[R] Discriminant Correspondence Analysis

Peter Ehlers ehlers at ucalgary.ca
Wed Dec 15 09:20:25 CET 2010

See inline.

On 2010-12-14 23:48, Wayne Sawtell wrote:
> Thank you all for the advice.
> I have looked through the Introduction to R pdf and got some pointers
> but when I try to implement them it does not work. If someone could
> clarify a couple of basic things, I would appreciate it.
> When I successfully read in my file, the prompt changed from > to +.
> Then when I typed in the suggested commands, nothing happened.
See page 5 of 'An Introduction to R'. I don't want to sound too
pedantic but I strongly recommend that you (at least) work the
whole of the intro session in Appendix A.

> For the discrimin.coa command, the only part I don't understand is what
> to put for "fac". Is this the grouping variable that I obtained from my
Discriminant analysis works with 'classes' (as you did quote in
your original mail). What do you consider to be your classes?

> Principal Co-ordinates Analysis? My goal, by the way, is to test whether
> the groups into which PCoA put my data are valid. The data consist of
> specimen measurements and categorical observations. So I have a
> rectangular table of data with headings (names of measured characters)
> at the top of each column of numbers. This is a sample:
> X1       X2      X3       X4   X5
> 0.123  0.854  0.319  1     2
> 0.562  0.472  0.917  0     1
> 0.381  0.285  0.146  2     1
> where X4 is a body shape character, which I've converted to numbers,
This is almost always a bad (although usually harmless) idea. Why
not use the words?

> instead of words (0 - round, 1 - oblong, 2 - rectangular). I've included
> X5, which is just the column in which I entered the group number into
> which PCoA grouped the data points or rows (each row represents a
> different specimen that was measured according to the characters in the
> headings). So, should I put "fac = X5"? Is that how Discriminant
> Correspondence Analysis works?
Perhaps it's time to find a local statistical consultant?

Peter Ehlers

> thanks again and sorry if my question is too long
> Wayne
> On 14 December 2010 18:39, Peter Ehlers <ehlers at ucalgary.ca
> <mailto:ehlers at ucalgary.ca>> wrote:
>     Wayne,
>     So far, no one has said the obvious:
>     Please do work your way through (or at least
>     skim) "An Introduction to R" which you'll
>     find right there on your computer under
>     Help/Manuals. Your questions indicate that
>     you have not yet done so. Do it, it really
>     will pay off.
>     Peter Ehlers
>     On 2010-12-14 12:36, Wayne Sawtell wrote:
>         Hello everyone,
>         I am totally new to the R program. I have had a look at some pdf
>         documents
>         that I downloaded and that explain how to do many things in R;
>         however, I
>         still cannot figure out how to do what I want to do, which is to
>         perform
>         Discriminant Correspondence Analysis on a rectangular matrix of
>         data that I
>         have in an Excel file. I know R users frown upon Excel and recommend
>         converting Excel files to .csv format, which I have done, no
>         problem. That
>         is not an issue.
>         There are several parts to my problem.
>         1) When I try the read.table command, even if I include the
>         directory name
>         in the filename, R still cannot read the file, even if it is in
>         .csv format
>         2) I was able to copy my file and then read the clipboard
>         contents into R
>         but then I do not know to assign a name to the data frame in
>         order to
>         conduct any operations on it
>         3) I need the ADE4 program in order to perform Discriminant
>         Correspondence
>         Analysis, so I used the "install.packages" command to install it. It
>         installed no problem but I do not know how to access the ADE4
>         program in R.
>         I am unable to open it directly, either.
>         4) I thought that using the ADE4 GUI (called "ade4TkGUI") would
>         be easier
>         because I do not know many of the R commands; but, again, I
>         downloaded it
>         but cannot open or access it.
>         The following is the suggested coding that I found through the R
>         website,
>         but when I try to use this code, I don't know how to assign a
>         name for the
>         df, or what to put for "fac", and what is worse, I get an error
>         message
>         saying that the program cannot find the "discrimin.coa" command.
>         Usage
>         discrimin.coa(df, fac, scannf = TRUE, nf = 2)
>         Arguments
>         df a data frame containing positive or null values
>         fac a factor defining the classes of discriminant analysis
>         scannf a logical value indicating whether the eigenvalues bar
>         plot should be
>         displayed
>         nf if scannf FALSE, an integer indicating the number of kept axes
>         Examples
>         data(perthi02)
>         plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))
>         For clarification, my data consists of measurements of morphological
>         characters of an assemblage of biological specimens. I have already
>         performed Principal Co-ordinates Analysis, Principal Compionents
>         Analysis
>         and Cluster Analysis in another program (PAST) in order to see
>         if the data
>         fall into distinct groupings that might represent different
>         morphological
>         species. I now want to test the groupings that I found on my
>         test data set
>         using Discriminant Correspondence Analysis.There are both
>         continuous and
>         categorical characters, which is the reason why I need to perform
>         Discriminant Correspondence Analysis, instead of Linear Discriminant
>         Analysis, which is only valid for continuous measurements. R
>         seems to be the
>         only program in which I can perform Discriminant Correspondence
>         Analysis.
>         Thanks for any help offered on any of these points.
>         Wayne
>                 [[alternative HTML version deleted]]
>         ______________________________________________
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>         http://www.R-project.org/posting-guide.html
>         and provide commented, minimal, self-contained, reproducible code.

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