I got a problem when plot the vectors in the .anf file.
When I work with small number of nodes, it works normally and I can plot the vectors (simulation time might arrive until 1000s).
But when the number of nodes is big (for example 50 nodes), I can not plot the vectors specially when simulation time is big ( more than 250s, i can't plot) and It always alerts:
"Evaluating dataset... has encountered a problem. An error occurred
during the dataset processing.
I use omnet++ 4.2
Please help me,
best regards
Related
I am new to R and Network theory. I would like to calculate the cluster correlation to investigate the structural equivalence of my data. I have been looking for a solution of the problem for quite some time now, also in this forum. I could find relevant contributions, but could not find out a solution for my problem.
The first note I should make is that I stacked directed network matrices on top of each other (one transposed and one not transposed for the directed network data). Following the structure http://www.imsbio.co.jp/RGM/R_rdfile?f=NetCluster/man/clusterCorr.Rd&d=R_CC
when I calculate
clustered_observed_cors <- clustConfigurations(num_vertices, advice_m1_money_m1_kinship_m1_hclust, advice_m1_money_m1_kinship_m1_cors)
the program stops and I cannot compute anything anymore until I hit the red stop button in the bottom left window of R Studio. That's my problem. does anyone have an idea what could be the reason for this being the case?
The error I get is
In cor(as.vector(d[g1[i], , ]), as.vector(d[g2[j], , ]), use = "complete.obs") :
the standard deviation is zero
Any help appreciated,
Simon
i have a problem with clustering time series in R.
I googled a lot and found nothing that fits my problem.
I have made a STL-Decomposition of Timeseries.
The trend component is in a matrix with 64 columns, one for every series.
Now i want to cluster these series in simular groups, involve the curve shapes and the timely shift. I found some functions that imply one of these aspects but not both.
First i tried to calculte a distance matrix with the dtw-distance so i
found clusters based on the values and inply the time shift but not on the shape of the timeseries. After this i tried some correlation based clustering, but then the timely shift
we're not recognized and the result dont satisfy my claims.
Is there a function that could cover my problem or have i to build up something
on my own. Im thankful for every kind of help, after two days of tutorials and examples i totaly uninspired. I hope i could explain the problem well enough to you.
I attached a picture. Here you can see some example time series.
There you could see the problem. The two series in the middle are set to one cluster,
although the upper and the one on the bottom have the same shape as one of the middle.
Have you tried the R package dtwclust
https://cran.r-project.org/web/packages/dtwclust/index.html
(I'm just starting to explore this package, but it seems like it covers a lot of aspects of time series clustering and it has lots of good references.)
you can use the kml package. It is used specifically to longitudinal data. You can consult its help. It has the next example:
### Generation of some data
cld1 <- generateArtificialLongData(25)
### We suspect 3, 4 or 6 clusters, we want 3 redrawing.
### We want to "see" what happen (so printCal and printTraj are TRUE)
kml(cld1,c(3,4,6),3,toPlot='both')
### 4 seems to be the best. We want 7 more redrawing.
### We don't want to see again, we want to get the result as fast as possible.
kml(cld1,4,10)
Example cluster
I have been trying to cluster my data to be able to sort out the different intensities. From the graph bellow you can see two distinct groups. Other plots like this are not so easilly distinquished so I thought k-means with a cluster estimation would be a good way to go. So I am using from the fpc package the function pamkCBI (basically the same as pamk just with an output I find is easier to use) and I am trying to get my data (also bellow) clustered. The problem I have is that the data are being clustered along the x-axis which produces two clusters with the top peak in one and the low peaks in the other set. I need it to distinguish between the V1-V8 lines. I was thinking just cluster along the y-axis by translocating columns and rows, but then I get this error:
Error in summary(silhouette(clustering[ss[[i]]], dx))$avg.width :
$ operator is invalid for atomic vectors
There has to be a way this can be done. If anyone has any suggestions, or another way to do this, using a different package (even a different program or a different clustering technique) I'd appreciate it. Sorry for the long question.
library(flexmix)
library(fpc)
cluster <- pamkCBI(mt,krange=1:100,criterion="multiasw", usepam=FALSE,
scaling=FALSE, diss=FALSE,
critout=FALSE, ns=10, seed=NULL)
Example test matrix
I have a large dataset in R (1M+ rows by 6 columns) that I want to use to train a random forest (using the randomForest package) for regression purposes. Unfortunately, I get a Error in matrix(0, n, n) : too many elements specified error when trying to do the whole thing at once and cannot allocate enough memory kind of errors when running in on a subset of the data -- down to 10,000 or so observations.
Seeing that there is no chance I can add more RAM on my machine and random forests are very suitable for the type of process I am trying to model, I'd really like to make this work.
Any suggestions or workaround ideas are much appreciated.
You're likely asking randomForest to create the proximity matrix for the data, which if you think about it, will be insanely big: 1 million x 1 million. A matrix this size would be required no matter how small you set sampsize. Indeed, simply Googling the error message seems to confirm this, as the package author states that the only place in the entire source code where n,n) is found is in calculating the proximity matrix.
But it's hard to help more, given that you've provided no details about the actual code you're using.
I am using R software (R commander) to cluster my data. I have a smaller subset of my data containing 200 rows and about 800 columns. I am getting the following error when trying kmeans cluster and plot on a graph.
"'princomp' can only be used with more units than variables"
I then created a test doc of 10 row and 10 columns whch plots fine but when I add an extra column I get te error again.
Why is this? I need to be able to plot my cluster. When I view my data set after performing kmeans on it I can see the extra results column which shows which clusters they belong to.
IS there anything I am doing wrong, can I ger rid of this error and plot my larger sample???
Please help, been wrecking my head for a week now.
Thanks guys.
The problem is that you have more variables than sample points and the principal component analysis that is being done is failing.
In the help file for princomp it explains (read ?princomp):
‘princomp’ only handles so-called R-mode PCA, that is feature
extraction of variables. If a data matrix is supplied (possibly
via a formula) it is required that there are at least as many
units as variables. For Q-mode PCA use ‘prcomp’.
Principal component analysis is underspecified if you have fewer samples than data point.
Every data point will be it's own principal component. For PCA to work, the number of instances should be significantly larger than the number of dimensions.
Simply speaking you can look at the problems like this:
If you have n dimensions, you can encode up to n+1 instances using vectors that are all 0 or that have at most one 1. And this is optimal, so PCA will do this! But it is not very helpful.
you can use prcomp instead of princomp