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Hi @sakifzaman. I suspect that you might have typed the aesthetics (in the aes part) with "quotes" instead of `backticks`. The difference is easy to miss (backticks are like single quotes pointing in the other direction). In R, backticks are used for names that have spaces in them. So `GDP per capita` is the name of the column. If you use quotes (single or double) instead, ggplot interprets it as a value. So this is why it would say that you provide aesthetics of length 1 (just the value "GDP per capita", even though the data has 144 rows.

Surely we can make it work. What error do you get?

@@gotnolove923 ah haha, that was me on another account that I was trying to delete.

Hi Murphyalex. Thanks for your comment! The notation used here is based on the book in the description. I was also initially confused about using eta as the systematic component, and then defining mu from inside the link function rather than the output of the link function, but thats how the link function is defined, and when you read their runthrough of the generalization it makes sense (just looked it up again; page 42, highly recommended). Note that mu is still defined, but as the inverse of the link function over eta. So for example, for poisson the mean function for the poisson distribution is defined as mu = exp(eta), which is identical to eta = log(mu). Or am I missing something else that you're referring to?

Hi @guaravsutar9080, I'm afraid I haven't planned anything of the sort. It's been a while since I used topic modeling (simply because my research took me elsewhere), so I'm not fully up to speed on the current state of the field.

@@kasperwelbers oh yes thank you so much Actually I’m going through it but some of the codes I’m not able to interpret in R

Thanks! I mostly used OBS, which is an open source tool for recording and streaming. I found it quite intuitive (with some tutorials), and as someone without any editing experience was able to set up a good simple system for switching and layering windows. (Though to be honest, this was amid early pandemic despair over how to manage online teaching, so I probably did spend quite some time on it). For the weather-person effect of talking in front of a screen, I bought a pull-up greenscreen, though since then I think automatic background filtering has come a long way, so a greenscreen might no longer be needed. I also used Kdenlive for editing. In my case I only used this for cutting and pasting pieces of recordings, which didn't really take long to figure out, but I think that tool also supports more advanced editing.

@@kasperwelbers thank you so much!

If I recall correctly, the correlated topic model mostly differs in that it takes the correlations between topics into account in fitting the model. It probably adds a covariance matrix, but there should still be posterior distributions for document-topic and word-document, and so you should still just be able to visualize the correlations of topics and documents (or topics with topics) in a heatmap. Though depending on what package you use to compute them, extracting the posteriors might work differently.

@@kasperwelbers Thank you! I tried this code, it seems to have worked for basic LDA: beta_x <- tidy(x, matrix = "beta") beta_wider = function(x){ pivot_wider(x, values_from = beta, names_from = topic) %>% arrange(term) %>% select(-term) %>% rename_all(~paste0("topic", .)) } beta _w <- beta_wider(x) cor1 <- cor(beta_w) I then plotted a correlation matrix.

Hi! Do you mean vastly different results, or very small differences? I do think some of the multilevel stuff could in potential differ due to random processes in converging the model, but if so it should be really minor.

Thanks!

Ahaha, not sure whether that's a question or a burn 😅. This is just a Blue Yeti mic in the home office I set up during the COVID lock downs. The room itself has pretty nice acoustic treatment, but I was still figuring out in a rush how to make recordings for lectures/workshops and it was hard to get clear audio without keystrokes hitting through.

Hi @roxyioana, please check the link to the tutorial in the description. We keep that repository up-to-date with changes. (and at some point I hopefully find the time to re-record some videos)

Interesting question. We can actually add more groups to the model instead of aggregating, but it depends on your question. In the example, we used days as a continuous variable, because we wanted to test if there was a linear effect on reaction time. If you also want to consider the time of the day as a continous variable, then it indeed becomes awkward how to combine them. However, maybe your reason for the four measurements is just to get more data points, so you think of them as factors rather than continuous. While aggregating might be viable, you could also consider adding another level to your model, for whether the measurement was in the (1) morning, (2) afternoon, (3) evening, or (4) night. You could then have random intercept, for instance to take into account that people might on average have lower reaction times in the evening due to their after-dinner-dip. (though note that with just 4 groups you might rather want to use fixed effects with dummy variables) Perhaps more generally, what you're interested in is multilevel models with more than one group level. This is possible and very common/powerfull. Groups can then either be nested or crossed. be nested, for instance people living in cities.

