Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation

[@shawnmckenzie8699]

To install ggbiplot, the code is now (17, Jan, 2020): library(devtools) install_github("vqv/ggbiplot") source: github.com/vqv/ggbiplot Excellent video and well explained these concepts. Thanks.

[@bkrai]

Thanks for the update!

[@abdullahmohammed8521]

Many thanks for you Dr. God bless you.

[@bkrai]

You are most welcome!

[@ramram2utube]

I revisited your video for interpretation of biplots in PCA. Many thanks.

[@bkrai]

You are welcome!

[@philipabraham5600]

This is the best PCA explanation I have seen anywhere so far. Thank you for sharing your knowledge.

[@bkrai]

Thanks for the feedback!

[@ashishsangwan5925]

Awesome Explanation

[@bkrai]

make sure you run following before installing: library(devtools)

[@Drgautham]

Thank you so much Professor🙏

[@bkrai]

You are very welcome!

[@ramram2utube]

Thanks a lot Sir for your nice presentation. You saved my time. Earlier I used your R codes on Kohonen NN and now for PCA for my training lectures. Your explanation is so lucid. I appreciate your noble service of sharing knowledge

[@bkrai]

You are most welcome!

[@babadrammeh656]

R PCA IS VERY GOOD PACKAGE AND VERY HELPFULL

[@bkrai]

Yes, I agree!

[@modelmichael1972]

Awesome video. Every R enthusiast needs to keep an eye on your channel. Thank you and keep up with great work!

[@bkrai]

+Model Michael thanks👍

[@padhanewalaullu]

Sir, Can we get code file ?

[@jacklu1611]

The Bio-plot was explained very clearly, thank you Dr. Rai!

[@bkrai]

You are welcome!

[@Dejia_Space]

Thank you!!Best explanation on Biplot on KZbin .

[@bkrai]

Glad it was helpful!

[@jinnythomas9815]

Great Explanation....

[@bkrai]

Thanks!

[@nyatonkitnya4267]

one really good video i have found. After watching few of your video now your videos are becoming a "turn to" when require. thanks

[@bkrai]

Glad to hear that!

[@jonm7272]

Thank you for this extremely helpful, and easily understood tutorial, particularly the clear interpretation of the Bi-Plot. Much appreciated

[@bkrai]

You're very welcome!

[@NIKHILESHMNAIK]

You are too good sir. An absolute treat for ML enthusiasts.

[@bkrai]

Thanks for your comments!

[@saurabhkhodake]

This video is worth its weight in gold

[@srujananeelam6547]

Fantastic session.Perfectly understood Biplot

[@bkrai]

Thanks for comments!

[@theeoddname]

Great Video! Excellent walk though on PCA and how it can be useful for actual classifications. Thanks for the upload.

[@bkrai]

+theeoddname thanks for the feedback!

[@bucklasek1]

Thanks for the video! It helped me a lot doing the forecasting for future values using PCA.

[@bkrai]

Very welcome!

[@koparka112]

Thank you for the material. It is very clear and actually very relevant to my current work. As I understand, the conversion of the data comprises addition products of notmalized predictors and loadings. Maybe you would have time to post a PLS regression video, please? The intriguing part is the explanation of the model itself

[@deepikachandrasekaran3554]

Very useful video sir. Could you explain me what is the need to partition the data into training and testing data?

[@bkrai]

You may review this: kzbin.info/www/bejne/l4SUgGt7nqx_msk

[@deepikachandrasekaran3554]

@@bkrai thank you sir.

[@galk32]

One of the best PCA videos i ever seen, Thank you Mr. Rai.

[@bkrai]

Thanks for comments!

[@flamboyantperson5936]

This is great. I was looking for PCA and you have done it. Many many thanks to you sir.

[@nyadav378]

Very informative and nice presentation sir, sir can we estimate PCA for factor (for eg species) with unequal no. of observation. And we want to see the correlations in terms of each species viz for setosa or other two, how to do it? Please explain...Thank You

[@jonimatix]

I really like your explanations in your videos. Keep them coming! Thanks

[@bkrai]

Thanks for the feedback!

