PCA to separate two outcomesHi,
I have a binary outcome for a set of patients, and a long list of variables measured (i.e. potential predictors/characteristics). I can do a PCA on the whole set, or PCA on each set separately (i.e. positive patients and negative patients separately). But what I really want is the following: what are the two principal components that best separate the positives from the negatives? I've played a bit with the discriminant analysis but haven't got too far, any help or suggestions would be welcome.
Thanks!
uriel
Advanced Statistical ModelingRe: PCA to separate two outcomesHi,
Have you considered the Discriminant Analysis platform, or the Response Screening platform if you need to filter / focus which variables are the most relevant to your classification.
Best,
TSRe: PCA to separate two outcomesHi TS and DS,
I am not looking for predictors with the PCA. I want to characterize, describe. I think the discriminant analysis should do it under the wide linear method as it shows principal components, but can't find the details of the principal components it generates to assess if that really is the case.
Uriel.
Re: PCA to separate two outcomesHi @utkcito ,
I agree with @ih , the PLS platform might be better suited to what you're wanting to do, or the SVM platform too.
One reason why the discriminant analysis might not be working is that it sounds like your data set might not entirely fit the expected format for the DA. The DA requires categorical X's and continuous responses Y's, and it is kind of the inverse, using the continuous Y's to ultimately predict the X's, i.e. it predicts a classification variable based on a known continuous Y variable.
If you want to use the X's to characterize/describe the outcome Y's, then PLS, partitioning, or SVM might be better platforms, I think. And as @ih mentioned, using the VIP plots, you can use that along with the VIP threshold to generate a simplified model where it's not using every single X column in the model. Clustering could also be an option, but it might not make as much sense as the PLS when trying to interpret the results.
Hope you can use one of those platforms to help your work.
Good luck!,
DS
Re: PCA to separate two outcomesHello @utkcito,
While I fully agree with the others on potential options for analyzing your data I would like to offer up another JMP Pro platform that will be well worth your time to try out. Generalized Regression (GR) is extremely well suited for binomial/binary outcomes. The LASSO and Elastic Net algorithms will allow you to find the best overall combination of variables in the factor space you are working and not in a principal component or latent factor(PLS) space. Both PCA and PLS models can be difficult to determine what is most important without a lot of extra effort. You also have many options for cross-validation to avoid overfitting.
Neural Nets (NN) might be useful to you. The TanH, Hyperbolic Tangen function is very well suited for binomial outcomes and you will be automatically directed to use cross-validation to avoid overfitting. (JMP and JMP Pro)
Bootstrap Forest as a partitioning method will also allow you to find the most significant factors in your factor space. (JMP Pro)
One last thought, if you have access to JMP 16 EA you can try Model Screening under Predictive Modeling to help guide you to the best overall model for your data.
Model Screening will be one of the great new features in JMP Pro 16
Re: PCA to separate two outcomesThanks to all that responded, your comments are helpful - I've in fact used all these options in the recent past to analyze the data. I was interested particularly in how an orthogonal discrimination would look like, but if not directly possible I'll move on.
Uriel.
