gmane.comp.lang.r.lme4.devel

Is glmmADMB the only package in R which can handle a zero-inflated Poisson model for repeated measures?

Mueller,John Martin — 2012-05-26T17:59:28

I am looking into which package to use in R to handle a zero-inflated Poisson model for repeated measures (growth curve analysis through multiple level modeling).


Is this correct?  Or is there a package in R other than the glmmADMB package that I should be considering?

And if the glmmADMB package is the only package for this purpose, can the glmmADMB handle a 3-level multi-level model (hierarchical linear model)?

The model that I am working on has TIME at level 1, FIRMS at level 2, and LOCATIONS (metropolitan statistical areas) at level 3.  The dependent variable at level 1 is a count variable (number of credit sources used by a firm) which has an over dispersion of zeros at various points in time.  Thus, the reason why I am having to use a zero-inflated Poisson model (or I could use a hurdle model).

Thanks for your help.

- JM

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error while trying to get predictions from a lme object

Antonio P. Ramos — 2012-05-25T23:09:52

Hi all,

I am trying to get predictions for individual level observations for an lme
object. Yet, since I am get different types of errors for different trials,
it seems to me I am missing something.  My model is the following:

model <- lme(log(child_mortality) ~ as.factor(cluster)*time +
             my.new.time.one.transition.low.and.middle + ttd +
             maternal_educ+ log(IHME_id_gdppc) + hiv_prev-1,
             merged0,na.action=na.omit,method="ML",weights=varPower(form =
~ time),
             random= ~ time| country.x,
             correlation=corAR1(form = ~ time),
             control=lmeControl(msMaxIter = 200, msVerbose = TRUE))


It runs fine and the results make sense. Now my attempts to get
predictions. For example:

 data.frame(time=c(10,10,10,10),country.x=c("Poland","Brazil","Argentina","France"),
+
my.new.time.one.transition.low.and.middle=c(1,1,1,0),ttd=c(0,0,0,0),
+
maternal_educ=c(10,10,10,10),IHME_id_gdppc=c(log(5000),log(8000),log(8000),log(15000)),
+                          hi

Error in lme4 0.999902344-0: "Object 'multResp' notfound" ?

Bertolt Meyer — 2012-05-24T15:30:07

Dear list,

The simplest random intercept model that I try to fit with the newest version of lme4 (0.999902344-0) throws an "Object 'multResp' not found" error on my Mac (Mac OSX 10.7.3, German R 2.15.0). Here is a reproducible example:

install.packages(c("minqa", "Rcpp"))
install.packages("lme4", repos="http://lme4.r-forge.r-project.org/repos")
library(lme4)

my_data <- read.csv("http://dl.dropbox.com/u/5384027/my_data.csv")
my_data$group <- as.factor(my_data$group)

# the data set consists of 97 four-person teams
# with two variables x and y observed on the individual
# level in each team

head(my_data)
#   X group        y          x
# 1 1     1 2.914286  -8.170984
# 2 2     1 2.746269 -13.504318
# 3 3     1 3.171429  -1.504318
# 4 4     1 2.978723  -4.837651
# 5 5     2 2.928571  -8.170984
# 6 6     2 2.987013   8.495682

mlmodel1_ri <- lmer(y ~ x + (1 | group), data = my_data)

# Fehler in lmer(y ~ x + (1 | group), data = my_data) : 
#   Objekt 'multResp' nicht gefunden

Does anyone know how to fix thi

Group level predictors in mixed models

Edwin Lebrija Trejos — 2012-05-23T03:35:21




Dear R-sig-mixed-models subscribers, I am analizing survival (ALIVE) of one year-old seedlings of over a hundred species as a function of the total number of seedlings (TOTABN) and the proportion of conspecifics seedlings (CONSPp) in their neighborhood (sampling quadrat). The data comes from monitoring seedling dynamics trough time (YEAR) in a series of forest plots (PLOT). A crucial part of the analysis is to understand the variation in responses between species (SPECIES). For now, I have one continuous species-level variable, abundance across the landscape (LANDSPABN), that I would like to use to explain the variation between species. After reading a number of printed and online references on advanced statistical models it seems to me that my problem could be properly analyzed using hierarchical bayesian modelling but I am just beggining to understand lmer (and R) and I was wondering if using such tools I can also perform a meaningfull analysis.My first approach in lmer was to specify a model with vary

How to obtain t-values from mer objects

Gang Chen — 2012-05-22T17:56:16

I know how to obtain the fixed effects from a mer object:



or,


But how can I obtain the t-statistic values for the fixed effects?

