Thanks a lot. I have got a question: that's right, PCR is designed to resolve regression tasks. And what about classification? For instance, two classes are assigned as an output. The variables are reduced to the principal components, ok. Is it correct to use the obtained PCs for classification instead of original variables afterwards? Thanks for consultation!
Yes, that works. Have a look at this video about LDA where I compare PCA and LDA for classification: ruclips.net/video/julEqA2ozcA/видео.html I also have a video about PLS-DA ruclips.net/video/zqFZ0mEc74k/видео.html
The scores of PC1 were rounded, which explains the difference. If you use the rounded scores, you will get what you say. Here are more exact values for PC1: 104.9293 107.7236 109.3397 110.5179 112.1340 114.9283
If I want to fit a linear regression model after pca, do I need to regress y on centered score or centered y on centered score? I have been confused about this question.
@@tilestats Not happening. I hope that you might be having some country specific criteria. will suggest you to kindly email me a separate link for making payment.
Thank you! I know PCA regression is an alternative of a multivariate regression to deal with multicollinearity. So, is PCA regression always better for prediction than a standard linear regression?
Thank you very much for the video! Very clearly explained!
Thank you so much for this video!
It really helped me to understand PCR.
Thanks
You are the best in my opinion. And I'm not bluffing
Thank you very much; very well explained!
thank you so much!
It really helped me
Here bellow is my model obtained after standardization ((X-mean)/stdev) of your sample data and PC1 and PC2 calculation:
Y= -3.75Chol+5.20Age+375
Thanks a lot. I have got a question: that's right, PCR is designed to resolve regression tasks. And what about classification? For instance, two classes are assigned as an output. The variables are reduced to the principal components, ok. Is it correct to use the obtained PCs for classification instead of original variables afterwards?
Thanks for consultation!
Yes, that works. Have a look at this video about LDA where I compare PCA and LDA for classification:
ruclips.net/video/julEqA2ozcA/видео.html
I also have a video about PLS-DA
ruclips.net/video/zqFZ0mEc74k/видео.html
If PC1 is assumed as explatory variable and use least square then
B0 is -75 and B1 is 1.85
How you get B0 = -83.9 and B1 = 1.932
The scores of PC1 were rounded, which explains the difference. If you use the rounded scores, you will get what you say. Here are more exact values for PC1:
104.9293 107.7236 109.3397 110.5179 112.1340 114.9283
If I want to fit a linear regression model after pca, do I need to regress y on centered score or centered y on centered score? I have been confused about this question.
It should not matter because centering y only affects the estimated intercept...
do we need to center the data?
No, but it simplifies the calculations.
I tried to purchase books (ePDFs) but it seems that the payment system is not working properly.
We just tried it, and it works. Maybe it was a temporary thing.
@@tilestats Not happening. I hope that you might be having some country specific criteria. will suggest you to kindly email me a separate link for making payment.
Send an email to andreas.tilevik@gmail.com
Epdf on which topic sir please reply
@@mdmahmudulhasanmiddya9632 Logistic Regression
Thank you! I know PCA regression is an alternative of a multivariate regression to deal with multicollinearity. So, is PCA regression always better for prediction than a standard linear regression?
It is harder to interpret the coefficients in PCR, so I would select linear regression as long as its assumptions are fulfilled.
@@tilestats Thank you for your answer!