How can a fungal infection be simulated?

My response variable is a count (1, 2, 3 ... etc.) with right skewness, and actually we use the inverse of the count (1/count). So a count of 1 gives 1, 2 gives 0.5, 4 gives 0.25 and so on. The data distribution changes from right to left skewness.

Gamma regression is usually recommended with right skewed data. If not suitable for left skweness, what is the alternative?

There seems to be vagueness when it comes to the difference between two way repeated measures and generalized linear mixed model (GLMM). As far as I know, when there are no missing values in the data of two independent variables, even if one of them is within and the other is between, there won't be any problem if the two way repeated measures is used instead of GLMM. In my case, there are no random variables but just fixed ones. One of them is time as a categorical variable. The other is treatment as a categorical variable,too. Treatment is a between-subjects variable, whereas time is a within-subjects variable. So, in this scenario, I consider I don't need to use generalized linear mixed model. I just use two way repeated measures, since there are no missing values. Is it correct ?

Hi

I measured some brain responses to different sound stimuli from 20 subjects. I have 543 observations in my dataset. I tried to fit the mixed effect model. When I did model control, both tests of normality and homogeneity of residuals failed (see also QQplot). I investigated residuals and found some outliers. If I remove 9 observations from my dataset with the largest residuals, I get the beautiful QQplot and both normality and homogeneity tests are passed. The question is: am I allowed to eliminated the outliers from the data? And if Yes, how should I report it?

Thanks for advance

Best regards

Anna

I am currently analysing some data (using R). One variable to analyze is the amount of time individuals spent on a certain action. The time ranges from 0 to 120 seconds with most individuals spending the entire 120 sec on the action. Consequently, the data is strongly left-skewed.

I want to analyse it with a generalized linear mixed-effect model because there are two fixed effects (treatments, potentially interacting with each other) and two random effects that should be accounted for. But I am not sure what distribution (family) I need to choose in order to avoid a violation of the model assumptions.

model <- glmer(variable ~ treatment_1*treatment_2 + (1|randomfactor1) + (1|rf2), data=mydata)

Has anyone suggestions?

I have a dataset with distances between beneficiaries and the nearest provision point (nearest hub).

I want to develop a model to explain distances based on several atrributes like category of beneficiary, category of provision point among others:

distance ~ cat_beneficiary + cat_provision + altitude + ...

I guess I should use a GLM, but I don't know which model would fits better with this kind of data (continuos and positive). Can I use a count data model (like Poisson or NB)? Or they just work with discrete data?

I attach a histogram.

We know the generalized linear models (GLMs) are a broad class of models. When fitting GLMs in R, we need to specify which family function to use from a bunch of options like gaussian, poisson, binomial, quasi, etc. I would like to have your advice regarding how to determine the optional family function used for GLM fitting in R. Thanks!

We are currently analyzing turnover (as part of beta diversity). The data is not normally distributed, but mostly left-skewed, values can be between 0 and 1. We are using linear mixed effects models and are wondering which data distribution we should use. Has anyone suggestions?

Respected Scholar,

kindly help me in knowing the situation when we shift from OLS to gamma GLM ...

Tahnk you