I have been trying to convert my supradjacency matrix of a multilayer network (not a multiplex! I have interactions between the layers) in order to investigate some properties such as centrality, communities etc.. I was thinking of using MuxViz or Multinet as a package. However, I do not understand how I can represent it in a way for the function to recognize my object as a network. I can get for instance my data to look like:
From layer
From Node
To Layer
To Node
Weight
A
X
B
Y
a
A
Y
B
Z
b
But I am not sure how to transform it into an object which would be readable for the functions.
Thanks
Related
I'm a noob with R, and I'm trying to do clustering on some data samples.
I've tried a PCA,
res.pca <- PCA(df,
ncp = 5, # nb composantes principales.
graph = TRUE,
)
and I can get the full elements list with new coordinates using
res.pca$ind
This is great and works perfectly
for info using the 2 first axis with the PCA, I've 80% of variability on one axis and a bit more than 10% on the Second axis. I was quite proud of the result considering that I've 30 variables ... and in the End the PCA implicitly says that 2 dimension will be enough.
Still working on those data I tried the DBSCAN Clustering method fpc::dbscan :
library (factoextra)
db <- fpc::dbscan(df, eps = 22, MinPts = 3)
and after doing the dbscan and graphing the clusters using fviz_cluster, the Two dimensions display says : 92.8% on axis 1 and 6.7% on axis 2!!!! (more than 99% of the total variance explained with 2 axis !
In short, the DBSCAN has transformed my 30 variables data in a way that looks to be better than the PCA. The overall clustering of DBSCAN is rubbish for my data, but the transformation that has been used is absolutely excellent.
My issue is that I would like to get access to those new coordinates ... but no way at this time...
the only accessible variables I can see are :
db$cluster, db$eps, db$Minpts, db$isseed
BUT I suspect that some data are accessible otherwize how fviz_cluster, could present the data.
Any Idea ?
The projection is not done by dbscan. fviz_cluster uses the first two components obtained via stats::prcomp on the data.
I'm using igraph community-detection and the community sizes are either too small or too large. Is there any way to specify the size of the detected communities? If not, is there any way for me to manually split or merge communities detected from igraph? Thanks!
Whilst I don't think it's possible to set/specify the size of a community detected by igraph, some of the community detection algorithms allow you to specify how many communities you want (an alternative to splitting/merging).
You can use either the cluster_spinglass() function and set spins to be the number of communities desired. Or use one of the hierarchical methods and then use cut_at() to get the desired number of communities, using the no argument to specify how many communities you want.
Example code:
# Set up your graph object
g <-[an igraph object] # set up your graph
# Use spinglass to create a set number of communities
sg <- g %>% cluster_spinglass(spins = 10) # produces 10 communities using spinglass algorithm
# Use hierarchical methods and cut_at to create a set number of communities
walk <- g %>% cluster_walktrap() %>% cut_at(no = 10)
eb <- g %>% cluster_edge_betweenness() %>% cut_at(no = 10)
Note that the spinglass method will give you back a communities object, whereas the cut_at method simply gives you back the community indices for all nodes in the graph (i.e. a simple numeric vector).
You can find more details on the communities help page.
I'm a complete beginner at the Z notation. I need to represent a graph type in Z. The idea I have is to use an incidence matrix so that I can traverse freely between nodes and edges with ease.
The only problem is, I don't know how to specify the incidence matrix in Z. I would think that I need a 2D array, but looking through the reference material available for the Z notation, arrays are commonly represented using seq. Is there another way to specify a multi dimensional array?
Thanks in advance.
I think a relation between nodes would be a better representation for an incidence matrix. Lets assume that we have a type node:
[node]
Then a graph could be modelled as a relation between nodes:
graph : node \rel node
This would be a directed graph because there could be an edge n1->n2 in graph but not n2->n1. If you need an undirected graph you can add a further restriction:
graph\inv = graph
(The inverse of graph is the same as graph, i.e. if n1->n2 in graph, then n2->n1 must be in graph, too.)
