I am working with the package random uniform Forest. I am trying to run the examples provided in the documentation:
data(iris)
XY = iris
p = ncol(XY)
X = XY[,-p]
Y = XY[,p]
iris.ruf = randomUniformForest(Species ~., XY, threads = 1)
But I get this error:
Error in checkForRemoteErrors(lapply(cl, recvResult)) :
7 nodes produced errors; first error: object '.doSnowGlobals' not found
I googled and found that this is because it is trying to use paralell computing and it does not find something it needs. I have never used paralell computing so I did not understand the explanations I have found and I do not know how to fix this problem. I also read "error: object '.doSnowGlobals' not found?".
According to the manual using "threads = 1" deactivated paralell computing, but I get the error anyways.
I have also checked and the packages paralell and doParallel are loaded.
I do not really need paralell computing and I do not know if I am "connected" to other computers, so I am not sure If that would even work. Would there be an easy way do deactivate paralell computing? Or another alternative for making this work?
The cause of the problem was that I was working on my university computer for which I do not have administrator rights. The randomUniformForest package makes use of parallel processing which uses the IP protocol (even when only one thread is used).
I tried the package on my private computer and it worked fine.
Related
I have been using the combcoint library for quite some time. Today I installed some new packages in R for my work and since then I have been getting an error, when I try to run the function bayerhanck. The error itself:
Error in p_value_Fisher_bc ~ poly(stat_Fisher_all_bc, 12) * k_dummy :
object 'rlang_tilde_eval' not found
I assume this there is a technical solution to this, but I have not been able to find it.
I am developing a package in Rstudio. Many of my examples need updating so I am going through each one. The only way to check the examples is by running devtools::check() but of course this runs all the checks and it takes a while.
Is there a way of just running the examples so I don't have to wait?
Try the following code to run all examples
devtools::run_examples()
You can also do this without devtools, admittedly it's a bit more circuitous.
package = "rgl"
# this gives a key-value mapping of the various `\alias{}`es
# in each Rd file to that file's canonical name
aliases <- readRDS(system.file("help", "aliases.rds", package=package))
# or sapply(unique(aliases), example, package=package, character.only=TRUE),
# but I think the for loop is superior in this case.
for (topic in unique(aliases)) example(topic, package=package, character.only = TRUE)
I am playing with cluster analysis in R and I would like to plot the cluster:
teste<-data.frame(v1=rnorm(100),v2=rexp(100),v3=runif(100) )
fit.c<- kmeans(teste,2)
aggregate(teste,by=list(fit.c$cluster),FUN=mean)
clusplot(teste, fit.c$cluster, color=TRUE, shade=TRUE,labels=2, lines=0)
But it gives me this error:
Error in clusplot.default(teste, fit.c$cluster, color = TRUE, shade = TRUE, :
4 arguments passed to .Internal(nchar) which requires 3
Does anyone know what is happening?
Thank you
I ran your code and got no problems. I am guessing that there is a version mismatch between your R and the packages you are using. Can you make sure that your cluster package is up to date (and any other packages you are using for that matter), and/or update to the latest version of R?
If that doesn't work, can you add the sessionInfo() information? That is often helpful for diagnosing these sorts of problems.
I want to perform discretization in order to perform mutual information based feature selection for supervised learning R.
library(dprep)
data(iris)
iris.discme=disc.mentr(my.iris,1:5)
I am getting following error:
Error in .C("Points", as.double(x), as.integer(n), mpoint = double(n),
: "Points" not available for .C() for package "dprep"
Any help would be appreciated.
This looks like a bug in the library "dprep". The error is telling you that there is no C function called "Points", even though disc.mentr() attempted to call this function. I would notify the authors of dprep.
I just looked on CRAN and did not see a library called "dprep". If it was available earlier, that may be a sign that it has been taken down.
Currently I am applying one dataset to WGCNA codes for Network construction and Module detection. Here I have to use a function called "pickSoftThreshold" to detect the network topology. When I run that it shows me this error-
> sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
pickSoftThreshold: will use block size 18641.
pickSoftThreshold: calculating connectivity for given powers...
..working on genes 1 through 18641 of 54675
Error in serialize(data, node$con) : error writing to connection
Any idea how to get rid of that?
Thanks in Advance!!
I myself just started using WGCNA a couple of days ago and am not really familiar with it yet. But the error looks like you are using too many genes (up to 55k): I think you should find a way to filter out some of them, if your computer isn't powerful enough.
(Ideas from http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/faq.html )