Thanks,
Gang

Syntax for lme function to model random factors andinteractions

Daisuke Koya — 2012-05-22T13:16:25

Hello all,

The following (i.e. below the hash marks) is a reposting of something originally posted on the R forum. I was told by a helpful person there to redirect my question to this forum.

My question concerns what the correct expression is to specify two random factors for the lme function. So far I've tried:

random = ~ 1|C + D

random = ~ 1 + (1|C) + (1|D)

random = ~ 1 + 1|C + 1|D

but all of these resulted in errors (mostly of the type "
Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups"). To be more specific, "C" refers to a subject factor, while "D" refers to a repetition factor (i.e. multiple evaluations of the same stimulus by the same subject). In this case, would the correct way to code this be:

random = ~ 1|C/D

? Additionally, I would be grateful if someone could tell me whether I have to code the interactions in the "random" argument in the lme function, and if so how.

Thank you very much in advance for the help.

Best regards,

Daisuke

#####################

reaped measures with unequal intervals

Ahmad Rabiee — 2012-05-22T04:51:12

Hi

I have a repeated measures dataset with unequal intervals (spacing) - the intervals are Day 6, Day 13 and Day 15.
Anyone knows how to handle this type of data?

Thanks
Ahmad


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ZERO variance and ZERO sd of random effect in lmer - justified to run a glm instead?

Julia Sommerfeld — 2012-05-22T01:27:12

Sorry, plots are two large. Please read this message.


Dear list,

I'm running a lmer (package lme4) with a binomial error distribution and
"bird" as the random effect (160 observations of 25 birds). The response
variable "ars1" is coded as 0, 1.
The fixed effect "sit" is numerical and "dive" is categorical (0, 1).

What puzzles me a little is that the a variance and sd of the random effect
is ZERO. Same question has been posted before and Douglas Bates answer was:

"No, an estimate of zero is not suspicious.  It is simply an indication

that the variability between individuals is not significantly larger
than what one would expect from the random variability in the
response."


While another answer suggested that the model was "wrong":


"A zero estimate of a variance possibly indicates the model is wrong."  This
wrong model seemed to be related to a negative covariation of one of the
fixed effects ?



 My simplified model is:

mod6 <- lmer(ars1 ~ sit + dive + (1|bird), data=dat, family=binomial)

General

ZERO variance and ZERO sd of random effect in lmer - justified to run a glm instead?

Julia Sommerfeld — 2012-05-22T01:07:22

Dear list,

I'm running a lmer (package lme4) with a binomial error distribution and
"bird" as the random effect (160 observations of 25 birds). The response
variable "ars1" is coded as 0, 1.
The fixed effect "sit" is numerical and "dive" is categorical (0, 1).

What puzzles me a little is that the a variance and sd of the random effect
is ZERO. Same question has been posted before and Douglas Bates answer was:

"No, an estimate of zero is not suspicious.  It is simply an indication

that the variability between individuals is not significantly larger
than what one would expect from the random variability in the
response."


While another answer suggested that the model was "wrong":


"A zero estimate of a variance possibly indicates the model is wrong."  This
wrong model seemed to be related to a negative covariation of one of the
fixed effects ?



