Hello,
I am looking for some suggestions as far as packages for doing
multilevel multinomial regression analysis.
Most appreciated.
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Dear Saul,
The most commonly used mixed-effect models software in R, in the lme4 and nlme
packages, use the Laird-Ware form of the model, which isn't explicitly
hierarchical. That is, higher-level variables are simply invariant within
groups and appear in the model formula in the same manner
Hello,
I have data with workers within departments. I am interested in testing the
effects of peers' satisfaction on employees' productivity. To assess peer
satisfaction, I calculate, for each employee, the average satisfaction of
the employees' peers within the department. In other words, I
Hello,
I am trying to run a *multilevel moderated mediation model in R*, with data
nested in three levels (children, within classes, within schools). All of
my variables are at the individual level, but I still need to account for
the nested nature of the data.
In separate analyses of
Dear David,
R-sig-mixedmodels is a better mailing list for this kind of question.
1) yes
2) use (Treatment | Random_Assignment_Block) instead of (1 |
Random_Assignment_Block)
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
1. Please post in plain text, not HTML, which can get garbled.
2. I believe your syntax is incorrect, but I haven't used lmer in a
while, and so what I believe should be ignored anyway. HOWEVER, there
is a SIG (special interest group) for mixed models, and you have a
much better chance of getting
I am conducting a multilevel regression analysis on the effect of an
intervention on student test results, and am not sure how to implement the
necessary R code to correctly capture the nested structure.
The outcome measure for the study is whether a student passed or failed a
final exam. The
Dear All,
I want to test a multilevel/cross-level mediated moderation model (Level 1:
IV, DV; and Level 2: Mod, Med). The dataset can be grouped by firm_id and I
use mediate{mediation} with lmer class to do it...
Can anyone suggest if the following models are specified correctly?
I don't know
No, I don't think that will make any difference.
1) Post this to the r-sig-mixed-models list rather than here, as you
are likely to get a much better answer there.
2) Did you realize that treatment is a linear term in the fixed
effects portion, not a factor? If you don't understand the question,
Have you tried running it using lmer() in lme4 instead, see if that helps?
Patrick
2014-03-27 6:21 GMT-06:00 Laura Thomas skagandboneg...@hotmail.com:
Hi All,
I am using R for the purpose of multilevel modelling for the first time. I am
trying to examine individuals interpersonal changes in
Hi All,
I am using R for the purpose of multilevel modelling for the first time. I am
trying to examine individuals interpersonal changes in the dependent variable
over time and how this varies between groups.
I am using the following code:
treat.lme1-lme(DependentVariable~Treatment*I(Time-1),
Thanks for your prompt responses. I will look at the readings you sugggest.
One quick question, sampling weights can be applied in clmm2?
Thank you,
Wander
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I forgot to add. How can I estimate cluster-robust standard errors and 95%
confidence intervals for odds ratios?
Thank you,
Wander
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Sent from the R help mailing list
-project.org] On
Behalf Of shkingdom
Sent: Saturday, 1 March 2014 11:57
To: r-help@r-project.org
Subject: [R] Multilevel analysis for ordinal responses
Dear all,
I need to fit a multielvel model for an ordinal response. Does R have a
command for conducting a multilevel ordinal logistic regression when
Yes; see clm and clmm2 (mixed effects) in the ordinal package for
fitting proportional odds models. See section 3 of
http://cran.r-project.org/web/packages/ordinal/vignettes/clm_tutorial.pdf
to see how to test the proportional odds assumption with clm - it is
equivalent for clmm2 models. For an
Dear all,
I need to fit a multielvel model for an ordinal response. Does R have a
command for conducting a multilevel ordinal logistic regression when the
model violates the parallel regression or proportional odds assumption?
Additionally, are there any tests to check the parallel regression
Anyone is working with the upgrade of any multilevel package for apply
sampling weights?