[@abhishek894]

Thank you for this nice video Dr. Rai. I have a doubt. Why the predict function was used multiple times. After the prcomp function, all the data of Principle components were available in: pc$x. Why do we have to do: trg

[@bkrai]

In R you can get same thing in multiple ways. This is just for illustration.

[@abhishek894]

@@bkrai Thank you Sir. That makes it clear.

[@bkrai]

@@abhishek894 You are welcome!

[@eldrigeampong8573]

Thank you so much Dr. Rai. Detailed teaching

[@bkrai]

Thanks for comments!

[@affyy04]

Thank you for this amazing video. Better than my university lectures

[@bkrai]

Thanks for comments!

[@Rutvi_patel_1111]

Fabulous work in PCA ! Keep it up

[@bkrai]

Thanks for the feedback!

[@ConeliusC33]

Your videos have been constant companions during the last months of my master thesis. It seemed as if every time I had to switch to another analysis technique you were allready waiting here. So thank you a lot for your guidance and clear explanations! The only thing I would appreciate would be if you could provide the basic R scripts. Even though the copying process might help with understanding each command due to step by step application, to type text of a tiny youtube screen shown in one half of my monitor to r studio in the other half is troublesome. Thanks!

[@bkrai]

Thanks for the feedback!

[@inesceciliacardonadevoz5072]

Thanks for this video sir, very good class but I can´t get it. because Error ... could not find function "ggbiplot". Excuse me, which is your R version ?

[@bkrai]

Try this: library(devtools) install_github("vqv/ggbiplot")

[@WahranRai]

19:12 It is only for purpose to show another way to get the principal component related to training because : identical(pc$x, predict(pc,training)) gives TRUE meaning that pc$x is same as predict(pc,training).

[@bkrai]

That's correct!

[@earlymorningcodes6100]

Orthogonality of principal component- 10:17

[@bkrai]

Thx

[@prithvivasireddy5564]

Awesome video sir...kudos... :) 1 doubt though .... 20:48 - why are we using 2 components only? How do we know how many principal components to use?(species ~ PC1 + PC2)

[@bkrai]

2 PCs capture more than 95% of the variability in the data. Other 2 only add about 5%. So you can choose to have PCs that capture over 80% or 90% of the variability.

[@johnstevenson6458]

Great video. Do you have a suggested package for running binary logistic regression? From a brief scan of nnet it appears to only have arguments for multinomial response variables. Thank you.

[@bkrai]

You can refer to this: kzbin.info/www/bejne/d4fbaIqZZqiEbbs

[@johnstevenson6458]

@@bkrai sorry I was unclear in my message. I was hoping for a suggested package to run a binary logistic regression using PCA components as predictors - similar to what you have done here with multinomial. Any suggestions are welcome.

[@bkrai]

Yes, you can use the PCA components as predictors and run binary logistic regression as shown in the link that I sent earlier.

[@dioagusnofrizal9773]

Thanks sir, why in this video use linear regression? Can i use k means to clustering from pc1 and pc2?

[@bkrai]

Which line are you referring to?

[@dioagusnofrizal9773]

Sorry, i mean logistic regression in line 59

[@donne4real]

Wonderful job explaining the material.

[@bkrai]

Thanks for your comments and finding it useful!

[@maf4421]

Thank you Dr. Bharatendra Rai for explaining PCA in detail. Can you please explain how to find weights of a variable by PCA for making a composite index? Is it rotation values that are for PC1, PC2, etc.? For example, if I have (I=w1*X+w2*Y+w3*Z) then how to find w1, w2, w3 by PCA.

[@bkrai]

For calculations you can refer to any textbook.

[@upskillwithchetan]

Really really great explanation sir, Thank you so much for making it very simple

[@bkrai]

Thanks for comments!

