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Subject: RE: [R] Principle Component Analysis: Ranking Animal Size Based On
Combined Metrics
The first principal component should be your estimate of "size" since it
captures the correlations between all 4 variables. The second principle
component must be orthogonal to the firs
102 0.9984574 1.000
David C
-Original Message-
From: Sidoti, Salvatore A. [mailto:sidoti...@buckeyemail.osu.edu]
Sent: Monday, November 14, 2016 11:41 AM
To: David L Carlson; Jim Lemon; r-help mailing list
Subject: RE: [R] Principle Component Analysis: Ranking Animal Size Bas
ersity
College Station, TX 77840-4352
-Original Message-
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Sidoti,
Salvatore A.
Sent: Sunday, November 13, 2016 7:38 PM
To: Jim Lemon; r-help mailing list
Subject: Re: [R] Principle Component Analysis: Ranking Animal Size Based
-Original Message-
From: Jim Lemon [mailto:drjimle...@gmail.com]
Sent: Sunday, November 13, 2016 3:53 PM
To: Sidoti, Salvatore A. ; r-help mailing list
Subject: Re: [R] Principle Component Analysis: Ranking Animal Size Based On
Combined Metrics
Hi Salvatore,
If by "size" yo
Salvatore,
I won't comment on whether to use log weight "to increase the
correlation" -- that depends on whether that makes sense, and whether
the relationships with other variables is more nearly linear.
Try this with your pca of the correlation matrix:
biplot(pca_morpho)
You'll see that
; Then divide by the sum of the weights:
> 0.43758 / 1.697 = 0.257855 = "animal size"
>
> This value can then be used to rank the animal according to its size for
> further analysis...
>
> Does this sound like a reasonable application of my PCA data?
>
> Salvatore A
Hi Salvatore,
If by "size" you mean volume, why not directly measure the volume of
your animals? They appear to be fairly small. Sometimes working out
what the critical value actually means can inform the way to measure
it.
Jim
On Sun, Nov 13, 2016 at 4:46 PM, Sidoti, Salvatore A.
wrote:
> Let'
While you may get a reply here, this list is about R programming, not
about statistics. So
1. Do your homework and read a tutorial on PCA on the web or
elsewhere. Isn't this what a PhD student is supposed to do?
2. Post on a statistics list like stats.stackexchange.com.
3. Consult your professor
phthao05 wrote:
> Dear All,
> In a package, I want to use PCA function. The structure I used follow this
> page: http://www.statmethods.net/advstats/factor.html.
>fit<-principle(mydata, nfactors=9, rotation=TRUE)
>or:
>result<-PCA(mydata)
>
> But I don't known why R languag
On 3/5/08, phthao05 <[EMAIL PROTECTED]> wrote:
> 1) I don't know why PCA rotation function not run although I try many times.
> Would you please hepl me and explain how to read the PCA map (both of
> rotated and unrotated) in a concrete example.
If you used the example from here [1], there's a
phthao05 wrote:
> Dear All,
> In a package, I want to use PCA function. The structure I used follow this
> page: http://www.statmethods.net/advstats/factor.html.
>fit<-principle(mydata, nfactors=9, rotation=TRUE)
>or:
>result<-PCA(mydata)
>
> But I don't known why R languag
phthao05 gmail.com> writes:
[snip]
> Because I have just learn R language in a few day so I have many problem.
> 1) I don't know why PCA rotation function not run although I try many times.
> Would you please hepl me and explain how to read the PCA map (both of
> rotated and unrotated) in a c
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