Re: [R-sig-eco] Building glmms to handle zero-inflated continuous data in R - what options are available? (especially relating to hurdle/mixture models)
How complex are the random effects? I they are relatively simple, give the mgcv package a look. Its gam() function can fit Tweedie models optimising over the Tweedie parameter too, and you can include random effects via splines using `bs = "re"`. G On 17 June 2015 at 15:57, Karan Odom wrote: > Hi, > > I have a zero-inflated continuous data set and want to build a glmm in R to > analyze it (I have both fixed and random effects). However, because my data > are continuous, I am discovering that this is not a simple task. > Zero-inflation options in glmmABMD are not appropriate because my data are > continuous and I don't know what other packages exist that allow for > zero-inflated glmms with continuous data. > > I tried implementing the Tweedie distribution using packages tweedie and > cplm, but these are a poor fit to my data. > > I think hurdle or mixture models might be especially useful for my data. > When I modeled the non-zero continuous data separately from the > zero/non-zero data, I get a very good fit to the data. However, I am stuck > at how to integrate the two models. There seem to be packages in R that do > this for count data but I have not found them for continuous data. > > I have been reading previous r-sig-ecology posts about this and find a lot > of information from 2008-2012. I was wondering in the last few years if > there have been developments in and if there are now available: (1) > packages or techniques for easily implementing glmms for zero-inflated data > in R, and (2) are there any good packages for mixture or hurdle models in R > that allow for continuous data (i.e., how can I integrate the two models > for the zero/non-zero versus non-zero continuous data)? > > Thank you very much for any help! > Karan > > -- > Karan J. Odom > Ph.D. Candidate, Biological Sciences > University of Maryland, Baltimore County > 1000 Hilltop Circle > Baltimore, MD 21250 > > [[alternative HTML version deleted]] > > ___ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > -- Gavin Simpson, PhD [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Building glmms to handle zero-inflated continuous data in R - what options are available? (especially relating to hurdle/mixture models)
You might try the gamlss package. I've had success using it to fit zero inflated inverse gaussian models to right-skewed continuous data with a large zero component. You can model covariates on the zero component, the mean, and the variance, separately. Ben From: R-sig-ecology [r-sig-ecology-boun...@r-project.org] on behalf of Karan Odom [kjo...@gmail.com] Sent: Wednesday, June 17, 2015 5:57 PM To: r-sig-ecology@r-project.org Subject: [R-sig-eco] Building glmms to handle zero-inflated continuous data in R - what options are available? (especially relating to hurdle/mixture models) Hi, I have a zero-inflated continuous data set and want to build a glmm in R to analyze it (I have both fixed and random effects). However, because my data are continuous, I am discovering that this is not a simple task. Zero-inflation options in glmmABMD are not appropriate because my data are continuous and I don't know what other packages exist that allow for zero-inflated glmms with continuous data. I tried implementing the Tweedie distribution using packages tweedie and cplm, but these are a poor fit to my data. I think hurdle or mixture models might be especially useful for my data. When I modeled the non-zero continuous data separately from the zero/non-zero data, I get a very good fit to the data. However, I am stuck at how to integrate the two models. There seem to be packages in R that do this for count data but I have not found them for continuous data. I have been reading previous r-sig-ecology posts about this and find a lot of information from 2008-2012. I was wondering in the last few years if there have been developments in and if there are now available: (1) packages or techniques for easily implementing glmms for zero-inflated data in R, and (2) are there any good packages for mixture or hurdle models in R that allow for continuous data (i.e., how can I integrate the two models for the zero/non-zero versus non-zero continuous data)? Thank you very much for any help! Karan -- Karan J. Odom Ph.D. Candidate, Biological Sciences University of Maryland, Baltimore County 1000 Hilltop Circle Baltimore, MD 21250 [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Building glmms to handle zero-inflated continuous data in R - what options are available? (especially relating to hurdle/mixture models)
Hi, I have a zero-inflated continuous data set and want to build a glmm in R to analyze it (I have both fixed and random effects). However, because my data are continuous, I am discovering that this is not a simple task. Zero-inflation options in glmmABMD are not appropriate because my data are continuous and I don't know what other packages exist that allow for zero-inflated glmms with continuous data. I tried implementing the Tweedie distribution using packages tweedie and cplm, but these are a poor fit to my data. I think hurdle or mixture models might be especially useful for my data. When I modeled the non-zero continuous data separately from the zero/non-zero data, I get a very good fit to the data. However, I am stuck at how to integrate the two models. There seem to be packages in R that do this for count data but I have not found them for continuous data. I have been reading previous r-sig-ecology posts about this and find a lot of information from 2008-2012. I was wondering in the last few years if there have been developments in and if there are now available: (1) packages or techniques for easily implementing glmms for zero-inflated data in R, and (2) are there any good packages for mixture or hurdle models in R that allow for continuous data (i.e., how can I integrate the two models for the zero/non-zero versus non-zero continuous data)? Thank you very much for any help! Karan -- Karan J. Odom Ph.D. Candidate, Biological Sciences University of Maryland, Baltimore County 1000 Hilltop Circle Baltimore, MD 21250 [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
