You are very welcome!

You can use the "quasipoisson" as the family or the negative binomial model

Yes you are right. I presented as my way for pedagogical purposes. Cheers!

Hi there. This example assumes cross-sectional data. However, yes, you can extend this to panel data (e.g., rpsychologist.com/r-guide-longitudinal-lme-lmer). That said, I don't believe you can model more complex level 1 error structures (e.g., autoregressive) using lmer (as you can do in other programs). Cheers!

@@mikecrowson2462 thank you very much...

Hi Aman, I'll put this on my "to-do" list. I think I'll probably be doing something on this sometime in the near future. Best wishes!

Thank you, Sir.

Hi Georgi, thanks for your question. In the preface that most of the analyses carried out in the text are done using HLM (the program by Scientific Software International) - with some SEM-based approaches using MPlus. Sometimes, there are differences between programs in certain defaults (e.g., in terms of test statistics reported, such as z-test versus t-test of fixed effects) and even approaches to estimating standard errors (based on raw residuals, Pearson residuals, normalized residuals; use of robust standard errors versus no). I'll be honest, I haven't spent much time unpacking the differences between HLM and the lme4 package, but it looks like HLM uses Robust standard errors (see stat.utexas.edu/images/SSC/Site/M_HLM_Tutorialv2.pdf) by default, whereas this does not seem to be the case with 'lme4' (at least I have not found any discussion of robust standard errors in the documentation: cran.r-project.org/web/packages/lme4/lme4.pdf or cran.r-project.org/web/packages/robustlmm/vignettes/rlmer.pdf). This is further supported by a post here arxiv.org/abs/1612.04911 and article here (www.jstatsoft.org/index.php/jss/article/view/v087c01/v87c01.pdf) where the authors review their strategy for obtaining these standard errors when using lme4. Hope this helps!

@@mikecrowson2462 Thank you for the prompt response! This could indeed be the case so I will have a look at the sources you provided (just to let you know the first link you provided requires permission to access the site). It is slightly disturbing, though, because in Hox's analysis in the book the variances are statistically significant, while when you perform the analysis with the lme4 package, they are not. I will look into that and if something comes up I will give you a shout here. Thanks again and keep up with the nice work you're sharing with us!

Hi Aman, in general, the more complex your model becomes, the more likely you might run into problems with model convergence (that is because you are estimating additional parameters; by the way, if you specify an unstructured matrix, then it's even more complex as you will have variances and covariances among all randomly varying intercepts and slopes). But apart from this kind of issue there is no prohibition against specifying more than a single slope as randomly varying. Cheers!

@@mikecrowson2462 thank you Sir.

@@mikecrowson2462 But the question stands that if i include a random slope of top level predictor then will than mean to a lower level outcome variable.?

I'm running it in RStudio, it shows the error information. But I tried it in R then I can see the structure of the data

@@guani2155 ditto. did you do the rest in r vs. r studio?

@@daviddearlove Hi David, I was using R studio

I think the value of level 2 variables stay the same within a particular group, but varies with other groups. See the data structure posted at the beginning of the video