Dear List members: I performed two independent experiments (CRD) to test if a whitefly has preference to infect with a virus: potatos, tomatos or peppers (target hosts, TH), wether if the virus was obtained from potato or tomato (source hosts, SH). So I released 100 white flyes (previously infected with the virus from one or other SH) inside cages containing 10 plants of each TH (30 total). This is how the data looks like: exp SH TH cage tot posit 1 tom tom 1 10 4 1 tom bat 1 10 3 1 tom pep 1 10 0 1 bat tom 2 10 1 1 bat bat 2 10 2 1 bat pep 2 10 0 2 tom tom 3 10 6 2 tom bat 3 10 4 2 tom pep 3 10 0 2 bat tom 4 10 4 2 bat bat 4 10 0 2 bat pep 4 10 0 The issue I found here is that pepper was not infected at all, however it was infected in another experiment without chance of TH choice: i.e. I released infectious whiteflies inside cages containing the same pepper genotyope and they present the typical virus disease symptoms. So, how should I consider modeling this data? Zero-inflated negative binomial using the total plants as offset? Hurdle-model? Should I remove the pepper level for the model? Any help would be really helpful. Juan Edwards
glm model with all zeros for one of the factor level
4 messages · Paul Johnson, Ben Bolker, Juan Pablo Edwards Molina
(This question is about GLMs rather than mixed models in R.) I recommend reading up on separation in logistic regression, where the proportion in any of the categories formed by the fixed effects is exactly 1 or 0, so that a maximum likelihood estimate of the log odds doesn't exists. The logistf package is the simplest way of dealing with this in R. Good luck, Paul Sent from BlueMail<http://www.bluemail.me/r?b=11327>
On 5 Dec 2017, at 16:00, Juan Pablo Edwards Molina <edwardsmolina at gmail.com<mailto:edwardsmolina at gmail.com>> wrote:
Dear List members: I performed two independent experiments (CRD) to test if a whitefly has preference to infect with a virus: potatos, tomatos or peppers (target hosts, TH), wether if the virus was obtained from potato or tomato (source hosts, SH). So I released 100 white flyes (previously infected with the virus from one or other SH) inside cages containing 10 plants of each TH (30 total). This is how the data looks like: exp SH TH cage tot posit 1 tom tom 1 10 4 1 tom bat 1 10 3 1 tom pep 1 10 0 1 bat tom 2 10 1 1 bat bat 2 10 2 1 bat pep 2 10 0 2 tom tom 3 10 6 2 tom bat 3 10 4 2 tom pep 3 10 0 2 bat tom 4 10 4 2 bat bat 4 10 0 2 bat pep 4 10 0 The issue I found here is that pepper was not infected at all, however it was infected in another experiment without chance of TH choice: i.e. I released infectious whiteflies inside cages containing the same pepper genotyope and they present the typical virus disease symptoms. So, how should I consider modeling this data? Zero-inflated negative binomial using the total plants as offset? Hurdle-model? Should I remove the pepper level for the model? Any help would be really helpful. Juan Edwards ________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
Agreed. I added a section to the glmm FAQ giving guidance on how to to this in the GLMM case: http://bbolker.github.io/mixedmodels-misc/ecostats_chap.html#digression-complete-separation
On 17-12-06 09:36 AM, Paul Johnson wrote:
(This question is about GLMs rather than mixed models in R.) I recommend reading up on separation in logistic regression, where the proportion in any of the categories formed by the fixed effects is exactly 1 or 0, so that a maximum likelihood estimate of the log odds doesn't exists. The logistf package is the simplest way of dealing with this in R. Good luck, Paul Sent from BlueMail<http://www.bluemail.me/r?b=11327> On 5 Dec 2017, at 16:00, Juan Pablo Edwards Molina <edwardsmolina at gmail.com<mailto:edwardsmolina at gmail.com>> wrote: Dear List members: I performed two independent experiments (CRD) to test if a whitefly has preference to infect with a virus: potatos, tomatos or peppers (target hosts, TH), wether if the virus was obtained from potato or tomato (source hosts, SH). So I released 100 white flyes (previously infected with the virus from one or other SH) inside cages containing 10 plants of each TH (30 total). This is how the data looks like: exp SH TH cage tot posit 1 tom tom 1 10 4 1 tom bat 1 10 3 1 tom pep 1 10 0 1 bat tom 2 10 1 1 bat bat 2 10 2 1 bat pep 2 10 0 2 tom tom 3 10 6 2 tom bat 3 10 4 2 tom pep 3 10 0 2 bat tom 4 10 4 2 bat bat 4 10 0 2 bat pep 4 10 0 The issue I found here is that pepper was not infected at all, however it was infected in another experiment without chance of TH choice: i.e. I released infectious whiteflies inside cages containing the same pepper genotyope and they present the typical virus disease symptoms. So, how should I consider modeling this data? Zero-inflated negative binomial using the total plants as offset? Hurdle-model? Should I remove the pepper level for the model? Any help would be really helpful. Juan Edwards
________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models [[alternative HTML version deleted]] _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
Thanks Ben and Paul... By the way, that was what actually happened after fitting the glm: ...huge Wald confidence intervals...(from glm FAQ). I will take a look on that section of glm FAQ. best, Juan Edwards Juan
On Wed, Dec 6, 2017 at 11:53 AM, Ben Bolker <bbolker at gmail.com> wrote:
Agreed. I added a section to the glmm FAQ giving guidance on how to to this in the GLMM case: http://bbolker.github.io/mixedmodels-misc/ecostats_chap.html#digression-complete-separation On 17-12-06 09:36 AM, Paul Johnson wrote:
(This question is about GLMs rather than mixed models in R.) I recommend reading up on separation in logistic regression, where the proportion in any of the categories formed by the fixed effects is exactly 1 or 0, so that a maximum likelihood estimate of the log odds doesn't exists. The logistf package is the simplest way of dealing with this in R. Good luck, Paul Sent from BlueMail<http://www.bluemail.me/r?b=11327> On 5 Dec 2017, at 16:00, Juan Pablo Edwards Molina <edwardsmolina at gmail.com<mailto:edwardsmolina at gmail.com>> wrote: Dear List members: I performed two independent experiments (CRD) to test if a whitefly has preference to infect with a virus: potatos, tomatos or peppers (target hosts, TH), wether if the virus was obtained from potato or tomato (source hosts, SH). So I released 100 white flyes (previously infected with the virus from one or other SH) inside cages containing 10 plants of each TH (30 total). This is how the data looks like: exp SH TH cage tot posit 1 tom tom 1 10 4 1 tom bat 1 10 3 1 tom pep 1 10 0 1 bat tom 2 10 1 1 bat bat 2 10 2 1 bat pep 2 10 0 2 tom tom 3 10 6 2 tom bat 3 10 4 2 tom pep 3 10 0 2 bat tom 4 10 4 2 bat bat 4 10 0 2 bat pep 4 10 0 The issue I found here is that pepper was not infected at all, however it was infected in another experiment without chance of TH choice: i.e. I released infectious whiteflies inside cages containing the same pepper genotyope and they present the typical virus disease symptoms. So, how should I consider modeling this data? Zero-inflated negative binomial using the total plants as offset? Hurdle-model? Should I remove the pepper level for the model? Any help would be really helpful. Juan Edwards
________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models [[alternative HTML version deleted]] _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models