Hi Mike and all,
thanks again. Actually your suggestion is a good way to print data in R, but
unfortunately
does not add any information to what I aready have on my table data.
What I would like to see is how far are the trials from each others in terms
of
similarity.
...maybe I just need another way to analyze the data. For this reason I ask
if someone has other suggestions, maybe coming back to the MDS?
Is there anyone who can provide an R example about how I can analyze my data
with a MDS analysis?
Luca
On Mon, Feb 14, 2011 at 6:40 PM, Mike Marchywka <marchy...@hotmail.com>wrote:
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> ________________________________
> > Date: Mon, 14 Feb 2011 17:55:50 +0100
> > Subject: Re: [R] Analyzing dissimilarity ratings with Multidimensional
> > Scaling
> > From: turchet.l...@gmail.com
> > To: marchy...@hotmail.com
> > CC: r-help@r-project.org
> >
> > Dear Mike,
> > thanks a lot for your answer. Unfortunately the way you kindly
> > suggested is not suitable
> > to solve my problem.
> >
> > Indeed as said, I need to display the distances between the proposed
> trials,
> > for example I would like to see a 2D plot where I can see how fare is the
> > trial MT-MT from the trial MT-SW (that is how far are the evaluations of
> the
> > audio-visual trial with metal-metal from the trial with metal-snow)
>
> I thought the dendrogram is what you wanted but apparently you can just
> use daisy perhaps. This had a lot of typos and may not run as last example
> I sent had bad edits but this does create scatterplot with zero points on
> diagonal etc. Presumably you can define your own functions appropriate for
> your data ( I've never used some of this before which is why I'm trying,
> cavear emptor).
>
> library("cluster")
> ddm<-daisy(matrix(xm,6,6),metric="euclidean")
> str(ddm)
> library("scatterplot3d")
> dasm<-as.matrix(ddm);
> nddf<-reshape(data.frame(dasm),varying=c("X1","X2","X3","X4","X5","X6"),
> v.name="time", direction="long")
> str(nddf)
> ij<-as.numeric(gl(6,6))
> n2<-cbind(nddf,ij)
> str(n2)
> scatterplot3d(n2$ij,n2$id,n2$time,type="h")
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> > Does anyone have any other suggestion to build such perceptual map in R?
> >
> > In addition, how can I get some information regarding the significant
> > differences
> > between the trials?
> > If I used the ANOVA would be simple to get those p-values. I wonder if
> > it is the
> > case to use it...
> >
> > Help!
> >
> >
> > Luca
> >
> >
> >
> >
> > On Mon, Feb 14, 2011 at 3:23 PM, Mike Marchywka
> > > wrote:
> >
> >
> >
> > > My first goal in the analysis process is to print a perceptual map
> where to
> > > place the pairs of
> > > audio-visual stimuli (e.g. WD-WD, MT-DL, etc.) and see how far the
> trials
> > > are from each other.
> >
> > I've been using heatmap for stuff like this.
> > You can get a nice picture this way and get quick visual
> > survey and dendrograms,
> >
> > xm<-scan(file="avm.txt")
> > str(xm)
> > heatmap(xm)
> > heatmap(matrix(xm,6,6))
> >
> > I ran the above on your data and it visually looks
> > like there could be interesting patterns to test.
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > --
> > ---------------------------------------------------------------
> >
> > "Music is a moral law:
> > It gives a soul to the Universe,
> > wings to the mind,
> > flight to the imagination,
> > a charm to sadness,
> > and life to everything.
> > It is the essence of order,
> > and leads to all that is good,
> > just and beautiful,
> > of which it is the invisible,
> > but nevertheless dazzling,
> > passionate, and eternal form".
> >
> > Plato, 400 B.C. (from the Dialogues)
> >
>
>
--
---------------------------------------------------------------
"Music is a moral law:
It gives a soul to the Universe,
wings to the mind,
flight to the imagination,
a charm to sadness,
and life to everything.
It is the essence of order,
and leads to all that is good,
just and beautiful,
of which it is the invisible,
but nevertheless dazzling,
passionate, and eternal form".
Plato, 400 B.C. (from the Dialogues)
[[alternative HTML version deleted]]
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