Displaying 20 results from an estimated 800 matches similar to: "MCMCglmm error with multinomial distribution"
2011 Jun 25
2
Vector with factors inside lists/tuples
Hi, this seems like a strange question, but in R is there a function that
can handle vectors containing factors inside lists/tuples? Or is there some
other approach/functions I can use?
Like for example
V1
"{"Harry","Brown")"
"{"Brown","Harry")"
I want to use these variables in a machine learning setting, And don't want
to convert
2012 Nov 06
1
Multinomial MCMCglmm
Thanks for your answers Stephen and Ben,
I hope I am posting on the correct list now.
I managed so far to run the multinomial model with random effect with the
following command:
MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~
habitat:trait,random=~idh(trait):mesh,family="multinomial12",
data=dataA,rcov=~trait:units)
(where multiple responses are different species,
Habitat
2018 Mar 23
0
MCMCglmm multinomial model results
> On Mar 22, 2018, at 1:31 PM, Michelle Kline <michelle.ann.kline at gmail.com> wrote:
>
> Hi,
>
> Thanks in advance for any help on this question. I'm running multinomial
> models using the MCMCglmm package. The models have 5 outcome variables
> (each with count data), and an additional two random effects built into the
> models. The issue is that when I use
2018 Mar 24
1
MCMCglmm multinomial model results
Hi David,
Thanks for your comment. I haven't posted the data because they are
unpublished and include human subjects so there are issues with sharing on
a list serv, but I thought perhaps someone had encountered a similar
problem and would already know the answer.
I will reconsider whether my University's ethics approval would allow me to
post the data and update the question if I think
2018 Mar 22
2
MCMCglmm multinomial model results
Hi,
Thanks in advance for any help on this question. I'm running multinomial
models using the MCMCglmm package. The models have 5 outcome variables
(each with count data), and an additional two random effects built into the
models. The issue is that when I use the following code, the summary only
gives me results for four of the outcome variables.
Here is the code for my model:
m3.random
2012 Sep 12
7
multinomial MCMCglmm
Dear all,
I would like to add mixed effects in a multinomial model and I am trying
to use MCMCglmm for that.
The main problem I face: my data set consits of a trapping data set,
where the observation at eah trap (1 or 0 for each species) have been
aggregated per traplines. Therefore we have a proportion of
presence/absence for each species per trapline.
ex:
ID_line mesh habitat Apsy Mygl
2009 Aug 24
2
Number of CPU's
Any way to get access to the number of CPU's, optionally their type,
from within R? In linux I can just read /proc/cpuinfo but for
win/mac ?
Thanks!
H?vard
--
H?vard Rue
Department of Mathematical Sciences
Norwegian University of Science and Technology
N-7491 Trondheim, Norway
Voice: +47-7359-3533 URL : http://www.math.ntnu.no/~hrue
Fax : +47-7359-3524 Email: havard.rue
2011 Jun 01
1
How to write random effect in MCMCglmm
Hi All,
The data set that I have is a cluster data, and I want to run a HLM mixed
model with multi-level response. Here is my data set:
response:
- Level (num: 1, 2, 3, 4, 5 - 5 levels)
Covariates:
- Type (Factor: A, B, C - 3 levels)
- yr (num: 2006, 2007, ...)
- Male (num: 0=not Male, 1=Male - 2 levels)
- Ethnicity (Factor: A, B, H, ..., - 7 levels)
- ELL (num: 0, 1, - 2
2011 Feb 17
0
Multi-response MCMCglmm (gaussian and zapoisson)
Dear MCMCglmm users,
I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects.
In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for
2012 Feb 08
0
MCMCglmm
Dear Jarrod,
I have a data set where residual have a heavy-tailed distribution with
some extreme residual values and consequently the distribution deviates
from the Gaussian one.
Is it possible to include an skewed-normal density for the residual in
MCMCglmm package?
I have done the analysis of this data with both ASReml & MCMCglmm. The
results are similar and outcome from MCMCglmm
2012 Jun 23
0
Using at.level() with a MCMCglmm zero-inflated poisson model
I have a question for users of MCMCglmm that have experience implementing
the zero-inflated poisson model.
