Dear Michael,


You haven't told us what your data is, and we can only surmise -- not very
helpful for you and annoying for those who try to help.

Apologies, I am brand new to R and this mailing list. Will try to be more
concise.

Here is my data a NEW verion of my data:

  Curvature Diameter   Quality
1      2.95     6.63    Passed
2      2.53     7.79    Passed
3      3.57     5.65    Passed
4      3.16     5.47    Passed
5      2.58     4.46 NotPassed
6      2.16     6.22 NotPassed
7      3.27     3.52 NotPassed

What I am trying to get from the candisc method is a 1 dimensional
scatterplot that separates my two groups Passed and NotPassed

On this data I do a "do.mod <- lm(cbind(Diameter, Curvature) ~ Quality,
data=do)"

>do.mod produces

Coefficients:
               Diameter  Curvature
(Intercept)    4.7333    2.6700
QualityPassed  1.6517    0.3825

I then run the "candisc" method: "do.can <- candisc(do.mod, data=do)"

this produces:

Canonical Discriminant Analysis for Quality:

   CanRsq Eigenvalue Difference Percent Cumulative
1 0.91354     10.566                100        100

Test of H0: The canonical correlations in the
current row and all that follow are zero

  LR test stat approx F num Df den Df   Pr(> F)
1        0.086   52.831      1      5 0.0007706 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

What "I think" I would like to plot is the discriminant function of each
sample 1-7.

Here is an example of what I am trying to do with candisc.

http://people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html

Thanks










On Thu, Dec 11, 2008 at 3:36 PM, Michael Friendly <friendly@yorku.ca> wrote:

> Dear Pete,
>
> You haven't told us what your data is, and we can only surmise -- not very
> helpful for you and annoying for those who try to help.
>
> Pete Shepard wrote:
>
>> Hello,
>>
>> I have a file with two dependent variables (three and five) and one
>> independent variable. I do  i.mod <- lm(cbind(three, five) ~ species,
>> data=i.txt) and get the following output:
>>
>>
>> Coefficients:
>>             three   five
>> (Intercept)   9.949   9.586
>> species      -1.166  -1.156
>>
> From this, it seems that species is numeric variable, not a factor.
> If so, canonical discriminant analysis in not appropriate, so
> all following bets are off.
>
> That's likely why you end up with only one canonical dimension.
>
>
>  I do a" i.can<-candisc(i.mod,data=i):
>>
> Is data=i the same as data=i.txt?
>
>>
>> and get the following output:
>>
>> Canonical Discriminant Analysis for species:
>>
>>    CanRsq Eigenvalue Difference Percent Cumulative
>> 1 0.096506    0.10681                100        100
>>
>> Test of H0: The canonical correlations in the
>> current row and all that follow are zero
>> LR test stat approx F num Df den Df   Pr(> F)
>> 1        0.903   63.875      1    598 6.859e-15 ***
>> ---
>> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>>
>> this is different than the output I get with SAS:
>>
> What was your SAS code? Was the data the same?
>
>>
>>         Eigenvalue Difference Proportion Cumulative      Ratio     F Value
>> Num DF Den DF Pr > F
>>
>>       1     0.1068                1.0000     1.0000 0.90349416
>> 31.88      2    597 <.0001
>>
>
>
>
>> I am also wondering how to plot the can1*can1 like it is done in SAS.
>>
>> proc plot;
>>    plot can1*can1=species;
>>    format species spechar.;
>>    title2 'Plot of Constits_vs_cassettes';
>>  run;
>>
>>  If you want to compare plots for canonical analysis in SAS and R,
> see my macros, canplot and hecan at
> http://www.math.yorku.ca/SCS/sasmac/
>
> But in general, if all you have is 1 canonical dimension, a dotplot or
> boxplot of the canonical scores would be more useful than a scatterplot
> plot of can1 * can1.
>
> The plot method for candisc objects in the candisc package has some
> code to handle the 1 can-D case.
>
> hope this helps
> -Michael
>
>> Thanks
>>
>>        [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> 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 Friendly     Email: friendly AT yorku DOT ca
> Professor, Psychology Dept.
> York University      Voice: 416 736-5115 x66249 Fax: 416 736-5814
> 4700 Keele Street    http://www.math.yorku.ca/SCS/friendly.html
> Toronto, ONT  M3J 1P3 CANADA
>
>

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