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Problems in using lmer to fit a multilevel model
4 messages · chenlei, Douglas Bates, David Duffy +1 more
2008/8/20 chenlei <chenlei at ibcas.ac.cn>:
Dear?? when I fitted my logistic model(binary data),I received an error "Error in mer_finalize(ans, verbose) : q = > n = ". what's the matter?how can I do with this problem? was this tell me the observations were not enough to fit the model ? I appreciate if anyone who use lmer could give me some advice.
Hmm. The message was supposed to be a bit more informative in that it should have given the values of q and n. I will repair that. The value of q is the total number of random effects and the value of n is the number of observations. I included that check because it did not make sense to me to try to fit more random effects than you have observations. I guess I could be persuaded that it would make sense in some circumstances because the random effects are determined by a penalized least squares optimization. What is the nature of the model that would require it to have more random effects than observations?
On Wed, 20 Aug 2008, Douglas Bates wrote:
2008/8/20 chenlei <chenlei at ibcas.ac.cn>:
when I fitted my logistic model(binary data), I received an error "Error in mer_finalize(ans, verbose) : q = > n = ".
Hmm. The message was supposed to be a bit more informative in that it should have given the values of q and n. I will repair that. The value of q is the total number of random effects and the value of n is the number of observations. I included that check because it did not make sense to me to try to fit more random effects than you have observations.
I guess I could be persuaded that it would make sense in some circumstances because the random effects are determined by a penalized least squares optimization. What is the nature of the model that would require it to have more random effects than observations?
Commonly, genetic models fit 2 or more random effects per individual, with different prespecified covariance matrices (A, D, A*A, A*D...) David Duffy.
| David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v
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The value of q is the total number of random effects and the value of n is the number of observations. I included that check because it did not make sense to me to try to fit more random effects than you have observations.
I guess I could be persuaded that it would make sense in some circumstances because the random effects are determined by a penalized least squares optimization. What is the nature of the model that would require it to have more random effects than observations?
Commonly, genetic models fit 2 or more random effects per individual, with different prespecified covariance matrices (A, D, A*A, A*D...)
Yes, and it can even happen that there is more individuals in the pedigree than there are phenotypic records! However, this is a bit special application. Perhaps, the warning might be more appropriate than the error. Gregor