 My simplified model is:

mod6 <- lmer(ars1 ~ sit + dive + (1|bird), data=dat, family=binomial)

Generalized linear mixed model fit by the Laplace approximation

2nd attempt - conflict of dfs or f value in lme

Charles Determan Jr — 2012-05-21T12:45:29

Greetings R users,


I am trying to match some SAS output of a mixed model.  After running PROC
MIXED with the covariance structure as AR1 the output below is given.  Now
when I try to replicate this with lme, I get the correct degrees of freedom
and somewhat close values.  If I try with gls, I get the exact F value but
the incorrect denominator degrees of freedom.  Is there some syntax or
parameter I can adjust to get lme to have the same F values as gls?  That
way the correct degrees of freedom would be applied and I would get the
replicated model.  The data is also included below.  Thanks to any who can
help with this issue.


Regards,

Charles


Row   ID Group Died Event_name      var
1    12510     3  YES          B     -1.05257
2    12510     3  YES        S45    -1.00000
3    12510     3  YES        FR2   -1.14630
4    12510     3  YES        FR8   -1.08831
5    12510     3  YES       FR20   -1.03339
6    21510     3   NO          B      -0.87290
7    21510     3   NO        S45     -1.22185
8    2151

Help with simple LME model specification in MCMCglmm

Chris Gast — 2012-05-20T21:12:27

Hello,

I would like to specify the following model (in lmer() syntax) in MCMCglmm,
but seem to be unable to do so. I receive the warning message "some fixed
effects are not estimable and have been removed", although I do not believe
this should be the case.

Here's the lmer() model:

fit = lmer(y ~ -1 + cyear:stratum + site + (-1+cyear|site),data=indat)

And here's an attempt at fitting this in MCMCglmm():

fitmcmc = MCMCglmm(y ~ -1 + cyear:stratum +
site,random=~idh(-1+cyear):site,data=indat)

The data structure is relatively simple: stratum and site are categorical
(technically site is nested within stratum, so I'd give bonus points if
someone can help me specify separate variance components for the
site-specific slopes within each stratum), and "cyear" is the continuous
predictor for which I would like stratum-specific slopes with random slopes
for each site (again, within stratum, if possible).


Thanks,

Chris Gast





-----------------------------
Chris Gast
cmgast-Re5JQEeQqe8AvxtiuMwx3w< at >public.gmane

error from clmm

Malcolm Fairbrother — 2012-05-20T15:49:25

Dear list,

I am making use of the "clmm" function from the "ordinal" package, for fitting mixed models with ordinal outcomes.

When fitting quite a few models, I get the error:

Error in if (maxGrad < rho$ctrl$gradTol) { : 
  missing value where TRUE/FALSE needed
In addition: Warning message:
In sqrt(phi2) : NaNs produced

Can anyone help me to understand why this may be happening, and/or whether there's anything obvious I should be doing different? With some seemingly small/arbitrary specification changes--or when taking (some) random subsets of my dataset--I don't get this message. But I'd like to find a way of reducing the frequency with which I do get it.

I don't think there's anything unusual about my call:

mod1 <- clmm(as.ordered(y) ~ x + (1 | fac1) + (1 | fac2), data=dat, link="logit", threshold="symmetric")

I'm running:
R version 2.15.0 (2012-03-30)
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)

Many thanks to anyone who can assist,

Malcolm

LME4: output interpretation of tricky model

Eiko Fried — 2012-05-20T11:53:00

Dear Mailinglist,

I would be very glad to get some assistance with interpreting a tricky
model output in LME4.

The model is this:

Y ~ -1 + Y_index + time + Y_index*x1 + Y_index*x2 + ( - 1 + Y_index |
subject)

What I am doing with this is modeling 9 items of a questionnaire as a
multivariate response variable Y.
Y_index is a categorical variable defining the number of item of the
questionnaire (1 through 9), and I am checking with interaction effects on
x1 and x2 whether these covariates have differential effects on my
multivariate response. It is a longitudinal design with 5 measurement
points, and I expect that x1 only affects some of my 9 items (the same for
x2).

The dataset is in long-long format (Y_index * time), so each subject has
9*5 lines.
(I found the suggestion for that kind of analysis in Hox, 2010).