Thanks
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PLEASE do read the posting
I have an example of multilevel analysis with 3 levels, but data are
non-normally distributed. In case of normal distribution, I would perform
multilevel linear analysis using lme function, but what should I do in case
of non-normal distribution?
thanks,
Srecko
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On Sep 30, 2013, at 2:50 PM, srecko joksimovic wrote:
I have an example of multilevel analysis with 3 levels, but data are
non-normally distributed. In case of normal distribution, I would perform
multilevel linear analysis using lme function, but what should I do in case
of non-normal
I thought so, but then I found this:
Normality
The assumption of normality states that the error terms at every level of
the model are normally distributed
maybe I misinterpreted something.
On Mon, Sep 30, 2013 at 3:06 PM, David Winsemius dwinsem...@comcast.netwrote:
On Sep 30, 2013, at
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On Behalf Of srecko joksimovic
Sent: Monday, September 30, 2013 3:22 PM
To: David Winsemius
Cc: R help
Subject: Re: [R] multilevel analysis
I thought so, but then I found
On Sep 30, 2013, at 3:22 PM, srecko joksimovic wrote:
I thought so, but then I found this:
Normality
The assumption of normality states that the error terms at every level of the
model are normally distributed
maybe I misinterpreted something.
Notice that it is the _error_terms_ that are
Thanks for your comments, David and Bert.
The best would be to provide an example. Let's say we have a dataset like
this one:
IDEmployee Company OU CountViewPortal CountLogin TimeOnTask Performance
1 Company1 Company1.OU1 21 33 627.8 4.3
2 Company1 Company1.OU2 45 54 34.8 2.3
3 Company2
Could you share the results of sessionInfo() and str(alllev)?
Also please share the exact in- and output with relevant error
messages; for example 'cntnew:male' does not make much sense without
context.
Unfortunately I don't understand your model specification and is lost
in the interpretation
Rune,
Thanks a lot for pointing me to your ordinal package. It is wonderful,
and I tried a random intercept model and it worked well except that
probably there is something wrong with my data (size is big), I got
some warning messages indicating that In sqrt(diag(vc)[1:npar]) :
NaNs produced.
On 6 June 2013 00:13, Xu Jun junx...@gmail.com wrote:
Dear r-helpers,
I have two questions on multilevel binary and ordered regression models,
respectively:
1. Is there any r function (like lmer or glmer) to run multilevel ordered
regression models?
Yes, package ordinal will fit such
Dear r-helpers,
I have two questions on multilevel binary and ordered regression models,
respectively:
1. Is there any r function (like lmer or glmer) to run multilevel ordered
regression models?
2. I used the glmer function to run a two-level binary logit model. I want
to make sure
that I did
Hi,
i am trying to learn something about multilevel analysis using a great
Discovering statistics using R. I constructed some sample data and then
tried to fit a model. Generally model fits well, however when trying to fit
the same model using z-score (standarizded) variables i got an error:
Hi Ben, thanks for your reply.
Your suggestion does not work indeed:
lme(y ~ x, random=list(~1|a:b, ~1|b:c), data=mydata)
Error in getGroups.data.frame(dataMix, groups) :
Invalid formula for groups
Here is a reproducible example of my data:
set.seed(123)
library(lme4)
library(nlme)
Dear list,
I am trying to fit some mixed models using packages lme4 and nlme.
I did the model selection using lmer but I suspect that I may have some
autocorrelation going on in my data so I would like to have a look using the
handy correlation structures available in nlme.
The problem is
jonas garcia garcia.jonas80 at googlemail.com writes:
I am trying to fit some mixed models using packages lme4 and nlme.
I did the model selection using lmer but I suspect that I may have some
autocorrelation going on in my data so I would like to have a look using the
handy correlation
Three comments:
1. If there is no right censoring (and it appears not), I would use
lmer on the awakening times, glmer on the FullyOriented variable. That
is, I agree with Burt.
Another option is GEE models
2. If you want to use a Cox model, then you can
a. Add + cluster(id) to the
Patients are either fully oriented or not (1 or 2) after an hour. If
they're
not, then the data is right censored.
