Timeline for a covariate versus a random effect
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Jun 20, 2019 at 1:31 | comment | added | antR | Thanks for the tip. Unfortunately that none of this actually answers the question of whether or not initial sampling abundances should be used as a covariate or a random effect. | |
| Jun 20, 2019 at 1:16 | comment | added | pankaj negi | My understanding is that random effects are usually categorical variables with a different intercept and slope for each level. Also, it works best when they are many levels to the categorical variable. I would suggest briefly reading 'Data Analysis Using Regression and Multilevel/Hierarchical Models' by Andrew Gelman and co. It has a chapter on modelling count data. | |
| Jun 20, 2019 at 1:15 | review | Low quality posts | |||
| Jun 20, 2019 at 3:54 | |||||
| Jun 20, 2019 at 1:04 | comment | added | antR | Ah. I'm more keen on keeping inital sampling as a covariate or a random effect as the question I'm interested doesn't have anything to with rates. What are you opinions of using it as a covariate versus a random effect? | |
| Jun 20, 2019 at 1:00 | comment | added | pankaj negi | The offset formulates the problem as a rate. So you would be modeling the ratio of abundance of prey after experiment to before experiment. The offset variable would have a coefficient of 1 | |
| Jun 20, 2019 at 0:56 | comment | added | antR | I used a negative binomial model because of overdispersion but what do you mean by an offset? | |
| Jun 20, 2019 at 0:55 | history | answered | pankaj negi | CC BY-SA 4.0 |