The for me relevant part of the output looks like this:

Fixed effects:
                              Estimate    Std. Error    t value
Y_index1                 0.3161592  0.0780922   4.049
Y_in

Use LRT to assess the effect of lower order terms in the presence of a significant higher order term (e.g., interaction)

Xiao He — 2012-05-19T14:27:40

Dear R-listers,

I have a couple of questions regrading how to assess the effects of lower
order terms when a higher order term is significant using likelihood ratio
tests. I am interested to examine how two independent variables (iv1 and
iv2) affect subjects' log-transformed reaction time (logRT). A subset of
the data can be accessed in the link below if needed:

http://www-scf.usc.edu/~hex/testset.txt

In a series of responses that Dr. Bates made in this thread:
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/005399.html, he
recommended that non-significant fixed effects be removed from a model
before further llkelihood ratio tests. For example, I can first remove
*iv2* and
compare the model with both iv1 and iv2 with the model without iv2.

model.main = lmer(logRT ~ iv1 + iv2 + (1 + iv2 | subject) + (1 | item),
Sample, REML = FALSE)
model.iv1 = lmer(logRT ~ iv1 + (1 + iv2 | subject) + (1 | item), Sample, REML
= FALSE)
anova(model.main, model.iv1)

#Data: Sample
#Models:
#model.iv1: logRT ~ iv1 +

help with MANOVE with mixed effects

David Costantini — 2012-05-19T09:16:15

Dear All
I wonder if anyone can help with this issue.?    I have two fixed factors (experimental group and sex) and a random factor (brood). I need the random factor to control for pseudoreplication because each brood provides a?    certain number of siblings, which are pseudoreplicates because share genes/environment. Then I have 8 variables. so I would like to to compare the covariance structures among groups while controlling for the random factor. I was told that R might do that, I mean a manova with mixed effects (manova(lme(....))?    

thanks for your help?    
David

______________________________________________ 

David Costantini, PhD

http://www.davidcostantini.it
NERC Postdoctoral research associate

Institute of Biodiversity, Animal Health and Comparative Medicine
School of Life Sciences
College of Medical, Veterinary and Life Sciences
University of Glasgow
Graham Kerr Building, room 511
Glasgow G12 8QQ, UK

See also my association Ornis italica
http://www.ornisitalica.com
http://www

Very odd parameter estimates using GEE with AR-1correlation structure

Anne Bjorkman — 2012-05-18T18:19:28

Hello mixed modelers,

I am having problems with some GEE models I am trying to run using geepack.

I have species abundance data for 52 different species in 154 sites over 47
years, and I am trying to extract slope parameter estimates so that I can
look at whether these species have increased or decreased in abundance over
time, while taking into account the repeated measurements at each site over
time.  I originally started doing this with mixed models, but have been
advised that GEE would be more appropriate for my data as it gives
population-averaged responses.

However, when I try to run GEE's on my data I get really bizarre parameter
estimates for some of my species.  As my dataset is huge I unfortunately
cannot provide the whole thing, but I have uploaded a subset of the data
for one species with a particularly bizarre slope parameter estimate here:
http://dl.dropbox.com/u/4481861/Example_for_GEE_one_species.csv

The data look like this:

  Site Year Species Value_Pres Value_Abs
1    1 1961       1

fitting model for repeated measures cross-over design?

Kirstin-Friederike Heise — 2012-05-18T13:13:04

Dear list members,
 
I would be very grateful for any advice on fitting a model for a cross-over design please excuse my lengthy post. 

I am currently trying to analyze a transient treatment effect (treatment vs. placebo) in a cross-over experiment (2 sessions) in two groups (old, young subjects) with repeated measurements for both treatment conditions (before treatment=baseline, post, post45, post90) in multiple outcome variables separately (2 physiological and 4 behavioral,  all of which metric). 

Points of interest are whether there is an effect of treatment 
in both/either of the groups
in one/more outcome variables
and if an inter-correlation between potential changes in physiological and behavioral outcome exists.