It doesn't look like right censored data to me, unless the time variable
were time to full orientation; you labeled it time to awake which
appears to be something different.
There is indeed right censoring, but I obviously didn't explain it very well.
Patients are either fully oriented or not (1 or 2) after an hour. If they're
not, then the data is right censored.
However, I don't feel that coxme is overkill at all, as I may also have to
account for repeated
Hello all, thanks for your time and patience.
I'm looking for a method in R to analyse the following data:
Time to waking after anaesthetic for medical procedures repeated on the same
individual.
str(mysurv)
labelled [1:740, 1:2] 20 20 15 20 30+ 40+ 50 30 15 10 ...
- attr(*,
On Jul 1, 2011, at 10:10 AM, dunner wrote:
Hello all, thanks for your time and patience.
I'm looking for a method in R to analyse the following data:
Time to waking after anaesthetic for medical procedures repeated on
the same
individual.
str(mysurv)
labelled [1:740, 1:2] 20 20 15
On Jul 1, 2011, at 10:22 AM, David Winsemius wrote:
On Jul 1, 2011, at 10:10 AM, dunner wrote:
Hello all, thanks for your time and patience.
I'm looking for a method in R to analyse the following data:
Time to waking after anaesthetic for medical procedures repeated on
the same
Is there any right censoring?
If not, then plain old lme, lmer, gam (in mgcv), ... etc. would seem
to me do just fine for time to waking = ORIENTATION as a response --
or are you thinking of this as interval-censored data, which it would
appear to be since you've binned the response? I strongly
Dear all,
I posted this two years ago, getting no answers or suggestions - now I
am trying again, hoping something new is available in R.
I am interested in an application of linear multilevel model with
unequal selection probabilities at both levels.
Do you know if there is an R function
Thanks for your reply David.
I didn't realize I could change the title of my post! Haha.
I rather like the example because Table 1 actually appears in Cameron and
Trivedi (potential error and all!).
aperm is not the issue. I am not sure why you get different output, it
should be the case.
Other
I see..
Row1 of Table 2 gives averages for category 3 in the group with a zero in
cols 6-8 AND col 2.
I wanted to averages for category 3 in the group with a zero in cols 6-8 and
a 1 in col 2.
I still think its suspicious that cols V1 and V2 in Table 1 are the same.
--
View this message in
Correction, this is not an issue with multilevel, rather a quirk with
aggregate.
Sill looking for help, anyone?
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On May 21, 2011, at 11:56 AM, eeecon wrote:
Correction, this is not an issue with multilevel, rather a quirk with
aggregate.
So change you subject.
Sill looking for help, anyone?
About what?
--
David Winsemius, MD
West Hartford, CT
__
On May 20, 2011, at 3:54 PM, eeecon wrote:
Hi,
My code indicates there may be a bug in multilevel.
I doubt this is actually the case, can anyone tell me what is wrong
with my
code?
The data file for this code can be downloaded here:
http://cameron.econ.ucdavis.edu/mmabook/mma15p4gev.asc
Hi,
My code indicates there may be a bug in multilevel.
I doubt this is actually the case, can anyone tell me what is wrong with my
code?
The data file for this code can be downloaded here:
http://cameron.econ.ucdavis.edu/mmabook/mma15p4gev.asc
Here is the code that generates the bug:
rm(list
Why this?
I write:
radon.data - list (n, J, x, y, county)
radon.inits - function (){
list (a=rnorm(J), b=rnorm(1), mu.a=rnorm(1),
sigma.y=runif(1), sigma.a=runif(1))
}
radon.parameters - c (a, b, mu.a, sigma.y, sigma.a)
# with 10 iterations
radon.1 - bugs (radon.data, radon.inits,
Caterina,
Did you get an answer to this question? I'm trying to do something similar.
Jason
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Hi R-Help.
