[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?
HTH,
Thierry
------------------------------------------------------------------------
----
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
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more
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
data.
~ 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:
<http://www.geocities.com/joophox/mlbook/excelxls.zip>
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:
SUCCESS <- as.integer(RESPISRR*DENOM)
y <- cbind(SUCCESS, DENOM-SUCCESS)
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),
family=binomial(link=logit))
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) +
(MAILDUM | 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
advance.
Brant Inman
Duke University Medical Center
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