similar to: WGCNA package installation segmentation fault

Hi all, I'm trying to set up the Windows Rtools toolset for building packages with compiled code. I installed for Windows R-3.4.3 from CRAN and installed Rtools-3.4 in a custom location M:\R\R-3.4.3 and M:\R\Rtools-3.4 Following the instructions, in shell, I set Path=M:\R\Rtools-3.4\bin;M:\R\Rtools-3.4\gcc-4.6.3\bin;M:\R\R-3.4.3\bin;... (the ... are other paths irrelevant for R/Rtools).

I can confirm the behaviour that you report.? Usually I put Rtools in c:\programs\Rtools and modify the path accordingly. Recently (don't recall for how long) I have encountered the same problems as you have and I have resorted to moving Rtools to c:\Rtools I have no idea as how to proceed; perhaps it could be worth trying an older version of Rtools (though that may cause other problems).

Dear all , I would like to reconstruct coexpression networks from proteomic count data having integer values. Some internal function doesn't like to work well with integers. How can this error be rectified? > adjacency = adjacency(datExpr, power = softPower, type = "signed"); Error in cor(datExpr, use = "p") : REAL() can only be applied to a 'numeric', not a

Hi, I'm running a tutorial ("Meta-analyses of data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below). I'm using R2.13 Can you help me? Do you know, what is happens? Thanks Raquel multiExpr = list(A = list(data=t(badea)),B = list(data=t(mayo))) # two independent datasets (dim = 13447 x 36) mp =

Hello fellow R users, I am currently learning to use R, so please forgive me if there is an obvious explanation for the following problem. My goal is to perform WGCNA on a dataset of 19776 genes, so I opted to follow the block-wise network construction (Section 2c) in the WGCNA R Tutorial by Peter Langfelder and Steve Horvath. I ended up with 10 blocks and am able to plot the dendrograms and

Hi, I am have a data set of around 43000 probes(rows), and have to calculate correlation matrix. When I run cor function in R, its throwing an error message of RAM shortage which was obvious for such huge number of rows. I am not getting a logical way to cut off this huge number of entities, is there an alternative to pearson correlation or with other dist() methods calculation(euclidean) that

Dear All, I am using the plotrix library to plot some matrices. I have a problem: some of my data are outliers, hence using a linear color scale does not work very well (you would see too many cells having a similar, indistinguishable color). See the code snipped at the end of the email. Plotting the logarithm of the data gets the job done, but my problem is that I would like to write in every

Hi all, I maintain the WGCNA package which at present has nearly 200 functions. In the future there will be more. Curious whether it would be preferable or useful to split the package into a couple different ones with different aims. Obviously, when one calls a function in R, package name spaces have to be traversed to find the matching name - does the speed of this depend on how functions are

Dear R-Help, I have a clustering problem with hclust that I hope someone can help me with. Consider the classic hclust example: hc <- hclust(dist(USArrests), "ave") plot(hc) I would like to cut the tree up in such a way so as to avoid small clusters, so that we get a minimum number of items in each cluster, and therefore avoid singletons. e.g. in this example, you can see

Hi All, is there any effiective and dense/compact method to calculate the mean of a list of - of course coincident - matrices on an element by element basis? The resulting matrix' [i, j]-th element is the mean of the list's matrices' [i, j]-th elements respectively... Iterating by for statement is quite straightforward, but I am seeking for a more elegant solution, and my attempt with

Dear all, I've been trying to find how to compare tow different heatmaps but I'm having trouble getting the colors bar to be the same. I'm doing something like the following: library(gplots) dat<-cor(matrix(rnorm(100, m=10), nrow=10)) mat<-cor(matrix(rnorm(100), nrow=10)) dev.new() heatmap.2(mat, Rowv=NA, Colv=NA, col=redgreen(75), symm=TRUE, trace="none",

It seems like many of the multi-threaded BLASes have some sort of fundamental problem preventing use in the way Juan suggests: - Dirk's vignette states that ATLAS "fixes the number of cores used at compile-time and cannot vary this setting at run-time", so any user-friendly implementation for R would have to compile ATLAS for 1-16 threads to allow the user to switch at run-time.

I am in need of someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix

Hello, I have a self-defined function to be computed on each column in a matrix. The basic idea is to ignore the elements that have value of 0 during computation. I should be able to write my own function but it could be computational expensive, so I'd love to ask if anyone may have suggestions on how to implement it more efficiently. Thanks in advance. For example, there are three

I am now getting the occasional complaint about survival routines that are not able to handle big data.?? I looked in the manuals to try and update my understanding of max vector size, max matrix, max data set, etc; but it is either not there or I missed it (the latter more likely).?? Is it still .Machine$integer.max for everything??? Will that change??? Found where? I am going to need to go

I didn't do the compile; is there a way to check whether that was used? If not, I'll inquire with our sysadmin and report back. In any case, my suggestion was motivated by the fact that some parts of R use OpenMP while others do not, in the hope that the former could have their OpenBLAS omelet without breaking the OpenMP eggs, so to speak. On 01/09/2018 06:41 PM, Keith O'Hara

Please pardon my ignorance, but doesn't OpenBLAS still not always play nicely with multi-threaded OpenMP? (for example, don't race conditions sometimes crop up)? If so, it might be nice to have the ability to temporarily disable multi-threaded OpenMP (effectively: omp_set_num_threads(1)) for the duration of operations using OpenBLAS. Regards Ben > Julia using OpenBLAS is *very*

Dear BioConductor and R fellow users I apologize in advance for double posting, but I am not sure which list would actually be best fit for this message. I am experiencing a weird error with my R installation on Ubuntu 10.04.4 (LTS) 64bit: When I run R on the terminal everything goes smoothly: $R R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation

Hi R-devel list, OpenBLAS is readily available for unix-likes: https://cloud.r-project.org/web/packages/gcbd/vignettes/gcbd.pdf However, my questions are: 1) Would R-devel consider using OpenBLAS for the main distribution of R for all platforms including Windows? 2) If so, would R-devel set the default multi-thread level to the number of (real) cores on a machine? My sense is there're a

Julia Programming Language uses also OpenBlas, and it is actively maintained with bugs being fixed as I have checked it out: http://www.openblas.net/Changelog.txt So I still see it ok to be included as an options(...) feature (by default off, just for safety), over other Blas libraries. R could not use Intel MKL for legal reasons (I think), because as long that R ships with GPL libraries,