> On 13 Dec 2015, at 20:31 , William Dunlap wrote:
>
>> as Bill or Jeff explained, "the empty set is always true"
>
> My wording was that any(logical(0)) is FALSE because "there are no
> TRUEs in logical(0)".
Yes. My mind still boggles over how the empty set slipped into Boolean algebra.
Ano
This looks like homework to me and this list has a No-Homework policy. Now,
once you have done your homework (and that includes reading the documentation
of the functions you are using), and you are still confused about details, you
are welcome to ask again. Please keep the following in mind:
h
You may use the "caret" package.
At the following link 2-classes and 3-classes examples:
http://www.inside-r.org/node/86995
--
GG
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Dear Colleagues
I need someone to kindly help me solving this problem.
A sample of 89 patients was tested for 4 tumor types (T1, T2, T3, T4).
The results of the operative predicted T stage and those of the pathology
tests are tabulated in the following table:
..---
And in case you would like to explore the supervised clustering approach, I may
suggest to explore the
use of knn() fed by a training set determined by your cluster assignments
expectations.
Some "quick code" to show what I mean.
z <- as.data.frame(cbind(scale(x), scale(y)))
colnames(z) <- c("x"
> as Bill or Jeff explained, "the empty set is always true"
My wording was that any(logical(0)) is FALSE because "there are no
TRUEs in logical(0)".
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Sat, Dec 12, 2015 at 1:54 AM, Martin Maechler
wrote:
>> Henrik Bengtsson
>> on Fri,
In addition to the other fine replies, you should also know that
kmeans's results
depend on the relative scales of the data columns (since it is based
on distances
between points). Your x and y have quite different scales so the distance is
essentially determined only by the differences in the var
You could use the clValid package to run your problem through a variety of
different algorithms and evaluate cluster quality, you will learn a lot.
https://cran.r-project.org/web/views/Cluster.html gives you many more options.
All of the algorithms I am aware of have tunable parameters - but wha
It sounds to me like you don't understand cluster analysis. You should
not expect perfect "allocation" of points. I suggest that you consult
references in the man pages of your functions or on the web. You might
also find it useful to post on stats.stackexchange.com or a machine
learning help site,
Dear all,
I am trying to do some cluster analysis, both with the base R and the
apcluster. Both methods give 2 clusters, which is what I am looking
for since I am interested in identifying positive and negative
results. However I could not find a way to fine-tuning the analysis
in order to properl
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