Problems I am trying to deal with:
Although a carry-over of direct treatment effect seems unlikely (sufficient wash-out phase in-between two conditions/sessions), a possible change (improvement) in response rate from 1st to 2nd session irrespective of treatment, i.e. rather "period effect"

conflict of degrees of freedom or f value in lme

Charles Determan Jr — 2012-05-18T13:21:53

Greetings R users,


I am trying to match some SAS output of a mixed model.  After running PROC
MIXED with the covariance structure as AR1 the output below is given.  Now
when I try to replicate this with lme, I get the correct degrees of freedom
and somewhat close values.  If I try with gls, I get the exact F value but
the incorrect denominator degrees of freedom.  Is there some syntax or
parameter I can adjust to get lme to have the same F values as gls?  That
way the correct degrees of freedom would be applied and I would get the
replicated model.  The data is also included below.  Thanks to any who can
help with this issue.


Regards,

Charles


Row   ID Group Died Event_name      var
1    12510     3  YES          B     -1.05257
2    12510     3  YES        S45    -1.00000
3    12510     3  YES        FR2   -1.14630
4    12510     3  YES        FR8   -1.08831
5    12510     3  YES       FR20   -1.03339
6    21510     3   NO          B      -0.87290
7    21510     3   NO        S45     -1.22185
8    2151

non-linear mixed effects model with binomial error

Mike Lawrence — 2012-05-18T13:02:48

Hi folks,

This (http://stats.stackexchange.com/questions/24293) recent-ish post
to stats.stackexchange.com suggests that when it comes to tools
available in R, one cannot fit a non-linear mixed effects model with
binomial error. I just wanted to double check that this is an accurate
portrayal of the state of the aRt.

(In case it matters, I'm looking to follow up the answer to my
question here [http://stats.stackexchange.com/questions/22895])

Cheers,

Mike

--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

Looking to arrange a meeting? Check my public calendar: http://goo.gl/BYH99

~ Certainty is folly... I think. ~

lmekin complains about dimnames

Joshua Wiley — 2012-05-18T01:00:11

Hi All,

I am trying to estimate disease heritability ultimately, but to get
started, I wanted to model a continuous outcome using lmekin().  I
tried something very similar (I think) to example 3 (GAW) of the
lmekin vignette from the coxme package
http://cran.r-project.org/web/packages/coxme/vignettes/lmekin.pdf

However, I get an error about the variance matrix not having dimnames.
 Although there does not seem to be data provided with the vignette, I
believe I should be essentially perfectly replicating the steps.  Any
ideas on what to do?  Below is made up data and an example along with
my session info.

Thanks!

Josh

################################################
require(kinship2)
adat <- data.frame(PersonID = 1:12,
  FatherID = c(NA, NA, NA, 1, 1, 1, 1, 4, 4, 4, 4, 4),
  MotherID = c(NA, NA, NA, 2, 2, 2, 2, 3, 3, 3, 3, 3),
  sex      = c(0,  1,  1,  0, 0, 1, 1, 0, 0, 0, 1, 1))
adat <- do.call("rbind", rep(list(adat), 100))
adat$FamilyID <- rep(1:100, each = 12)
set.seed(10)
adat$Outcome <- rnorm(nrow

Likelihood drops on adding random effect

Murray Jorgensen — 2012-05-16T22:35:02

*Hi all,

it is counterintuitive to me that adding a random effect to a GLMM should
lead to a drop in the likelihood of the fitted model for, after all, one
can give the new random effect vanishingly small variance which should lead
to the same likelihood as before. But this is what I have encountered. I
regret that the output below is not self contained but if needed I could
follow-up offline with additional files.

The factor "box" is nested inside the factor "tree" and the factor "gap" is
crossed with both of these. This is a binomial GLMM with 0/1 response.

(1|tree), family = binomial)
Generalized linear mixed model fit by the Laplace approximation
Formula: fincr ~ icfac + (1 | gap) + (1 | box) + (1 | gap:box) + (1 |
tree)
 AIC   BIC logLik deviance
 687 732.4 -335.5      671
Random effects:
 Groups  Name        Variance Std.Dev.
 gap:box (Intercept) 0.000000 0.00000
 box     (Intercept) 0.082784 0.28772
 gap     (Intercept) 0.122614 0.35016
 tree    (Intercept) 0.505608 0.71106
Number of obs: 2160, gro