hi there!
i am searching for a possibility to plot the cluster fusion number against the
distance in hierarchical clustering. i used the following code:
distances = dist(my data[, c(1:11)],
method="euclidean")
eward = hclust(distances, method="ward")
plot(eward, labels=my data[, 12], hang=0,cex
Hey there!
I am searching for an attribute evaluation algorithm (such as based on Info
gain, Gain ratio or Chi squared statistics).
Is something like that available in R?
Thanks for your reply.
Best regards,
Julia
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R-help@r-project.org mailing
richt
> Datum: Tue, 30 Oct 2007 17:03:49 -0400
> Von: "Kuhn, Max" <[EMAIL PROTECTED]>
> An: "Julia Kröpfl" <[EMAIL PROTECTED]>, r-help@r-project.org
> Betreff: RE: [R] NAIVE BAYES with 10-fold cross validation
> > am trying to implement the code of
hi there!!
i am trying to implement the code of the e1071 package for naive bayes, but it
doens't really work, any ideas??
i am very glad about any help!!
i need a naive bayes with 10-fold cross validation:
code:
library(e1071)
model <- naiveBayes(code ~ ., mydata)
tune.control <- tune.control
lumns than rows) and therefore get
an error message.
Any ideas what to do?
Thx for your help,
I really appreciate it!
Julia
Original-Nachricht
> Datum: Fri, 12 Oct 2007 23:38:01 +0300
> Von: "Kenn Konstabel" <[EMAIL PROTECTED]>
> An: "Julia Kr
Hallo!
Is there a package in R that does Q-type factor analysis?
I know how to do principal component analysis, but haven't found any
application of Q-type factor analysis.
Thx,
Julia
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Pt! Schon vom neuen GMX MultiMessenger gehört?
Der kanns mit allen: http://www.gmx.net/de/go/multimesse
Hey there!
I would like to justify the stability of the cluster of a subset of my data by
comparing it to another cluster of another subset. Does there exist a
quantitative similarity measure that can be applied?
I am open for any suggestions,
thx for your help,
Julia
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Hey there!
I got a pseudo code and don't know how to apply it to R, maybe someone can help
me:
Input: A dataset X, kmax: maximum number of clusters, num_subsamples: number of
subsamples.
Output: S(i; k) - a distribution of similarities between partitions into k
clusters of a reference
clusteri
Hallo!
I applied kmeans to my data:
kcluster= kmeans((mydata, 4, iter.max=10)
table(code, kcluster$cluster)
If I run this code again, I get a different result as with the first trial (I
understand that this is correct, since kmeans starts randomly with assigning
the clusters and therefore the
Hallo!
I would need a code for 10-fold cross validation for the classifiers Naive
Bayes and svm (e1071) package. Has there already been done something like that?
I tried to do it myself by applying the tune function first:
library(e1071)
tune.control <- tune.control(random =F, nrepeat=1,
repea
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