I am working on a data set with a 3-level nested structure. I have
individuals nested in households and multiple observations on each
individual. I assume that the individuals inside a given household are
correlated and that the individuals are correlated with themselves
over time. The
check out the coxme and the kinship packages, both have the capability to fit
the Cox proportional hazard model in a multi-level setting, or you could use
glmer in lme4 to fit discrete-time (logistic) models with random intercepts.
CS
-
Corey Sparks, PhD
Assistant Professor
Department of
* Please cc me if you reply as I am a digest subscriber *
Hi,
I am wondering how I can run a multilevel survival model in R? Below is
some of my data.
head(bi0.test)
childid famid lifedxm sex age delta
1 22.0222 CONTROL MALES 21.36893 0
2 13.0213 MAJOR MALES
On Thu, Jul 22, 2010 at 8:23 AM, Christopher David Desjardins
desja...@umn.edu wrote:
* Please cc me if you reply as I am a digest subscriber *
Hi,
I am wondering how I can run a multilevel survival model in R? Below is
some of my data.
head(bi0.test)
childid famid lifedxm sex
Thanks Stuart,
I already had some of those papers, will check the others!
best
Federico
On Wed, Jul 14, 2010 at 9:05 PM, Stuart Luppescu s...@ccsr.uchicago.eduwrote:
On Wed, 2010-07-14 at 04:31 -0700, Dr. Federico Andreis wrote:
does anybody know of a package (working under Linux) for
Dear All,
does anybody know of a package (working under Linux) for multilevel IRT
modelling?
I'd love to do this without having to go on WINSTEPS or the like..
thanks for the attention!
Federico Andreis
-
Dr. Federico Andreis
Università degli Studi di Milano-Bicocca, PhD Student
MEB
On Wed, 2010-07-14 at 04:31 -0700, Dr. Federico Andreis wrote:
does anybody know of a package (working under Linux) for multilevel
IRT modelling?
I'd love to do this without having to go on WINSTEPS or the like..
The first place to look would be the special issue of the Journal of
Statistical
Dear R users,
I have a question on multilevel modeling with R when there is endogenous
regressor(s) involved. As far as my economics background concerned, I
understand that Hausman-Taylor estimator (via Generalized Least Square)
deals with this situation and the package plm does the trick.
Hello everyone,
I am trying to regress applicants' performance in an assessment center
(AC) on their gender (individual level) and the size of the AC (group
level) with a multi-level model:
model.0 - lme(performance ~ ACsize + gender, random = ~1 | ACNumber,
method = ML, control =
Your request might find better answers on the R-SIG-mixed-models
list ...
Anyway, some quick thoughts :
Le vendredi 26 mars 2010 à 15:20 -0800, dadrivr a écrit :
By the way, my concern with lmer and glmer is that they don't produce
p-values,
The argumentation of D. Bates is convincing ... A
Thanks everyone for the helpful ideas. It appears that this will be more
difficult than I thought. I don't necessary have an inclination toward
p-values, but many journals certainly do. I would be willing to try to
calculate the confidence intervals around the estimates, but I haven't
gotten
I am using a multilevel modeling approach to model change in a person's
symptom score over time (i.e., longitudinal individual growth models). I
have been using the lme function in the multilevel package for the analyses,
but my problem is that my outcome (symptoms) and one of my predictors
By the way, my concern with lmer and glmer is that they don't produce
p-values, and the techniques used to approximate the p-values with those
functions (pvals.fnc, HPDinterval, mcmcsamp, etc.) only apply to Gaussian
distributions. Given that I would likely be working with quasi-poisson
have you tried using glmer?
If your dependent variable is poisson distributed, you can try something
like
fit-glmer(y~x+(1|group), family=poisson)
and if you have differential exposure, you can do
fit-glmer(y~offset(log(exposure))+x+(1|group), family=poisson)
Is this what you are asking?
With
Whoops, sorry that's pt(), not dt()
Thanks Dennis!
