Thread by @selcukorkmaz

1/10: 🧪📊 Introducing Generalized Linear Models (GLMs) and how to perform them using R! A thread. #GLM #Rstats #DataScience

2/10: 💡GLMs are a general class of regression models that extend linear regression, allowing for a variety of response distributions & link functions. They're used for modeling relationships between a response variable & one or more explanatory variables. #RStats #DataScience

3/10: 📐The main components of a GLM are:

Random Component: The response variable's distribution (e.g., Gaussian, Poisson, Binomial)
Systematic Component: Linear predictor (linear combo of explanatory variables)
Link Function: Connects the two components. #Rstats #DataScience

4/10: 🧩 To perform a GLM in R, use the glm() function. It requires two main arguments: a formula specifying the model and a family argument specifying the distribution & link function. #RStats #DataScience

5/10: 🔧 Example: modeling count data (Poisson distribution) with a log link function.

model <- glm(response ~ explanatory_variable, family = poisson(link = "log"), data = dataset)

Replace response, explanatory_variable, and dataset with your own data. #Rstats #DataScience

6/10: 🔍 Check your model's goodness-of-fit using summary() and AIC() functions to evaluate the residual deviance and Akaike Information Criterion, respectively. #RStats #DataScience

7/10: 📈 To visualize the relationship between the response variable and the explanatory variables, use plot() and lines() functions in R. #DataViz #RStats #DataScience

8/10: 📚 Learn more about different types of GLMs, their distributions, and link functions in the R documentation: rdocumentation.org/packages/stats… #RStats #DataScience #GLM

9/10: 💡 Remember, model selection and interpretation should be based on a strong understanding of the data, domain knowledge, and appropriate statistical techniques. Always validate your models using cross-validation or other methods. #RStats #DataScience

10/10: 🚀 That's it! You're now ready to explore the world of Generalized Linear Models using R. Good luck, and happy modeling! 🎉 #GLM #Rstats #DataScience