[@anigov]

Dear Sir..thanks for a wonderful video. I have some questions. 1) At 20:18, why did u choose to reorder by setosa? 2)Why did you choose to use data as trg and not training to build mymodel given that trg has predictions from training 3) Can PCA be used to choose k in kmeans. If so, how to go about it? Thanks again. Regards

[@siddharthadas86]

Seriously awesome explanations! Thank you again.

[@bkrai]

Thanks!

[@earlymorningcodes6100]

scatter Plat and Correlation- 2:04

[@bkrai]

Thx

[@ainli4125466]

Thank you for sharing, I get an error "Error in plot_label(p = p, data = plot.data, label = label, label.label = label.label, : Unsupported class: prcomp"", when I try to run the ggbiplot. Would you please advise how to fix it?

[@asiangg]

Thank you. Learned a lot from your channel

[@bkrai]

Thanks!

[@katherinechau5594]

your videos are great :)

[@bkrai]

Thank you!

[@mukhtaradamuabubakar370]

Nice video and very helpful, I have challenges while installing the ggbiplot and mnet packages (am using R version 3.6.3) please any advice on how to over come such challenge?

[@mukhtaradamuabubakar370]

OK for the nnet package it was successfully installed. but still struggling with the ggbiplot (despite using your codes). thanks

[@LlamaFina]

Great video! Thanks for sharing your knowledge.

[@bkrai]

Thanks for comments!

[@souvikmukherjee7977]

sir, please make a session on factor analysis with prediction

[@bkrai]

Thanks for the suggestion!

[@soumyanayak445]

Sir why have you predicted the training and test data with respect to PC? can use trg data for making neural model and test using tst data set? and find correlation b/w act and predicted values?

[@bkrai]

When there are many variables, chances of having multicollinearity problem increases. And PCA helps to solve that problem. And yes, you can use neural network model.

[@soumyanayak445]

@@bkrai sir can you please explain me the significance of the lines under the heading: prediction with principle components.As I am unable to understand why we are predicting twice on test data set. Please explain sir

[@bkrai]

To avoid over-fitting where you get very good result from training data but not so from testing.

[@golumworks]

If I just use addEllipses =TRUE, what determines the size of those ellipses? Also, if I specify ellipse.type = “confidence”, what confidence level is used to generate the ellipses? I used factoextra if that helps.

[@mukeshchoudhary2842]

Great video.. What if we want to include factor-like "Control and Heat" for genotypes? Please suggest

[@bkrai]

It should work fine.

[@azzeddinereghais7494]

Good evening If you want to show the first dimension (Dim1) and the third dimension (Dim3) What to do or if you can provide the code for that Thanks

[@safeeqahmed3306]

Great video. I have one doubt. What does the stddev attribute of PC contain? Standard deviations of the variables are already in scale..so what does stddev represent? Thanks a lot

[@bkrai]

At what point in time do you see this?

[@safeeqahmed3306]

Bharatendra Rai sorry it’s sdev attribute of pc and in 9:48 while showing the summary of pc, I would like to know what the standard deviation row denote..thanks a lot

[@bkrai]

It is standard deviation related to principal components. It helps to estimate what percentage of variability is captured by each principal component.

[@safeeqahmed3306]

Bharatendra Rai thanks a lot. I understand this now

[@numitayogesh9280]

great lecture..please share your thoughts on machine learning introduction too

[@bkrai]

For machine learning such random forest, neural networks, support vector machines, and extreme gradient boosting, you can refer to following: kzbin.info/aero/PL34t5iLfZddu8M0jd7pjSVUjvjBOBdYZ1

[@k5555-b4f]

Hello great video as always! However one question i had (even though you warned against hard interpretability of results) relates to how to interpret the coefficients. If we look at the coefficient table and read the first line (after the intercept), does that mean that with every increase of Sepal.Length there is a log odd increase of 14.05 in the probability of categorizing the specie as Versicolor, relative to a Setosa? Thanks!

[@bkrai]

Your interpretation is correct.

