I am using sas 9.3v.
proc princomp data=plants plots=all;
In the output, I get score plots as "Plots of component scores" and loading plots as "Plots of component pattern".
I want to overlay 2 by 1 score plot on 2 by 1 loading plot to better visualize my results.
Also, in loading plots I would like to color my variables differently.
For example: X1-X75 as green, X76-X150 as blue, and X151-X244 as red.
Can anyone provide me the code to perform these functions?
Since prinqual typically iterates, it tries to get rid of potentially problematic variables. You can specify a really small singularity criterion as in this example.
data x; do i = 1 to 10; x1 = normal(7); x2 = normal(7); x3 = normal(7); x4 = normal(7); x5 = normal(7) * 1e-10; output; end; run; proc prinqual data=x std; var ide(x:); run; proc prinqual data=x std singular=1e-50; var ide(x:); run;
You can always output anything produced by any procedure and rearrange it in any way to make a graph. Do an ODS OUTPUT on the data object that underlies prinqual's graph. Then use princomp pieces to make something like that. Again be aware that prinqual scales things in a nice way. The documentation has details.
The easiest way to display scores and loadings simultaneously is to use proc prinqual.
If someone has not showed you how to change the colors for some variables before then, I will help you with it tomorrow. I'll probably recreate the graph outside the proc with a modified data object that has a group variable that determines the colors. Some of the techniques I use are in https://support.sas.com/documentation/prod-p/grstat/9.4/en/PDF/odsadvg.pdf
This answer has undergone several edits. Sorry for the churn. Replaced earlier answer around 4:00 EDT on Nov 1. This is probably the most technical answer that I ever posted in SAS Communities, and I learned a few things in the process. Ask questions if you are confused and I will try to help. Please understand that you don't have to understand everything that I do to use it.
If you have never seen code like this, it is going to be hard to digest. I examined the graph template and modify it. I also add a new ID variable. For you, it will contain groups (like 1s for all the variables that you want to be the first color), 2s for all the variables you want to be the second color, and so on. You can change the colors, but I did not get into that. http://go.documentation.sas.com/?docsetId=grstatgraph&docsetTarget=n0mx6gudpcurt7n1ubv4fvbhj4fc.htm&...
@Rick_SAS recently did a blog on modifying the template. https://blogs.sas.com/content/iml/2017/10/30/programming-modify-ods-templates.html Take a look if you are confused by what I did. For more advanced examples, see https://support.sas.com/resources/papers/proceedings16/SAS1800-2016.pdf .
Note that PRINQUAL does not make a "true" biplot. It scales the vector lengths in a nicer way. You now have all the tools you need to do other customizations.
title 'Ratings for Automobiles Manufactured in 1980'; data cars; input Origin $ 1-8 Make $ 10-19 Model $ 21-36 (MPG Reliability Acceleration Braking Handling Ride Visibility Comfort Quiet Cargo) (1.); datalines; GMC Buick Century 3334444544 GMC Buick Electra 2434453555 GMC Buick Lesabre 2354353545 GMC Buick Regal 3244443424 GMC Buick Riviera 2354553543 GMC Buick Skyhawk 3232423224 GMC Buick Skylark 4145555422 GMC Chevrolet Camaro 2254541241 GMC Chevrolet Caprice Classic 2445353555 GMC Chevrolet Chevette 5335425223 GMC Chevrolet Citation 4155555525 GMC Chevrolet Corvette 2153542242 GMC Chevrolet Malibu 3333444544 GMC Chevrolet Monte Carlo 3253353544 GMC Chevrolet Monza 2142233114 Chrysler Dodge Aspen 2143333424 Chrysler Dodge Colt Hatchback 5544445434 Chrysler Dodge Diplomat 2153343434 Chrysler Dodge Mirada 2143432434 Chrysler Dodge Omni 024 4345535225 Chrysler Dodge St Regis 1154353545 Ford Ford Fairmont 3324345434 Ford Ford Fiesta 5445344414 Ford Ford Granada 2233233233 Ford Ford LTD 3354354555 Ford Ford Mustang 3244323222 Ford Ford Pinto 4134313222 Ford Ford Thunderbird 2354344444 Ford Mercury Bobcat 4134313212 Ford Mercury Capri 3154322222 Ford Mercury Cougar XR7 2454444444 Ford Mercury Marquis 3354354555 Ford Mercury Monarch 2353232232 Ford Mercury Zephyr 3124345434 GMC Oldsmobile Cutlass 3443444544 GMC Oldsmobile Delta 88 2435353555 GMC Oldsmobile 98 2445353555 GMC Oldsmobile Omega 4155555522 GMC Oldsmobile Starfire 2133522154 GMC Oldsmobile Toronado 3323443544 Chrysler Plymouth Champ 5544445434 Chrysler Plymouth Gran Fury 2134353535 Chrysler Plymouth Horizon 4345535235 Chrysler Plymouth Volare 2153333424 GMC Pontiac Bonneville 2345353555 GMC Pontiac Firebird 1153551231 GMC Pontiac Grand Prix 3224432434 GMC Pontiac Lemans 3333444544 GMC Pontiac Phoenix 4155554415 GMC Pontiac Sunbird 3134533234 ; * Ensure we are not using the modified template (if we run this more than once). OK if you get a warning the first time that it does not exist.; proc template; delete Stat.Prinqual.Graphics.MDPref / store=work.modtemp; quit; ods graphics on; ods listing close; ods html body='b.html'; * Default analysis; proc prinqual data=cars mdpref; ods output mdprefplot=m; transform ide(mpg -- cargo); id model; run; * Default data object; proc print data=m; run; * Store template in a file. Look at it.; proc template; source Stat.Prinqual.Graphics.MDPref / file='tpl.tpl' store=sashelp.tmplstat; quit; * Store modified template in WORK so it disappears when SAS closes; ods path (prepend) work.modtemp(update); ods path show; * Modify template. You cannot write code like this in a vacuum. You must look at the original template.; data _null_; infile 'tpl.tpl' end=eof; input; * Add proc call; if _n_ = 1 then call execute('proc template;'); * Remove Store option; i = index(lowcase(_infile_), '/ store = '); if i then substr(_infile_, i) = ';'; * Skip using the group variable; _infile_ = tranwrd(_infile_, '_id2=IDLAB2', ' '); _infile_ = tranwrd(_infile_, ' _id2 ', ' '); * Find start of vectorplot; v + index(lowcase(_infile_), 'vectorplot'); if v then do; * Add group var; _infile_ = tranwrd(_infile_, '/', '/ group=idlab2'); * remove current label attributes; _infile_ = tranwrd(_infile_, 'datalabelattrs', 'primary=true; *'); * flag end of vectorplot statement; if index(_infile_, ';') then v = 0; end; * write out line; call execute(_infile_); * end; if eof then call execute('run;'); run; proc template; source Stat.Prinqual.Graphics.MDPref; quit; * Map the variable names to group numbers. You will want some simpler logic based on groups of variable names. You won't even need to make an informat.; data cntlin; length start $ 24; Type = 'i'; FmtName = "CarFmt"; input start $ label; datalines; MPG 1 Acceleration 1 Reliability 2 Braking 3 Handling 3 Ride 3 Visibility 4 Comfort 4 Quiet 4 Cargo 5 ; proc format cntlin=cntlin; quit; * Add a new group variable. Make it reflect the color groups that you want. It will become idlab2 in the data object; data cars2; set cars; array __x[*] _numeric_; * Here is where you will substitute your logic for making the group names.; if _n_ le 10 then group = input(vname(__x[_n_]), carfmt.); run; * Need the ODS Document to access the dynamics.; ods document name=MyDoc (write); * Add group as an ID variable. User the modified data set and template. We will ignore this graph, but it will provide the pieces that we need for the real graph.; proc prinqual data=cars2 mdpref; ods output mdprefplot=m; transform ide(mpg -- cargo); id model group; run; * The warning is because of my two-data object trick. ODS Graphics does not accept variables from different data objects (although in some ways it appears that it does). This is a bit complicated, so let's leave it at that. I need one more major step, then I can create the graph outside PRINQUAL. ods document close; * Need to see the path for the graph.; proc document name=MyDoc; list / levels=all; quit; * Display and store the dynamics; proc document name=MyDoc; ods output dynamics=dynamics; obdynam \Prinqual#1\MDPREF#1\MDPrefPlot#1; quit; * Examine the new data object just to better see what is going on; proc print data=m; run; * This calls SGRENDER using the modified template and populates the DYNAMIC statement with all of the dynamic name/value pairs.; data _null_; set dynamics(where=(label1 ne '___NOBS___')) end=eof; if _n_ = 1 then do; call execute('proc sgrender data=m ' || 'template=Stat.Prinqual.Graphics.MDPref;'); call execute('dynamic'); end; if cvalue1 ne ' ' then call execute(catx(' ', label1, '=', ifc(n(nvalue1), cvalue1, quote(trim(cvalue1))))); if eof then call execute('; run;'); run; * Unfortunately for what you want, your problem requires *all* of the steps for making highly customized graphs. I initially thought I could get by with just a template modification. 1) Modify the template. 2) Output the data object. I modified it by using an ID variable before hand, (just because that was how I started approaching this) but I could have modified it after the fact. 3) Access the dynamic variables. 4) Use CALL EXECUTE to make SGRENDER code that uses the data object, modified template, and dynamic variables. ODS Graphics gives you *incredible* *flexibilty* through programming, but everything you might want is not available by flipping an option. And yes, there is a long learning curve for some of this.; ods html close; ods listing;
You are welcome. Yes, there is a long learning curve associated with what I did. Again, start with @Rick_SAS's blog if you want to understand the process. The main thing you need to customize is the creation of the _group variable and the variables and data set for PROC PRINQUAL. If you have questions, post them.
PS. This question inspired my next blog in Graphically Speaking. I am still working on it.
If the concept is "completely new", you might want to start by copying the template into a text editor, making the changes that Warren recommends, and then running PROC TEMPLATE to save the new template. Some people find that process conceptually easier to understand because they can see and understand the template edits.
Sorry. Hold off on this. While what I did apparently worked--it affected the graph in the way I had in mind--that WARNING is a signal that what I did will not generalize to your exact problem. I will repost when I have this all resolved.
I feel compelled to point out that biplots were developed in the days when "big data" meant thousands of observations and dozens of variables. They don't scale well to hundreds of variables and tens of thousands of observations. For example, have you considered how you expect to visualize 244 vectors on one graph? Almost surely the labels will be impossible to read when the vectors are projected onto the first two principal components. Similarly, the observations are likely to suffer from overplotting, although using transparency can alleviate this somewhat.
It did occur to me as to how to visualize vectors with the graph. Thanks for suggesting transparency.
One more question is : When I am running the proc princomp, the explained variance for component 1: 34.66% and Component 2: 23.52% .
But when I am running proc prinqual, the explained variance for component 1: 38.35% and Component 2: 25.88%.
I am using 'transform ide' for proc prinqual as suggested by Warrenkuhfeld.
Why is there a difference in explained variance between princomp and prinqual.
I am trying to work on one problem at a time. That is why I didn't post these questions earlier.
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