I have not used 'sna'.
Have you tried using "debug" to walk through the code line by
line, examining and even changing things at will?
For example, how big is "rho", passed as starting values to
"optim"? If that matches the size of your adjacency matrix, it could
expose a theoretical problem. Similarly, how many observations do you
have? For example, if you have 2,000 observations, that might help
explain why you get an answer with adjacency matrices smaller than that
but not with larger matrices.
Hope this helps.
Spencer Graves
p.s. Are you aware that you can get the source code for any CRAN
package? For example, the source for the "sna" package is available in
a file "sna_2.0-1.tar.gz" downloadable from
"http://cran.fhcrc.org/web/packages/sna/index.html";. If the people who
wrote a particular functions included comments in their code, they will
appear in the *.tar.gz file but not in the version you get by typing the
function name.
Brandy Lee Aven wrote:
I am trying to use the lnam autocorrelation model from the SNA package. I have it running for smaller adjacency matrices (<1,500) it works just fine but when my matrices are bigger 4000+. I get the error:
lnam1_01.adj<- lnam(data01$adopt,x01,ec2001.csr)
Error in optim(rho, n2ll.rho, method = method, control = control, beta = parm$beta, :
initial value in 'vmmin' is not finite
I have looked at the lnam code and cant even figure out what vmmin is.
Is there anyway around this? Am I doing something wrong? What makes me think that its about the size of the adjacency matrix is that I can run the same command on similar objects that are just smaller and it works fine.
please help!
sessionInfo()
R version 2.9.1 (2009-06-26)
x86_64-pc-linux-gnu
locale:
C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] numDeriv_2009.2-1 sna_2.0-1
loaded via a namespace (and not attached):
[1] tools_2.9.1
class(data01$adopt) #This is the response vector y
[1] "integer"
data01$adopt[1:10] # Its just a binary outcome for all vertices
[1] 0 0 0 0 0 0 0 0 0 0 ......until 4,003
class(x01) #X01 is a matrix of my six covariates for all vertices
[1] "matrix"
#here is the an example of the data
x01[1:10,1:6]
on01 indegree outdegree between eigen numalters01
1 1 0 0 0 1 1
19 1 0 1 0 0 1
123 1 0 1 0 0 1
140 1 0 1 0 0 1
169 1 0 1 0 0 1
189 1 0 1 0 0 1
195 1 0 1 0 0 2
204 1 0 1 0 0 1
231 1 0 2 0 0 1
252 1 0 3 0 0 4
# this is the adjacency matrix (in Sparse matrix format) that causes the error.
I have another that is 10,500 and does the same thing.
dim(ec2001.csr)
[1] 4003 4003
class(ec2001.csr)
[1] "matrix.csr"
attr(,"package")
[1] "SparseM"
ec2001.csr[1:10,1:10] #here is what it looks like
1 19 123 140 169 189 195 204 231 252
1 1 0 0 0 0 0 0 0 0 0
19 0 0 0 1 0 0 0 0 0 0
123 0 0 0 0 0 0 0 0 0 0
140 0 0 0 0 0 0 0 0 0 0
169 0 0 0 0 0 0 0 0 0 0
189 0 0 1 0 0 0 0 0 0 0
195 0 0 0 0 0 0 0 0 0 0
204 0 0 0 0 0 0 0 0 0 0
231 0 0 0 0 0 0 0 0 0 0
252 0 0 0 0 0 0 0 0 0 0
#There are also no infinite values in the objects.
is.infinite(x01)
[1] FALSE ..... N
is.infinite(data01$adopt)
[1] FALSE .....N
is.infinite(ec2001.csr)
[1] FALSE
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