Timeline for zero inflated binomial data [closed]
Current License: CC BY-SA 3.0
11 events
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| Aug 2, 2017 at 11:07 | history | closed |
kjetil b halvorsen♦ mdewey gung - Reinstate Monica Michael R. Chernick Peter Flom |
Needs details or clarity | |
| Aug 1, 2017 at 14:38 | review | Close votes | |||
| Aug 2, 2017 at 11:07 | |||||
| Mar 4, 2017 at 18:17 | history | tweeted | twitter.com/StackStats/status/838091016137748480 | ||
| Jun 3, 2013 at 14:42 | comment | added | Jonathan Bone | I am not running into any errors. The reason I thought it may be a problem was because I was getting fixed effect coefficients coming out the model as negative when means from the raw data suggested to me that they should be positive. | |
| Jun 3, 2013 at 13:51 | comment | added | Peter Flom | Yes, I think you are mistaken about what is problematic. Zero inflated logistic doesn't make sense to me, and logistic regression makes no assumptions about the proportion who say 0 or 1 | |
| Jun 3, 2013 at 13:26 | comment | added | Glen_b |
The difference between a proportion and a count is the total that you scaled by; surely that can't be a problem; just move between unscaled and scaled counts as needed. If your Bernoulli's are zero-inflated, then as suggested there may be no issue. However, there are zero-inflated binomials in R (VGAM::zibinomial).
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| Jun 3, 2013 at 13:21 | history | edited | Glen_b | CC BY-SA 3.0 |
deleted 22 characters in body
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| Jun 3, 2013 at 12:47 | comment | added | COOLSerdash | I've never heard of zero inflated logistic regression (maybe someone else has?). Of course, if you have only zeroes, then you just don't have enough information to fit the model. Does the regression give an error? Something that can happen is complete separation if the outcome variable separates a predictor variable or a combination of predictor variables completely. | |
| Jun 3, 2013 at 12:35 | comment | added | Jonathan Bone | Hi yes that is correct. The data specifically is the presence/absence of a certain decision in a game with which there is many rounds. I am therefore using a mixed model with subject id as the random effects. Maybe I am mistaken in thinking that the high number of zeros is a problem in a glmm with binomial errors? | |
| Jun 3, 2013 at 12:28 | comment | added | COOLSerdash | I don't understand: Doesn't 0 mean "absence" and 1 "presence"? If not, what are the zeros referring to? | |
| Jun 3, 2013 at 12:27 | history | asked | Jonathan Bone | CC BY-SA 3.0 |