similar to: an integration question

Hi, I have two questions want to ask. 1. If I have a matrix like this, and I want to figure out the rows whose value in the 3rd column are less than 0.05. How can I do it with R. hsa-let-7a--MBTD1 0.528239197 2.41E-05 hsa-let-7a--APOBEC1 0.507869409 5.51E-05 hsa-let-7a--PAPOLA 0.470451884 0.000221774 hsa-let-7a--NF2 0.469280186 0.000231065 hsa-let-7a--SLC17A5

Dear R user, I have written a function which returns max,min and variation of a power (see below) Power is a given matrix(1,n) I call the function >Variation<-VAR(p,(n-deltat)) Now the problem is when I want plot(Results[1],Results[2]). Not possible! I become the following error (in english it means: Error in as.double.default(x) :Object cannot be transformed in double) >

The documentation says, "pmax and pmin take several vectors as arguments and return a single vector giving the parallel maxima (or minima) of the vectors." I discovered that, if you use a matrix or array instead of a vector, pmax returns a matrix or array, respectively. This makes pmax and pmin much more useful, and should not be left to people to discover on their own! For example:

Hi everyone, I am wondering what¹s stopping the LLVM optimizer (opt -O3) from eliminating the apparently useless « icmp sgt » instruction in the following piece of LLVM IR. > ; ModuleID = 'lambda-opt.bc' > target datalayout = "e-m:o-i64:64-f80:128-n8:16:32:64-S128" > target triple = "x86_64-apple-macosx10.10.0" > > ; Function

Hi, Please consider the following : x = c(1,3,NA,5) y = c(2,NA,4,1) min(x,y,na.rm=TRUE) # ok [1] 1 max(x,y,na.rm=TRUE) # ok [1] 5 sum(x,y,na.rm=TRUE) # ok [1] 16 pmin(x,y,na.rm=TRUE) # ok [1] 1 3 4 1 pmax(x,y,na.rm=TRUE) # ok [1] 2 3 4 5 psum(x,y,na.rm=TRUE) [1] 3 3 4 6 # expected result Error: could not find function "psum" # actual result

Hi, I wonder whether similarly to the very quick rowSums and colSums functions in the base package, one could add quick functions that calculate the min or max over rows / cols in a matrix. While apply(x,1,min) works, I found out by profiling a program of mine that it is rather slow for matrices with a very large number of rows. A quick functionality seems to be already there in the

Hello, I'm trying to override pmin and pmax for my own matrix. These two functions have ... as an argument. I tried to override them as follows: setMethod("pmax", class_name, function(x, ..., na.rm) { ... }) I use this way to override primitive functions such as min/max and it works fine. But it doesn't work for pmin and pmax. I guess because they are regular functions? How

Here's a version that doesn't try to do block deletion on it's own. If you use -adce then -simplifycfg, you get what you want. It passes all tests except one, which is that we delete an invoke of a pure function, IE Transforms/ADCE/dce_pure_invoke.ll - I'm not sure why that's bad. The reason we delete it is because it returns false to I.mayHaveSideEffects(), and in particular,

Dear R users, I'm trying to write a function which returns minimum,maximum,mean of a vector(power) I've done the following : VAR<-function(power,length){ for(i in tml:length)){ tvar[i]<-i pmean[i]<-mean(power[i:i+deltat]) pmin[i]<-min(power[i:i+deltat]) pmax[i]<-max(power[i:i+deltat]) varmax[i]<-100*(pmax[i]-pmean[i])/pmean[i]

Why does the assignment of a 3178x93 object to another 3178x93 object remove the dimension attribute? > GT <- array(dim = c(6,nrow(InData),ncol(InSNPs))) > dim(GT) [1] 6 3178 93 > SNP1 <- InSNPs[InData[,"C1"],] > dim(SNP1) [1] 3178 93 > SNP2 <- InSNPs[InData[,"C2"],] > dim(SNP2) [1] 3178 93 > dim(pmin(SNP1,SNP2)) [1] 3178 93

R> x <- 0. + 1:8000 R> y <- outer(-x, x, pmin) Error: cannot allocate vector of size 1000000 Kb Why does R need to allocate a gigabyte to create an 8000 x 8000 matrix? It doesn't have any trouble with outer(-x, x, "+"). Thanks. -- David Brahm (brahm at alum.mit.edu) Version: platform = i686-pc-linux-gnu arch = i686 os = linux-gnu system = i686, linux-gnu status =

Estimados usuarios: Estoy intentando reproducir el ejemplo 6.4 de Thomas Lumley. Complex Survey. Editorial Wiley. 2010 (ver la página en google:

Hello, all, I have a dataframe of three rows and umpteen columns. I want to show the maximum, minimum, and median with a vertical line and a central dot (I'd use a boxplot, but with only three data points, that's overkill; I can't just use points, because of overlap and some of the other data plotted on the graph). This works: > boxplot(data_frame,

Hi, in order to save space for a publication, it would be nice to have a combined scatter and density plot similar to what is shows on http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=78 I wonder if anybody perhaps has already developed code for this and is willing to share. This is the reproducible code for the histogram version obtained from the site: def.par <-

Please help, I tried a program on S-plus, and it worked. Also I tried the same program on R but not worked. Here is the programme. I put it in a function form. The model and assumption are at the bottom. where counts<-c(22,2,2,0,5,7,14,0,0,2,36,0,0,1,17,10) which is name.data, i is row size and j is the column size. symmetry function(i, j, name.data) { row <- (c(1:i)) col <-

Hello all, I am trying to run a simulation. the simulation presented below. > rep=5 > sr=.10 # selection ratio > pmin=.10 # minority ratio > nap=1000 # total number of applicant > nsle=sr*nap # number of ee selected > nb=nap*pmin # number of minority > nw=nap-nb # number of majority > mb=100 # mean minority > sb=15 # sd minority > mw=100 # mean majority > sw=15 #

Would someone be able to help with this question? I'm using the Gcmrec, Survrec, and Design packages to do a power analysis on simulated data. I'm receiving an error after using the Survr function that all data must have a censoring time even after using the gcmrec function: newdata<-addCenTime(olddata). My program is below. I'd greatly appreciate any help!

Hi All, I meant to take the min row by row. But the result is apparently not what I want. Changing min to pmin solve the problem. > df=data.frame(X=1:10, Y=1:10) > transform(df, Z=min(X,10-Y)) X Y Z 1 1 1 0 2 2 2 0 3 3 3 0 4 4 4 0 5 5 5 0 6 6 6 0 7 7 7 0 8 8 8 0 9 9 9 0 10 10 10 0 I try to look at the source code to understand what transform() does. I know

I have two vectors (k.floor and k.ceiling) of integers of the same length, and a function (fpr). b <- 10:40 k.floor <- floor(log(2) * b) k.ceiling <- ceiling(log(2) * b) fpr.floor <- fpr(b, k.floor) fpr.ceiling <- fpr(b, k.ceiling) If R had a element-wise ternary function, I'd like to do something like this: (fpr.floor < fpr.ceiling) ? k.floor :

Hi everyone, I'm experiencing difficulty getting the results I want when I use a nested for loop. I have a data set to which I perform some calculations, and then try to apply a regression over a rolling window. The code runs, but the regression results I am getting (intercept and slope) are simply the same, repeated again and again in the results matrix. The regression does not seem to be