Re: [R] negative binomial lmer
On 7/28/06, Gregor Gorjanc [EMAIL PROTECTED] wrote: Ben Bolker bolker at ufl.edu writes: ... I haven't tried it, but you could also consider using a Poisson-lognormal (rather than neg binomial, which is Poisson-gamma) distribution, which might make this all work rather well in lmer: www.cefe.cnrs.fr/esp/TBElston_Parasitology2001.pdf Actually it is very simple lmer(y ~ effA + (1 | effB), family=quasipoisson) i.e. this fits the following model y_ijk ~ Poisson(\lambda_ijk) log(lambda_ijk) = \mu + effaA_i + effB_ij + e_ijk effB_i ~ Normal(0, \sigma^2_b) e_ijk ~ Normal(0, \sigma^2_e) Gregor I would advise checking the results from lmer against those from another way of fitting this model or the negative binomial model. There may be a problem in the way that lmer handles the scale parameter. I haven't checked generalized linear mixed models with a scale parameter as extensively as I have checked those without a separate scale parameter (the binomial and Poisson families). If anyone can provide me with an example of such a model and sample data (preferably off-list) I would appreciate it. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] negative binomial lmer
To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood, comparing the full model to reduced models (containing all but one effect, for all effects). However, for negative binomially distributed data, I need to estimate the parameter theta. I have been doing this by using a negative binomial glm of the same model (except that all the effects are fixed), and estimating mu as the fitted model like so: model_1 -glm.nb(y~x1+x2+x3, data = datafilename) mu_1 - fitted(model_1) theta_1 - theta.ml(y, mu_1, length(data), limit = 10, eps = .Machine$double.eps^0.25, trace = FALSE) Then, I conduct the lmer, using the estimated theta: model_11 -lmer(y~x1+x2+(1|x3), family = negative.binomial(theta = theta_1, link = log), method = Laplace) First, I wondered if this sounds like a reasonable method to accomplish my goals. Second, I wondered if the theta I use for reduced models (nested within model_11) should be estimated using a glm.nb with the same combination of variables. For example, should a glm.nb with x1 and x3 only be used to estimate theta for an lmer using x1 and x3? Third, I wish to test for random effects of one categorical variable with 122 categories (effects of individual). For this variable, the glm.nb (for estimating theta) does not work--it gives this error message: Error in get(ctr, mode = function, envir = parent.frame())(levels(x), : orthogonal polynomials cannot be represented accurately enough for 122 degrees of freedom Is there any way that will allow me to accurately estimate theta using this particular variable (or without it)? Or should I be using a Poisson distribution (lognormal?) instead, given these difficulties? If anyone has advice on how to properly conduct this test (or any references that might tell me in a clear way), I would be very grateful. Also, please let me know if I should provide additional information to make my question clearer. Please respond to me directly, as I am not subscribed to this list. Thank you very much, Tracy S. Feldman Postdoctoral Associate, the Noble Foundation, Ardmore, OK. __ [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] negative binomial lmer
I think you should use glmm.admb. library(glmmADMB) ?glmm.admb glmm.admb package:glmmADMB R Documentation Generalized Linear Mixed Models using AD Model Builder Description: Fits mixed-effects models to count data using Binomial, Poisson or negative binomial response distributions. Zero-inflated versions of Poisson and negative binomial distributions are available. 2006/7/28, Tracy Feldman [EMAIL PROTECTED]: To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood, comparing the full model to reduced models (containing all but one effect, for all effects). However, for negative binomially distributed data, I need to estimate the parameter theta. I have been doing this by using a negative binomial glm of the same model (except that all the effects are fixed), and estimating mu as the fitted model like so: model_1 -glm.nb(y~x1+x2+x3, data = datafilename) mu_1 - fitted(model_1) theta_1 - theta.ml(y, mu_1, length(data), limit = 10, eps = .Machine$double.eps^0.25, trace = FALSE) Then, I conduct the lmer, using the estimated theta: model_11 -lmer(y~x1+x2+(1|x3), family = negative.binomial(theta = theta_1, link = log), method = Laplace) First, I wondered if this sounds like a reasonable method to accomplish my goals. Second, I wondered if the theta I use for reduced models (nested within model_11) should be estimated using a glm.nb with the same combination of variables. For example, should a glm.nb with x1 and x3 only be used to estimate theta for an lmer using x1 and x3? Third, I wish to test for random effects of one categorical variable with 122 categories (effects of individual). For this variable, the glm.nb (for estimating theta) does not work--it gives this error message: Error in get(ctr, mode = function, envir = parent.frame())(levels(x), : orthogonal polynomials cannot be represented accurately enough for 122 degrees of freedom Is there any way that will allow me to accurately estimate theta using this particular variable (or without it)? Or should I be using a Poisson distribution (lognormal?) instead, given these difficulties? If anyone has advice on how to properly conduct this test (or any references that might tell me in a clear way), I would be very grateful. Also, please let me know if I should provide additional information to make my question clearer. Please respond to me directly, as I am not subscribed to this list. Thank you very much, Tracy S. Feldman Postdoctoral Associate, the Noble Foundation, Ardmore, OK. __ [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- 黄荣贵 Department of Sociology Fudan University __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] negative binomial lmer
Tracy Feldman tracysfeldman at yahoo.com writes: To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood, comparing the full model to reduced models (containing all but one effect, for all effects). However, for negative binomially distributed data, I need to estimate the parameter theta. I have been doing this by using a negative binomial glm of the same model (except that all the effects are fixed), and estimating mu as the fitted model like so: \ I haven't tried it, but you could also consider using a Poisson-lognormal (rather than neg binomial, which is Poisson-gamma) distribution, which might make this all work rather well in lmer: www.cefe.cnrs.fr/esp/TBElston_Parasitology2001.pdf cheers Ben Bolker __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] negative binomial lmer
Ben Bolker bolker at ufl.edu writes: ... I haven't tried it, but you could also consider using a Poisson-lognormal (rather than neg binomial, which is Poisson-gamma) distribution, which might make this all work rather well in lmer: www.cefe.cnrs.fr/esp/TBElston_Parasitology2001.pdf Actually it is very simple lmer(y ~ effA + (1 | effB), family=quasipoisson) i.e. this fits the following model y_ijk ~ Poisson(\lambda_ijk) log(lambda_ijk) = \mu + effaA_i + effB_ij + e_ijk effB_i ~ Normal(0, \sigma^2_b) e_ijk ~ Normal(0, \sigma^2_e) Gregor __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
