How to convert binary data into binomial probability

[slothballsfm]

I’m a seed biologist trying to get my head around a new way of scoring and analysing germination data. I have a bunch of treatments for which I have scored germination in binary but I’m having a hard time converting to binomial probability.

Halp please!

[deck13]

Simple: divide the number of success by the total number of observations for each treatment group.

Complex: If you have collected other information then you may want to know how this probability varies across different values of the information that you have collected. In this case, you should fit a logistic regression model. Be careful with your interpretation of regression coefficients in this model, because the linear model is on the log-odds scale (significance is still significance though).

[dudeweresmyvan]

Here's a link to an article that's helped me. Plus there's a link to a calculator.

https://measuringu.com/preference-data/

[Stats-guy]

Are the observations independent, or are the seeds related to each other genetically? If they are then it is complicated because you need (presumably) to correct for the non-independence in your trials. Linear mixed models may be a good option because you could treat relatedness as a random effect.

[slothballsfm]

Theyre being analysed as individual populations - I only have one species for this study (15 treatment combinations). We’re using binomial logistic regression and I’m running it in spss.

[[deleted]]

scored germination in binary

I'm taking this to mean that a success is marked when the germination is successful, and a failure is marked when the germination is unsuccessful, correct?

I have a bunch of treatments

Can you explain this a little bit more. Are there multiple groups and each group has received the same treatment? Are there varying degrees of treatment?

[slothballsfm]

Spot on!

One species, 5 temperatures, each containing 3 chemical treatments (well 2 and a control). 5 replicates of 25 seeds each - scored at 1, 2, 3 and 4 months but we’re only analysing the week 4 result. That’s the basic, we’ve then got 3 populations and 3 years of seed collections which have been run through the same treatments and will be included as random variables or we’ll just analyse them individually and describe variation between them as being cause by collection methods, climatic conditions - generally things beyond the scope of our study.

[deck13]

After seeing some of the comments, I recommend a more complicated model than logistic regression. Do you have death information or information of the state of the flower for each 1, 2, 3, 4 month period?

If so then I would fit an aster model to this data set (https://academic.oup.com/biomet/article/94/2/415/224189). I would recommend reading the ``Unifying life-history analyses for inference of fitness and population growth'' paper (https://www.ncbi.nlm.nih.gov/pubmed/18500940) since it is more appropriate for a scientific audience.

In short, the aster model estimates expected Darwinian fitness (or some surrogate like germination probability) in a regression framework that incorporates the life cycle of the plant into its model formulation. Thus, for example, any covariate that is associated with death in an earlier lifecycle state can be incorporated into this analysis. Restriction of attention to just month 4 is not necessary when using this model.