If you really want to model an incidence matrix as a multidimensional array, you can define a function that maps from a position in the array to an integer, e.g.:
matrix: (node \cross node) \fun {0,1}
The relation between the two representations can be expressed as:
\forall n1,n2:node # (n1,n2)\in graph \iff graph( (n1,n2) ) = 1
Hi I am using partitioning around medoids algorithm for clustering using the pam function in clustering package. I have 4 attributes in the dataset that I clustered and they seem to give me around 6 clusters and I want to generate a a plot of these clusters across those 4 attributes like this 1: http://www.flickr.com/photos/52099123#N06/7036003411/in/photostream/lightbox/ "Centroid plot"
But the only way I can draw the clustering result is either using a dendrogram or using
plot (data, col = result$clustering) command which seems to generate a plot similar to this
[2] : http://www.flickr.com/photos/52099123#N06/7036003777/in/photostream "pam results".
Although the first image is a centroid plot I am wondering if there are any tools available in R to do the same with a medoid plot Note that it also prints the size of each cluster in the plot. It would be great to know if there are any packages/solutions available in R that facilitate to do this or if not what should be a good starting point in order to achieve plots similar to that in Image 1.
Thanks
Hi All,I was trying to work out the problem the way Joran told but I think I did not understand it correctly and have not done it the right way as it is supposed to be done. Anyway this is what I have done so far. Following is how the file looks like that I tried to cluster
geneID RPKM-base RPKM-1cm RPKM+4cm RPKMtip
GRMZM2G181227 3.412444267 3.16437442 1.287909035 0.037320722
GRMZM2G146885 14.17287135 11.3577013 2.778514642 2.226818648
GRMZM2G139463 6.866752401 5.373925806 1.388843962 1.062745344
GRMZM2G015295 1349.446347 447.4635291 29.43627879 29.2643755
GRMZM2G111909 47.95903081 27.5256729 1.656555758 0.949824883
GRMZM2G078097 4.433627458 0.928492841 0.063329249 0.034255945
GRMZM2G450498 36.15941083 9.45235616 0.700105077 0.194759794
GRMZM2G413652 25.06985426 15.91342458 5.372151214 3.618914949
GRMZM2G090087 21.00891969 18.02318412 17.49531186 10.74302155
following is the Pam clustering output
GRMZM2G181227
1
GRMZM2G146885
2
GRMZM2G139463
2
GRMZM2G015295
2
GRMZM2G111909
2
GRMZM2G078097
3
GRMZM2G450498
3
GRMZM2G413652
2
GRMZM2G090087
2
AC217811.3_FG003
2
Using the above two files I generated a third file that somewhat looks like this and has cluster information in the form of cluster type K1,K2,etc
geneID RPKM-base RPKM-1cm RPKM+4cm RPKMtip Cluster_type
GRMZM2G181227 3.412444267 3.16437442 1.287909035 0.037320722 K1
GRMZM2G146885 14.17287135 11.3577013 2.778514642 2.226818648 K2
GRMZM2G139463 6.866752401 5.373925806 1.388843962 1.062745344 K2
GRMZM2G015295 1349.446347 447.4635291 29.43627879 29.2643755 K2
GRMZM2G111909 47.95903081 27.5256729 1.656555758 0.949824883 K2
GRMZM2G078097 4.433627458 0.928492841 0.063329249 0.034255945 K3
GRMZM2G450498 36.15941083 9.45235616 0.700105077 0.194759794 K3
GRMZM2G413652 25.06985426 15.91342458 5.372151214 3.618914949 K2
GRMZM2G090087 21.00891969 18.02318412 17.49531186 10.74302155 K2
I certainly don't think that this is the file that joran would have wanted me to create but I could not think of anything else thus I ran lattice on the above file using the following code.
clusres<- read.table("clusinput.txt",header=TRUE,sep="\t");
jpeg(filename = "clusplot.jpeg", width = 800, height = 1078,
pointsize = 12, quality = 100, bg = "white",res=100);
parallel(~clusres[2:5]|Cluster_type,clusres,horizontal.axis=FALSE);
dev.off();
and I get a picture like this
Since I want one single line as the representative of the whole cluster at four different points this output is wrong moreover I tried playing with lattice but I can not figure out how to make it accept the Rpkm values as the X coordinate It always seems to plot so many lines against a maximum or minimum value at the Y coordinate which I don't understand what it is.
