Message-ID: <9e961572-f0dc-713a-351d-b66035c8946d@dewey.myzen.co.uk>
Date: 2017-01-26T13:39:42Z
From: Michael Dewey
Subject: Categorize subjects using PCA
In-Reply-To: <CACR65sEcsjb21w3KPcRuwHKPY2pX74u9gXuwjCOGJAGbhRJCPw@mail.gmail.com>
If you want to discover groupings in your data-set I would have thought
latent class analysis was a more principled solution. There are several
packages for doing this.
If you want more detail about this you might be better asking on
stats.stackexchange.com for statistical help. When you do be sure to
define what you mean by 'do not have a measure' as it is not, to me at
least, clear what s/he means.
On 26/01/2017 13:20, Dr.Vinay Pitchika wrote:
> Hello everyone,
>
> I have been analyzing dietary pattern data obtained via FFQ. We performed
> Principal Component Analysis and decided to extract 3 dimensions (healthy,
> unhealthy and mixed diet) and to adjust this in linear regression model
> along with various other variables. However, our statistician was not happy
> with the approach because the 3 dimensions we adjust do not have a measure.
>
> So, I would like to classify the patients in our dataset using the PCA
> results. Is it possible to classify all the patients into one of the 3
> groups mentioned above?
>
> Thank you
> Vinay
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Michael
http://www.dewey.myzen.co.uk/home.html