[R-sig-ME] Multilevel logistic regression

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Fri Mar 20 10:32:16 CET 2009

Dear Brant,

The model is too complex. You have maximum three observations for each
level of the random effect. Allowing for a random intercept and two
random slopes does not make much sense then. Does it?



ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 

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than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

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
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Brant Inman
Verzonden: donderdag 19 maart 2009 3:11
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] Multilevel logistic regression

lmer Experts:

I am trying to use lmer to duplicate the results found in Joop Hox's  
book "Multilevel Analysis: technique and applications" 2002.  In  
chapter 6 of his book he shows an example of multilevel logistic  
regression for a meta-analysis of survey response rates.  The data are  
available in the file "metaresp.xls" at his website:


The dataset includes the following variables of interest:

Individual level (Level 1) variables:
TELDUM	 = telephone questioning
MAILDUM  = mail questioning
RESPONSE = the outcome of interest, the study response rate
DENOM    = the number of people questioned

Study/group level (Level 2) variables:
SOURCE 	 = the study identifier
YEAR	 = year of study
SALIENCY = how salient the questionnaire was (0 to 2)
RESPISRR = the way the response rate was calculated

The null model (Table 6.2) proposed by Joop is easy to fit:


f1 <- lmer(y ~ RESPISRR + (1 | SOURCE), family=binomial(link=logit))

Joop then adds a couple Level 1 variables (Table 6.3):

f2 <- lmer(y ~ RESPISRR + TELNUM + MAILDUM + (1 | SOURCE),  

He then says that these two Level 1 variables should be allowed to  
vary across studies (varying slopes).  When I try to fit what I  
believe to be the correct model, I get an error

f3 <- lmer(y ~ RESPISRR + TELNUM + MAILDUM + (TELNUM | SOURCE) +  
	+ (1 | SOURCE), family=binomial(link=logit))

Error in mer_finalize(ans) : q = 240 > n = 105

Can anyone tell me what I am doing wrong here?  Thanks so much in  

Brant Inman
Duke University Medical Center

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