It will be great if anybody can help me out. Sorry If my question still seems absurd to you.
I do not know of any pre-built functions that generate the plot you indicate, which looks to me like a sort of parallel coordinates plot.
But generating such a plot would be a fairly trivial exercise.
Add a column of cluster labels (K1,K2, etc.) to your original data set, based on your clustering algorithm's output.
Use one of the many, many tools in R for aggregating data (plyr, aggregate, etc.) to calculate the relevant summary statistics by cluster on each of the four variables. (You haven't said what the first graph is actually plotting. Mean and sd? Median and MAD?)
Since you want the plots split into six separate panels, or facets, you will probably want to plot the data using either ggplot or lattice, both of which provide excellent support for creating the same plot, split across a single grouping vector (i.e. the clusters in your case).
But that's about as specific as anyone can get, given that you've provided so little information (i.e. no minimal runnable example, as recommended here).
How about using clusplot from package cluster with partitioning around medoids? Here is a simple example (from the example section):
require(cluster)
#generate 25 objects, divided into 2 clusters.
x <- rbind(cbind(rnorm(10,0,0.5), rnorm(10,0,0.5)),
cbind(rnorm(15,5,0.5), rnorm(15,5,0.5)))
clusplot(pam(x, 2)) #`pam` does you partitioning
Your comments, suggestions, or solutions are/will be greatly appreciated, thank you.
I'm using the fpc package in R to do a dbscan analysis of some very dense data (3 sets of 40,000 points between the range -3, 6).
I've found some clusters, and I need to graph just the significant ones. The problem is that I have a single cluster (the first) with about 39,000 points in it. I need to graph all other clusters but this one.
The dbscan() creates a special data type to store all of this cluster data in. It's not indexed like a data frame would be (but maybe there is a way to represent it as such?).
I can graph the dbscan type using a basic plot() call. But, like I said, this will graph the irrelevant 39,000 points.
tl;dr:
how do I graph only specific clusters of a dbscan data type?
If you look at the help page (?dbscan) it is organized like all others into sections labeled Description, Usage, Arguments, Details and Value. The Value section describes what the function dbscan returns. In this case it is simply a list (a standard R data type) with a few components.
The cluster component is simply an integer vector whose length it equal to the number of rows in your data that indicates which cluster each observation is a member of. So you can use this vector to subset your data to extract only those clusters you'd like and then plot just those data points.
For example, if we use the first example from the help page:
set.seed(665544)
n <- 600
x <- cbind(runif(10, 0, 10)+rnorm(n, sd=0.2), runif(10, 0, 10)+rnorm(n,
sd=0.2))
ds <- dbscan(x, 0.2)
we can then use the result, ds to plot only the points in clusters 1-3:
#Plot only clusters 1, 2 and 3
plot(x[ds$cluster %in% 1:3,])
Without knowing the specifics of dbscan, I can recommend that you look at the function smoothScatter. It it very useful for examining the main patterns in a scatterplot when you otherwise would have too many points to make sense of the data.
The probably most sensible way of plotting DBSCAN results is using alpha shapes, with the radius set to the epsilon value. Alpha shapes are closely related to convex hulls, but they are not necessarily convex. The alpha radius controls the amount of non-convexity allowed.
This is quite closely related to the DBSCAN cluster model of density connected objects, and as such will give you a useful interpretation of the set.
As I'm not using R, I don't know about the alpha shape capabilities of R. There supposedly is a package called alphahull, from a quick check on Google.