reino isa.utl.pt> writes:
>
> Dear all,
> We want to test if the invasiveStatus is predicted by the amount
> (quant) of animals arriving to a country of a certain species
> (taxonid). We are using lmer to perform the model.
In general lmer questions belong on r-sig-mixed-mod...@r-proje
Dear all,
We want to test if the invasiveStatus is predicted by the amount (quant) of
animals arriving to a country of a certain species (taxonid). We are using lmer
to perform the model.
The model is:
lmer(invasiveStatus~I(log(quant+1))+I(log(inDegree+1))+(1|taxonid)+(1|country),
family=binom
My name is Giovanna and I am a PhD student in Norway.
I am a beginner with statistics and R, hence my ignorance. Apologies from
now.
I have been collecting data on time performances of 5 subjects using a 1:3
scale tower yarder. The task was consisting in yarding 5 small logs placed on
perm
Why did you use the 'lower.tri' syntax?
Does this work for you?
lme(Y~Random, data = DATA,
random = list(Random = pdSymm(CovM,~Random)))
Kevin
On Wed, Jul 11, 2012 at 9:27 AM, Marcio wrote:
> Dear Simon,
> Thanks for the quick reply.
> Unfortunately I don't have access to Pinheiro and Bates. I
Dear Simon,
Thanks for the quick reply.
Unfortunately I don't have access to Pinheiro and Bates. I tried googling
the pdSymm and lme but I still cannot get the syntax right.
In my model, I only have 1 random factor with repetitions (groups) (e.g. 2
records per each level)
I am pasting bellow a ver
Aah. From your model description, you are more interested in the
covariance structure of the random effects, rather than the residuals.
You will then need to use the pdSymm class in the specification of the
random effects. See Pinheiro and Bates pp 157-166.
Cheers,
Simon.
On 06/07/12 11:43,
You need to look at the corSymm correlation class for nlme models.
Essentially, in your lme call, you need to do
correlation=corSymm(mat[lower.tri(mat)], fixed=TRUE)
Where mat is your (symmetric) variance-covariance matrix. Remember to
make sure that the rows and columns of mat are in the sam
Hi folks,
I was wondering how to run a mixed models approach to analyze a linear
regression with a user-defined covariance structure.
I have my model
y = xa +zb +e and
b ~ N (0, C*sigma_square). (and a is a fixed effects)
I would like to provide R the C (variance-covariance) matrix
I can easi
Hello,
I've run a Proc Mixed function on a set of data in SAS.
The data was a result of an experiment that measured % viability over time and
I wanted to compare a Large sample lets say 50L to a small sample say 5L. And
compare the % viability between the 2 sizes to see if I get the same answ
Hi Thierry,
Thanks again! You are a great help!!
I have taken habitat out, and then run it with style but still the same
problem exists, so I have taken both style and habitat out. The problem
here is it leaves me with only 3 parameters and because they are all
correlated I cant use them in t
an be extracted from a given body of
data.
~ John Tukey
-Oorspronkelijk bericht-
Van: Emma Stone [mailto:emma.st...@bristol.ac.uk]
Verzonden: vrijdag 13 maart 2009 11:08
Aan: ONKELINX, Thierry; Emma Stone; r-help@r-project.org
Onderwerp: RE: [R] Mixed models fixed effects
Hi Thierry,
Thank
Emma Stone [mailto:emma.st...@bristol.ac.uk]
Verzonden: vrijdag 13 maart 2009 10:50
Aan: ONKELINX, Thierry; Emma Stone; r-help@r-project.org
Onderwerp: RE: [R] Mixed models fixed effects
Hi Thierry,
That's great thanks!
I have done as you have said but I keep getting a warning message here
is
my code:
2009 10:50
Aan: ONKELINX, Thierry; Emma Stone; r-help@r-project.org
Onderwerp: RE: [R] Mixed models fixed effects
Hi Thierry,
That's great thanks!
I have done as you have said but I keep getting a warning message here
is
my code:
G1Hvol<-glmer(passes~hvolume+style+habitat(1|Site),family =
Emma Stone
Verzonden: woensdag 11 maart 2009 15:29
Aan: r-help@r-project.org
Onderwerp: Re: [R] Mixed models fixed effects
Dear All,
This may sound like a dumb question but I am trying to use a mixed model
to
determine the predictors of bat activity along hedges within 8 sites. So
my
response is c
rinner
>>
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of
>> data.
>> ~ John Tukey
>>
>> -Oorspronkelijk bericht-
>> Van: r-help-boun...@r-proj
ijk bericht-
Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
Namens Emma Stone
Verzonden: woensdag 11 maart 2009 15:29
Aan: r-help@r-project.org
Onderwerp: Re: [R] Mixed models fixed effects
Dear All,
This may sound like a dumb question but I am trying to use a mixed mo
Re: [R] Mixed models fixed effects
Dear All,
This may sound like a dumb question but I am trying to use a mixed model
to
determine the predictors of bat activity along hedges within 8 sites. So
my
response is continuous (bat passes) my predictors fixed effects are
continuous (height met
Dear All,
This may sound like a dumb question but I am trying to use a mixed model to
determine the predictors of bat activity along hedges within 8 sites. So my
response is continuous (bat passes) my predictors fixed effects are
continuous (height metres), width (metres) etc and the random ef
There is no generally agreed upon notion of random effects for quantile
regression applications. Insofar as one is willing to accept the
idea that
random effects are just "shrunken fixed effects" one can consider
similar
schemes in the QR context; one such is described in
“Quantile Regressio
Dear R,
I have a question concerning quantile regression models.
I am using the quantile regression model to test the relationship between
abalone and the percentage cover of algae etc at different sites and depths.
An example is
fit<-rq(abalone~%coversessileinvertebrates+factor(Depth)+factor(S
I believe that glmm.admb relies on the proprietary software ADMB. If
so, your question is inappropriate for this list which is for
discussion and assistance regarding the open source and freely
available software R.
There are ways of fitting a generalized linear mixed model in R, in
particular th
Hello Folks,
I'm after some help regarding mixed models.
Basically I have sampled a number of different animals at 10 independent sites
and am trying to create a mixed model to account for the variation between
sites my current model looks like this:
mm<-glmm.admb(Hep~Sex+Mass, random=~Mouse, g
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