[@k5555-b4f]

Thank you! Keep up the good work! Your r videos are great!

[@VenkateshDataScientist]

Sir ..ggbiplot is not installed hence cant work on this ..though i followed the video throughly

[@harishnagpal21]

Hi Bharatendra, nice video. I have got couple queries. If there are large no of numeric variables and through PCA we find that they are highly correlated then before going for model building 1) Do we need to remove highly correlated variables ! 2) which one to remove ! Thanks

[@bkrai]

You don't need to remove if you are using the components for developing a prediction model. This video provides a similar example.

[@harishnagpal21]

thanks

[@desert00200]

Principal components are orthogonal to each other, saying differently they are uncorrelated and can be used as is in model building.

[@bkrai]

Thanks!

[@ramp2011]

Awesome video. Thank you. As time permits can you do a video on use of caret package? thank you

[@bkrai]

Saw this today. Thanks for comments!

[@nahalhoghooghi8575]

Great job, same as always. Can I use PCA for 2 or more categorical variables? Can I define those variables as 0 and 1 in PCA?

[@bkrai]

You can only use numeric variables. You can try using 0 and 1 and see if it works ok.

[@siddharthabingi]

Great lecture. Thanks.

[@bkrai]

Thanks!

[@SaranathenArun11E214]

brilliant sir..simple and sweet..thanks...nice music....if i have 10 DISCRETE VARIABLEShow to reduce to 2 or 3 components, please explain?

[@bkrai]

Thanks for comments! Note that this method is only for numeric variables.

[@adityapatnaik7078]

too good!! plz make more such videos...plz!

[@bkrai]

Thanks for comments! You may find this useful too: kzbin.info/aero/PL34t5iLfZddu8M0jd7pjSVUjvjBOBdYZ1

[@GeFaaaA]

Hello very nice video!!! i have a question. Do you how i choose how many PC i have to use and which ones ???

[@bkrai]

When you have many PCs, you can select first few that capture almost all variability contained in data.

[@GeFaaaA]

@@bkrai thank you for your response! So I have to test every possible model , right? Do you know if I can use something like a criterion ?

[@bkrai]

It is good to capture over 80% of the variability.

[@PrimoSchnevi]

Hello. I dont know anything about Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation and i will never need to since thats not in my line of work. I Appreciate your Intromusic though. You are a true champ Bharatendra and enrich this world with your presence. Also that intro music fucking slaps.

[@bkrai]

Thanks for comments!

[@indranipal8131]

Do you have a video on PCA for unsupervised learning via clustering and similarity ranking?

[@bkrai]

not yet.

[@sainandankandikattu9077]

Awesome video! Could you plz add Partial least squares regression and principal components regression to your playlist! That would be of great help. Thanks in advance!

[@bkrai]

Thanks for suggestions!

[@saifsplaka]

Hi Sir,Could you take one session on SVD in R and also some theoretical explanation on it. I m finding it very difficult to understand it with most of the material available on the net.

[@sonalichakrabarty1618]

Can you please show back propagation algorithm in r

[@bkrai]

Refer to this: kzbin.info/www/bejne/Y4fWaomXmpd-f5I

[@alessandrorosati969]

can a dataset consisting of the principal components and the target variable be used to perform machine learning techniques?

[@bkrai]

Yes, this video shows an example of doing it.

[@abiani007]

Can you upload a video describing independent component analysis in R

[@bkrai]

I've added it to my list.

[@sebvangeli]

Great work! Thank you

[@md.tabibulislam9740]

Firstly thank you for your helpful video. I have problem to add ellipse in the plot. I have 30 variables, first 29 is the numeric and last one is the factor variables. But i can,t plot the ellipse in the PCA plot. How can i solve this? Please help.

[@andreafiore8373]

Thank you, this video will be really helpful to complete my thesis :)

[@bkrai]

Good luck!

[@murilocintra180]

Excellent demonstration of PCA, really helpful​. I just don't understand why in pc object, you use only training data instead of the entire data.

