I've used glm with backwards elimination to assess importance and interactions of multiple factors (independent variables) in a research in the field of architectural engineering.
The independent variables have 2-4 levels, and are either categorical or discrete numbers.
The summary of results as a table is fairly clear for me to read as it includes all necessary information I want to see such as t-value, estimate, standard error, significance, etc.
However, I'm wondering what's a proper way to visualize these information/data to make it graphically easier to understand for the audience of my research who are not that much into statistics. They are architects or building/architectural engineers.
What packages, commands, etc would you suggest for this purpose?
I would appreciate it if you could support your answers with examples of code and visualization. Thank you
Dear Aryan Shahabian
There are lots of commands you can use to visulaise your data in R, such as plot(), barplot(), his(), boxplot(), densityplot(), splom(),..etc. and There are lots of useful packages in R which you can use for your data visualization, such as "ggplot2" and "plotly". I recomend using ggplot2 which provides very nice plots.
Please see the following article for a comprehensive guide to data visualisation in R for beginners:
https://towardsdatascience.com/a-guide-to-data-visualisation-in-r-for-beginners-ef6d41a34174
Good luck
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