I find that the documentation, and previous questions, do not offer a lot
of clear guidance on specifying and interpreting the zipoisson model. In
particular, I see a lot of zero-inflated poisson examples that use the
at.level(trait, x):variableName syntax.
Specifically, the MCMCglmm
2012 Feb 13
1
MCMCglmm with cross-classified random effects
Dear R-users,
I would like to fit a glmm with cross-classified random effects with
the function MCMCglmm. Something along the lines:
model1<-MCMCglmm(response~pred1, random=~re1+re2, data=data)
where re1 and re2 should be crossed random effects. I was wondering
whether you could tell me specifying cross-classified random effects
in MCMCglmm requires a particular syntax? Are there any
2012 May 03
0
LME4 to MCMCglmm
Hi all,
I am trying to run an lme4 model (logistic regression with mixed effects) in
MCMCglmm but am unsure how to implement it properly.
Currently, my lme4 model formula looks as follows: "outcome ~ (1 + var1 +
var2 | study) + var1 + var2"
In English, this means that I am fitting a random effects model, where the
intercept, var1 and var2 are jointly distributed according to study.
2017 Aug 23
0
MCMCglmm issue
When I try to use the following code, I get the error message shown. This
is quite confusing to me, insofar as family is a recognized argument for
MCMCglmm. Can anyone spot an obvious glitch?
model1 <-MCMCglmm(fixed = NoRRVpos ~ Year, random = ~County,
family="zipoisson", data=Rabies_Project_Init)Error in MCMCglmm(fixed =
NoRRVpos ~ Year, random = ~County, family =
2012 May 02
0
MCMCglmm priors including phylogeny
Hi all,
I'm hoping I might be able to get some help with some issues specifying priors for MCMCglmm.
I'm trying to fit a gaussian glmm using MCMCglmm to a data set with two (correlated) response variables. The response variables are both logit-transformed proportions (there are a few reasons why I've chosen these with gaussian error over binomal glmm, which I won't go into).
2009 Dec 20
1
Problems in installing MCMCglmm package
Dear R-Helpers,
I am having troubles with installing with MCMCglmm package and I get the
following error with a package "Matrix"
Warning in library(pkg, character.only = TRUE, logical.return = TRUE,
lib.loc = lib.loc) :
there is no package called 'Matrix'
Error: package 'Matrix' could not be loaded
Execution halted
ERROR: lazy loading failed for package
2018 May 01
2
Specifying priors in a multi-response MCMCglmm
Hi Bert,
That was distinctly unhelpful, and your outward hostility to a field you
obviously don't understand reveals a regrettable level of ignorance.
By the way, my research is Anthropology despite my job title.
Michelle
On Tue, May 1, 2018 at 2:48 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
> 1. (Mainly) Statistical issues are generally off topic on this list.
> You
2010 Mar 29
2
mcmcglmm starting value example
Hi R-users:
Can anyone give an example of giving starting values for MCMCglmm?
I can't find any anywhere.
I have 1 random effect (physicians, and there are 50 of them)
and family="ordinal"?
How can I specify starting values for my fixed effects? It doesn't seem to have the option to do so.
Thanks, Ping
2011 Aug 23
1
pMCMC and HPD in MCMCglmm
Dear R users,
I?d like to pose aquestion about pMCMC and HDP.
I have performed a mixed logistic regression by MCMCglmm (a very good package)
obtaining the following results:
Iterations = 250001:799901
Thinning interval = 100
Sample size = 5500
DIC: 10.17416
G-structure: ~ID_an
post.mean l-95% CI u-95% CIeff.samp
ID_an 0.7023 0.0001367 3.678 2126
R-structure: ~units
post.mean l-95%
2018 May 03
1
MCMCglmm - metric of the estimates
Hi,
my question is probably amateurish but I can't seem to find the answer
anywhere.
In what metric are the MCMCglmm package's posterior means for family =
"categorical"?
I suppose that they can't be odds ratios and probabilites as my numbers are
outside their bounds. So I'm thinking ? are they just basic regression
coefficients conceptually equal to those obtained by