Excellent study material. BUT BECAME BLURRED FROM 6.45 TO ABOUT 16.05 MINUTES. Please how can I study the entire video clearly? Thank you for such a summary.

I have a question, what if i want want to perform PCA on data that have not just different scale, but also different unit, such as data that involves environmental parameters such as temperature, humidity, light intensity, etc. Will scaling the data can solve this? Thank you

use remote sensing data

Sir, please make a videos regulary

great tutorial, although some functions are not up to date anymore (like plot for boxplot is now boxplot etc.)

attend to learn

when i generate the PCA with the code explained @ 20:46 my legend appears as a gradient rather than the separate values (as in your three different species appearing in red, blue green. how can i change this?

Outstanding. Thank you.

really love your explanantion! thank you so much for your video, really helpful and i can understand it! keep it up! looking forward to your many more upcoming videos

really useful video thank you, I've just started my MSc project using PCA, so thank you for this. I will be following subsequent videos.

thank you so much for this video. incredibly helpful.

Great tutorial but it leaves me with the question, what do i do with it? Is this just the begining of a K means classification that gives me an idea of the proper k?

Fantastic. Subscribed!

Many thanks for creating this. I found it very useful.

Great! There are many practical points.

10:21 - When using "prcomp", the calculation is done by a singular value decomposition. So, these are not actually eigenvectors, right?

Many thanks for your efforts to make this complex issue much easier for us. Could you enlight me to understand group similarly and dissimilarity using pca?

How is it possible to generate outliers uniformly in the p-parallelotope defined by the coordinate-wise maxima and minima of the ‘regular’ observations in R?

You are really doing amazing videos with providing the class codes, that's totally amazing. Why are not you continuing your video?

In fact I found out how to overcome the multicolinearity , by using the eigen values of PC1 and PC2! I love PCA!

Thank you for this great video. can you show how to seek multicolinearity or treat multicolinearity with PCA ? I have a data set with 40 variables with high intercorrelation because of cross reactivity . VIF and matrix correlation doesnt work probably because of multiple comparison ....:(((

Excellent walkthrough. Thank you!

Wow! Absolute great video, well explained + really helpful tipps and tricks! Thank you for that!

Added to my stats/math playlist! Very useful.

please explain Scatterplot using DAPC also

Ty for the quality content brother , I am beginner that's been very helpful can you please provide more videos 🙂 thankyou

Why not uploading more videos brother?

How apply conditional vlookup in r program?

5 year old video still one of the best I found on the topic on YT. Thumbs up

Sir, you are a saint! Thank you thank you thank you!!! Not only did you make this easy but you gave me peace of mind. If we ever meet in person, I hope you will give me the honor of buying you a drink.

Great video! How can I have the package ggplot2 in the new R version?

Amazing video! Thanks for the explaining everything very simply. Could you please do a video on PLS-DA?

I wish you had made a career out of KZbin tutorials, this is the best R tutorial I've found. You seem to understand very well what is important to know for beginners and convey it beautifully. Thank you so much!

Thanks for the the video, it helped me a lot!! Your explanation is very didactic!

Your videos are Gems!!

Thank you so much, I've watched almost all your videos and am now able to use R for useful data processing tasks in my work field which is agriculture. You're great at explaining, cheers!

Thanks much Hefin!!!

Thank you for the video. I have a question regarding the option of exporting or saving graph as Jpeg or PNJ. How can we keep the font size exactly the same after saving pic. I did set up fig.height and fig.witdth in '''{r}, but the title of the graph misses some characters of the end. Thank you !

How can I upload my data into RStudio to work with ?

Can you run PCA on factor variables coded as 0 vs 1. 1 meaning presence of something?

thank you very much

GRE = graduate record exam - a standards base testing, evaluating preparedness for grad school

Explained everything one might need. If only every tutorial on KZbin is like this one!

Nice explained! Thank you!

Giving commanding is tough....

Where did you define PC1 and PC2 (where you use them in the ggplot)? I'm getting "Error: object 'PC1' not found"

Wow, Thank you!!! Please some more

Thank you so much for your videos!! Your videos are the best I have seen hands down :) All of your explanations and step by step through R are what I needed to work on my research. One area I am having trouble with (since I am not a statistician) is making sure I run my data through all the necessary statistical tests before running the PCA. My data is similar to the iris dataset (skull measurements categorized by family and subfamily levels) but I am seeing different sources run different tests before the PCA (ANOVA vs non-parametric tests). If anything, would you be able to recommend some good sources for me to refer to? Thank you! I really appreciate it!

superb!

Dumb question, but the way we interpret our results given different variables for our logistic regression, it's always "in the direction of whatever our "1" value is"? As in, a positive odds ratio says we are X times likely to see (fill in blank for definition of value 1 = "get admitted to college"; "catch a fish while fishing", "Win the lottery")? This is not the odds of getting a "0", correct?