I'm new to scilab so working out basics.Below script opens the graphics window shows the empty box.I guess a straight line should be shown for these x,y,z points which doesn't show up here.Why is that so?
x=linspace(1,100)
y=linspace(1,100)
z=linspace(1,100)
plot3d(x,y,z)
plot3d plots surfaces, and you give it 3 vectors instead of matrices. With 3 vectors you can plot a (parametric) curve in 3 dimensions with param3d:
x=linspace(1,100)
y=linspace(1,100)
z=linspace(1,100)
param3d(x,y,z)
I would like to plot in R following contour plots representing two dimensional cumulative distributon functions (CDF)
A CDF in 2 or more dimensions is not unique (Lopes et al. The two-dimensional Kolmogorov-Smirnov test) that's why there are 4 alternative plots (and probably some more).
So far I have no R/Matlab code to show. I don't think it's difficult but most likely very time consuming. There might out there something I could use.
EDIT
Type 1 & 4 are more or less covered, but any help with 2 & 3 would be really appreciated.
EDIT2
Types 2 & 3 using geom_rect - as simple as it gets!
The sequence of recangles is ordered wrt the eucleadian distance of the data. This means, if we assume this generic ordering, there is possibly only one version of defining a 2D CDF instead of two. That would confirm the statement of Lopes et al.and other that there are only 2^N-1 (here 3) ways to define the CDF.
Any thoughts?
i really like the dotplot function in clusterprofiler package. For some reasons related to the object this program creates by itself i cannot replicate this graph with my data.
So my question is, could someone point me to a similar dotplot package/function for achieving the same plot?
Its important to me to show that some of these "biological processes" are present in some clusters (x-axis) ad not in other, and that the colour of the dot is representing its importance (fold). The size of the dot would be represented by an integer.
here the data example i want to show.
thanks in advance.
biological process cluster1-fold cluster2-fold cluster3-fold cluster1-num cluster2-num cluster3-num
cell cycle 0 3 5 0 23 24
dna replication 4 2 0 43 22 0
here the plot i want to replicate
I have a dataset with a x-y-z structure.
X = age of arrival in the city
Y = year of arrival
Z = number of current survivors from X/Y combination
I have no problem plotting this for any given time using RGL in R. However I would like to introduce a time dimension.
I could of course make 23 plots and paste them together, but I would like to be able to manipulate the viewing on the fly, and treat the whole time series as one plot. I have Z values for 23 years. I also would like to colour my plot with an extra z2 variable, being z_year/z_(year-1). Is this possible within the RGl pakcage with some programming or is there a better package available?
Try creating a video like described on SO..
Alternative is a for-loop with a plot and delaying it -> look at ?Sys.sleep
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