I think the easiest way would be to use the readtext package. This allows you to read an entire folder ("my_doc_files/") or use wildcards ("my_doc_files/article*_.txt). cran.r-project.org/web/packages/readtext/vignettes/readtext_vignette.html#microsoft-word-files-.doc-.docx

If I understand you correctly, I think it's indeed possible to model a dependent variable with a tri-modal distribution with glm. Actually, you might not even need glm for that. Whether a distribution is multimodal is a separate matter of the distribution family. A tri-modal distribution might be a mixture of three normal distributions, three binomial distributions, etc. Take the following simulation as an example. Here we create a y variable that is affected by a continuous variable x, and a factor with three groups. Since there is a strong effect of the group on y, this results in y being tri-modal. ## simulate 3-modal data n = 1000 x = rnorm(n) group = sample(1:3, n, replace=T) group_means = c(5,10,15) y = group_means[group] + x*0.4 + rnorm(n) hist(y, breaks=50) m1 = lm(y ~ x) m2 = lm(y ~ as.factor(group) + x) summary(m1) ## bad estimate of x (should be around 0.4) plot(m1, 2) ## error is non-normal summary(m2) ## good estimate after controlling for group plot(m2, 2) ## error is normal after including group

Good question. I'm not an expert on Turkish, so I don't know how well these bag-of-word style approaches work for it, but there does seem to be some support for it in quanteda. Regarding stopwords, quanteda uses the stopwords package under the hood. That package has the functions stopwords_getlanguages to see which languages are supported. Importantly, you also need to set a 'source' that stopwords uses. The default (snowball) doesn't support Turkish (which I assume is TR), but it seems nltk does: library(stopwords) stopwords_getsources() stopwords_getlanguages(source = 'nltk') stopwords('tr', source = 'nltk') Similarly, for stemming it uses SnowballC. Same kind of process: library(SnowballC) getStemLanguages() char_wordstem("aslında", language='turkish') # (same should work for dfm_wordstem) So, not sure how well this works, but it does seem to be supported!

@@kasperwelbers This is so helpful, thank you!!

Hahahaha 🤣. Nice, thanks!!

If your file is too large to keep in memory, the only option is to work through it in batches or streaming. So the first thing to look into would be whether there is a package in R for importing ZST files that allows you to stream it in or select specific rows/items (so that you can get it in batches). But perhaps the bigger issue here would be that with this much data you really need to focus on fast preprocessing, so that you'll be able to finish your work in the current decade. So first make a plan what type of analysis you want to do, and then figure out which techniques you definitely need for this. Also, consider whether it's possible to run the analysis in multiple steps. Maybe you could first just process the data to filter it on some keywords, or to store it in a searchable database. Then you could do the more heavy NLP lifting only for the documents that require it.

Hi @67lobe, I don't think I discuss word files in this tutorial. But I think the best ways are to use the 'readtext' package, or 'antiword'. The readtext package is probably the best to learn, because it provides a unified interface for various file types, like word, pdf and csv.

You can certainly have multiple groups. First, you could have groups nested in groups. If you perform the same experiment in many countries across the world, your units would be observations nested in people (group 1) nested in countries (group 2). Second, you could have cross-nested (or cross-classified) groups. For example, say we want to study if the effect of more alcoholic beverages on blood pressure differs depending on the type of alcoholic beverage (beer, wine, etc.). In that case, each person could have observations for multiple beverages, and each beverage could have observations for multiple people.

@@kasperwelbers I see, thanks. I can imagine that having all these nested and cross-nested groups can complicate quite a lot the model and its interpretation.

Yes, you're correct. The quanteda api has seen some changes since this video was recorded. You can still pass a corpus directly to kwic, but it will now throw a warning that this is 'deprecated'. This means that at the moment it still works, but at some point in the (near) future it will be mandatory to tokenize a corpus before using kwic