[@bkrai]

We only use training data so that we can later use test data to assess prediction model.

[@garykuleck1320]

Dr. Rai, Thanks for this informative video. I am having a problem getting the predict function to work with the model created on the training dataset. I am getting two errors(paraphrased): 1. NAs not allowed in subscripted assignments; 2. newdata has 1900 rows but variables found have 8100 rows. I think it is looking for the same number of rows in the test dataset. Is there something I am doing wrong? Appreciate any feedback.

[@bkrai]

NAs occur when there is missing data. For handling missing values, refer to: kzbin.info/www/bejne/d5-an4OCf5WZqck

[@jinnythomas9815]

Thanks for the video Please publish video on Exploratory Factor Analysis,Confirmatory Factor Analysis application in a model Also please explain the difference from PCA

[@bkrai]

Thanks for the suggestion, I've added this to my list.

[@manpreetkaur7716]

Add a video on non negative matrix factorization like intNMF

[@bkrai]

Thanks, I've added it to my list of future videos.

[@safezonesharing914]

Thank you for your VDO. My R version is 3.5.1 and it cannot allow ggbiplot. Do you have any package instead of ggbiplot ?

[@bkrai]

Try installing it by running this line: install_github("ggbiplot", "vqv")

[@safezonesharing914]

@@bkrai Thank you for your kindly replying When I ran it, it would shown like this. Error in install_github("ggbiplot", "vqv") : could not find function "install_github"

[@ashishsangwan5925]

@@safezonesharing914 I'm also getting the same error

[@ashishsangwan5925]

@@safezonesharing914 try below command. It worked for me library(devtools) install_github("vqv/ggbiplot")

[@alexandrec.2939]

@@ashishsangwan5925 Arf, for few seconds I believed you were my saver ^^. But nope, your alternative didn't work as well

[@samdavepollard]

Thank You - this was extremely useful. Very nice channel you have here - easy sub.

[@bkrai]

Thanks for comments!

[@joujoumilor2898]

As usual your videos are the best explained , Sir please I have a question actually my data contains both numeric and categorical variables, so what is the best method to reduce demension and what is the name of the pckage because I'm new in R .

[@dhavalpatel1843]

You can binaries your categorical variables. Try to google and learn about One-of-k coding. That way you end up with only numerical data.

[@aks1008]

Sir can I use boruta function instead of pca in r..

[@bkrai]

Yes certainly. Here is the link: kzbin.info/www/bejne/jHalkqtojLKVe6M

[@aks1008]

@@bkrai sir what do you like between r and python..i find r code more easy to understand and write..

[@bkrai]

In universities, business students usually use R and computer science students mostly use Python. If you are mainly looking to apply various machine learning and statistical methodologies, R is perfect.

[@MinhasA]

thank you for the amazing video!

[@bkrai]

Thanks for comments!

[@dejunli6417]

Hi, I want to know from where can I get the iris example data ? thank you!

[@bkrai]

It's inbuilt in R itself. You can access it by running first 3 lines shown in the video.

[@Dhrittinagpal]

Hello sir, I have been a regular follower of ur videos on R. Must appreciate the content and the ease with which you explain the concept.I have a small query. In PCA I am not able to create a biplot as I am not able to run the command - install_github("ggbiplot", "vqv"). I am getting the following message - Error in parse_repo_spec(repo) : Invalid git repo specification: 'ggbiplot'.Your help will be highly appreciated. Thanks.

[@dhavalpatel1843]

library(devtools) install_github("vqv/ggbiplot") Try this!!!!!

[@bkrai]

Thanks for the update!

[@kashgarinn]

Great video, thanks for uploading.

[@bkrai]

Thanks for comments!

[@rainbowdu509]

Hi..good day bharatendra..I want to replace one my columns with value 1 for all its elements,what is the code in R studio..thanks for your time?