-
Corey Sparks, PhD
Assistant Professor
Department of Demography and Organization Studies
University of Texas at San Antonio
501 West Durango Blvd
Monterey Building 2.270C
San Antonio, TX 78207
210-458-3166
corey.sparks 'at' utsa.edu
Greetings,
Is there a package in R that will run multilevel models (e.g. students
nested in schools) where sampling weights can be employed at both levels?
Thanks in advance.
David
--
===
David Kaplan, Ph.D.
Professor
Department of
You can use intervals to get the Confidence intervals of fixed and
random effects.
Best
2009/3/17 WONG, Ka Yau ka...@ied.edu.hk:
Dear All,
I use R to conduct multilevel modeling. However, I have a problem
about the interpretation of random effect. Unlike the variables in fixed
-project.org
Subject: Re: [R] Multilevel modeling using R
You can use intervals to get the Confidence intervals of fixed and
random effects.
Best
2009/3/17 WONG, Ka Yau ka...@ied.edu.hk:
Dear All,
I use R to conduct multilevel modeling. However, I have a problem
about the interpretation
Regards,
Tommy
Research Assistant of HKIEd
From: ronggui [mailto:ronggui.hu...@gmail.com]
Sent: 17/3/2009 [Tue] 14:10
To: WONG, Ka Yau
Cc: r-help@r-project.org
Subject: Re: [R] Multilevel modeling using R
You can use intervals to get the Confidence intervals
Dear experts,
I use R to conduct multilevel modeling. However, I have a problem
about the interpretation of random effect. Unlike the variables in fixed
effects, the variables in random effects have not shown the standard error
(s.e.) and p-value, so I don't know whether
, March 17, 2009 12:05 PM
To: r-help@r-project.org
Subject: [R] Multilevel Modeling using R
Dear experts,
I use R to conduct multilevel modeling. However, I
have a problem about the interpretation of random effect.
Unlike the variables in fixed effects, the variables
In most biometric applications, those variances are treated as
nuisance parameters. They only need to be controlled for, while the
main purpose is to get the right point estimates and standard errors
for the fixed effects. In social science multilevel modeling (of which
education is probably the
Dear All,
I use R to conduct multilevel modeling. However, I have a problem
about the interpretation of random effect. Unlike the variables in fixed
effects, the variables in random effects have not shown the p-value, so I don't
know whether they are significant or not? I want to
Hi,
I'm trying to perform a power simulation for a simple multilevel
model, using the function glmmPQL in R version 2.8.1. I want to extract
the p-value for the fixed-effects portion of the regression, but I'm
having trouble doing that. I can extract the coefficients
On 3/11/2009 11:29 AM, Howard Alper wrote:
Hi,
I'm trying to perform a power simulation for a simple multilevel
model, using the function glmmPQL in R version 2.8.1. I want to extract
the p-value for the fixed-effects portion of the regression, but I'm
having trouble doing that. I can
Dear all,
I am interested in an application of linear multilevel model with unequal
selection probabilities at both levels.
Do you know if there is an R function for multilevel pseudo-maximum
likelihood estimation? Or is it possible to obtain these estimates using
the nlme package? In practice
Dear all,
Is it possible to estimate a SEM with panel data in R ?
Sincerly
 Justin BEM
BP 1917 Yaoundé
Tél (237) 99597295
(237) 22040246
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Hi R users
I want to use the lme package for a multilevel analysis on the following
example:
math-c(2, 3,2, 5, 6 ,7 , 7)
sex-c(1, 2, 1, 2, 2, 2, 1)
school_A-c(1,1,1,2,2,2,2)
school_B-c(10,10,10,20,20,20,20)
mydata-data.frame(math, sex, school_A, school_B)
mydata
School_A and school_B are
eugen pircalabelu eugen_pircalabelu at yahoo.com writes:
I want to use the lme package for a multilevel analysis on the
following example:
math-c(2, 3,2, 5, 6 ,7 , 7)
sex-c(1, 2, 1, 2, 2, 2, 1)
school_A-c(1,1,1,2,2,2,2)
school_B-c(10,10,10,20,20,20,20)
mydata-data.frame(math, sex,
76 matches
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