search for: pcols

Hi, i am working with large data frames with many dependend variables. I want to write some functions that will allow me to quickly select variables from the frame and plot them in various colors depending on factor columns, possibly selecting rows according to factor conditions. In order to do this in a nice function, i need to understand how to work with a column name in the body of a

...000E+03 2.33000E+01 4.90000E+01", " 43 3.77870E+01 2.46260E+03 4.44227E+03 2.31390E+01 4.84272E+01", " -1 3.67870E+01 1.46260E+03 4.44227E+03 2.31390E+01 4.84271E+01") x2 <- read.table(textConnection(x1),as.is=T,head=T,skip=1) x3 <- oC(x2) pcols <- c(names(x2)[x3$thix],x3$oi1$cnm) x4 <- x2[,match(c(setdiff(names(x2),pcols),pcols),names(x2))] Thanks a TON Santosh [[alternative HTML version deleted]]

The continuing challenges of getting a workable server continue. The latest spins of VirtualBox seem to have solved the issues of DKMS auto compiles of the driver being aborted. The yum updates work as expected, provided one stops the running vms manually first. However, trying to get systemd to start the vms is proving to be a PITA. I have extensively googled and found that there are no

Hello, I'm training a set of data with Caret package using an elastic net (glmnet). Most of the time train works ok, but when the data set grows in size I get the following error: Error en { : task 1 failed - "arguments imply differing number of rows: 9, 10" and several warnings like this one: 1: In eval(expr, envir, enclos) : model fit failed for Resample01 My call to train

Dear all, I'm calculating matrix correlations with permutation tests and I got this funny result. All correlation coefficients are the same with mantel.test {ncf} and pcol {simba} but the two functions yield dramatically different p-values (using the same number of permutations). Could anyone please enlighten me what is causing the difference and which result I can trust? (My matrices

Dear R users, I have a 51 by 51 matrix of p-values (named as pvalue_MA). I want to present graphically this matrix in a plot (filled contour plot) where both axes represent probabilities. I have also added a grid in this plot. I want to highlight in white the cells of the grid that represent p-values smaller than the (common) significance threshold, 0.05. The code from this plot is colored in

Hi all, My data genes [,1] [,2] [,3] [,4] [1,] 25 72 23 55 [2,] 34 53 41 33 [3,] 26 43 26 44 [4,] 36 64 64 22 [5,] 47 72 67 34 stu<-t.test(genes[,1:2],genes[,3:4]) > stu$p.value [1] 0.4198002 i get 1 pvalue for the entire col1:col2 Vs col3:col4. I am trying to get 5 p values for the 5 rows i have. I am trying to avoid a for loop coz my

> I was wondering why survreg (in survival package) can not handle > three-way interactions. I have an AFT ..... You have given us no data to diagnose your problem. What do you mean by "cannot handle" -- does the package print a message "no 3 way interactions", gives wrong answers, your laptop catches on fire when you run it, ....? Also, make sure you read