[@bkrai]

suppose you are using following data: data(iris) To add what you indicated to a "new" column, you can use: iris$new

[@rainbowdu509]

thanx for ur ans ..I do already have a column with different values,I wanna replace all values on that column with just 1

[@bkrai]

So for iris data if you want to change all values for Sepal.Length variable to 1, you can use: iris$Sepal.Length

[@mamadououattara210]

Hi Dr, How to I use PCA to generate a score based on several variables? Regards

[@Jubo256]

Hello, you put training [5] to reference the column on trg variable.... shouldn't it be training[ , 5]?

[@bkrai]

It is training[ , 5] in the video.

[@jayashriraghunath3210]

Awesome explanation sir...👍👍can you make a video for independent component analysis using r in the same way sir?

[@bkrai]

Thanks, I've have added it to my list.

[@ramp2011]

Another great video. Thank you.. If your data has number of categorical columns, PCA will miss out the dependence of the target variable on these columns. Correct? In such cases what other technique can one use? Thx

[@bkrai]

PCA doesn't include target variable. At the time of developing a predictive model where we use a few principal components, we can include categorical variables. So, we don't miss out on the usefulness of any categorical variable that was excluded from PCA.

[@vishnukowndinya]

hi sir, dose it mean that, if i have x1 x2 c3 x4 (c3 is a categorical) . At 1st i have to use all the numeric (x1 2 4) variables to build pca, then adding the cat var c3 my data changes as (pc1,pc2, c3) ??? or in the begining itself i have to convert the categorical c3 into (1, 0) and then include all my input vars to form a pcs????

[@BbakMs]

Sir, I am doing PCA analysis on DJ 30 Stocks and when I view pca$loadings for 30 variables, I noticed that some were not displayed. For example, Component 1 has -0.218 for Apple but then shows none for JPM, what does this mean?

[@scholars.home999]

Sir, can you please suggest how I can perform PCA on my Panel Data? -Regards

[@ketanverma7839]

is there any other alternative package for ggbiplot ?

[@bkrai]

Try this for biplot ( I just now ran this in RStudio cloud, and it worked fine): library(devtools) install_github("fawda123/ggord") library(ggord)

[@raisulalam6051]

Thank you

[@bkrai]

Welcome!

[@sidraghayas8583]

Can you please help with combined pca and ann model?

[@bkrai]

I'm adding to the list of future videos.

[@Miichaelk]

Thanks for this video sir, Unfortunately, I have a problem with downloading the ggbiplot package, I tried the code you used in the video and I also googled, but I can not get it to work... Do you have any suggestions on how to download the package?? Thanks in advance

[@bkrai]

Try this: library(devtools) install_github("vqv/ggbiplot")

[@arsalanriaz7784]

@@bkrai thank you

[@diaraofany7053]

thank you so much for the video, this helped me a litle bit, because i still dont know how to use R to produce scatter plot for EOF QBO (Quasi Biennial Oscillation). Excuse me Sir, May you help me with the script?

[@bkrai]

For data visualization you can find this link useful: kzbin.info/aero/PL34t5iLfZddskPZVTm03hed8K93RsyP24

[@Pankajjadwal]

It was a fruitful video.Can you please share the code.

[@rashmisajwan1724]

I'm using stata, are there any specific commands for principal component analysis PCA in PANEL DATA Or Just simply run PCA after standardizing variables?

[@bkrai]

I've not used stata, so difficult to say what command will be correct.

[@seaatm]

Cool video! Can you do a video about Multiple Correspondance Analysis(MCA) for cualitative data? It would help me a lot

[@bkrai]

Thanks, I've added this to my list.

[@mohammadj.shamim9342]

Dear Respected Sir, I wanted to install ggbiplot using the command you provided with us. but it gives me another message. The message is (Installation failed: SSL certificate problem: self signed certificate in certificate chain Warning message: Username parameter is deprecated. Please use vqv/ggbiplot) I used vqv/ggbiplot as well, but no good results. please guide me what shall I do?

[@bkrai]

Not sure what went wrong. May be some typo or something else. Probably you can try running commands using my R file.