Federico,
you might also want to have a look at packages flexclust or flexmix,
so you can take into account that you have binary data. The mclust
package can be used to estimate mixtures of Gaussian distributions.
flexclust implements kmeans-like algorithms, but you can specify a
distance
Hi All,
I have a n x m matrix. The n rows are individuals, the m columns are variables.
The matrix is in itself a collection of 1s (if a variable is observed for an
individual), and 0s (is there is no observation).
Something like:
[,1] [,2] [,3] [,4] [,5] [,6]
[1,]1011
@stat.math.ethz.ch
Sent: Wednesday, July 18, 2007 3:37 PM
Subject: [R] EM unsupervised clustering
Hi All,
I have a n x m matrix. The n rows are individuals, the m columns
are variables.
The matrix is in itself a collection of 1s (if a variable is
observed for an
individual), and 0s
Dimitris Rizopoulos wrote:
you could also have a look at function lca() from package `e1071' that
performs a latent class analysis, e.g.,
fit1 - lca(data, 2)
I tried but I got:
lca(data, 2)
Error in matrix(0, 2^nvar, nvar) : matrix: invalid 'nrow' value (too large or
NA)
In addition:
