Negative Binomial & Zero-Inflated Models in R using Microbiome Data | Nutribiomes

Libraries mentioned:
install.packages("ggplot2", dependencies = TRUE, repos = "cran.us.r-proje...")
install.packages("MASS")
install.packages("pscl")
Instagram: @nutribiomes
Twitter: @DrKebbe

[@allthingsrandom1855]

Thank you so much, just what I needed. I have struggling with this part of my analysis so thank you for this Maryam

[@FlorisHuider-w6m]

Great video, thanks!

[@pedroriosguayasamin640]

Thank you very much for the explanation. Any chance that you can share a link that may explain the interpretation of interactions in a NB or ZINB models?

[@shlomopreiss-bloom7577]

Thanks so much, this was exactly what I needed!

[@battleon81]

I was just wondering if something like this was actually necessary for my data. Turns out it lowered the AIC by 155.

[@alexiscanari8776]

Thank you so much for your videos!! I have a brief question, in the case of negative binomial in panel data, would you recommend using this library glmmTMB? thank you a lot in advance for your answer :)

[@michaelallen185]

What keyboard do you use? Or is it just the keyboard on your macbook?

[@adrianfigueroa8093]

Hi Dr. Kebbe, I'm a PhD candidate currently working on my dissertation research. My response variable follows a zero-inflated beta distribution. Would you have any advice for me to model this? I am having trouble finding an R package that allows me to do that.

[@laxmanbisht2638]

thanks!

[@sanujaahammu6747]

Hi, very informative video..thanks

[@parthparikh6464]

I want to know how to interpret the data after we get over the result of a zero-inflated negative binomial. How to define the coefficient-and-variable in a data relationship

[@yusufhaji9091]

Thank you,I was enquiring if is possible to send the codes you were using since it is not clear to me

[@mimmim1673]

Great

[@PEOPLE-777]

It's , really interesting
I'm going to put ZIP for bioChemists data set, if is it possible please make mode on that data set.
Thank you

[@sanujaahammu6747]

I am using negative binomial zero inflated mixed model for my thesis project on gut microbiome in babies. I have 48 subject measured at three different points. The subjects are from 2 different locations. They are evaluated for allergy at later stage..so how i find the right model

[@nutribiomesmaryamkebbephd9830]

Hi Sanuja, thank you for watching. This will depend entirely on what research question you are hoping to answer, how your data looks like, etc... You may consider the glmm.zinb package that allows for random effects to account for a longitudinal design, if this fits with your purposes.

[@sanujaahammu6747]

@@nutribiomesmaryamkebbephd9830 Thanks

[@sanujaahammu6747]

@@nutribiomesmaryamkebbephd9830
I want to find how a genus varies over different time points in subjects from both locations. Will the model be
zinb = (counts = Time + location|Subject + offset (log(N)), link ="logit", dist = "negbin", data = data4)
where counts = count data of bacterial taxa
N - total no of reads in each sample
Time - week 1, week 4, week 24
location - X and Y.
And after running this model, what is the next step? do i use the predict function? Sorry I am new to this topic
Thanks

[@TheForceVM]

@@nutribiomesmaryamkebbephd9830 Can you share the xl file of the data so we can practice matching the results ? blongum.xlsx