similar to: pmin(), pmax() - slower than necessary for common cases

Yes, functions like c, min and max are special cases, as they are primitives. For ordinary functions, you just need to promote them with "..." as the signature: setGeneric("pmax", signature="...") setMethod("pmax", "Class", function(..., na.rm=FALSE) { }) One caveat is that all arguments passed via "..." must derive from the class

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

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

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:

Here's a patch that should make predict.glm(..., type="response") retain the names. The change passes make check on our Opteron running SLES9. One simple test is: names(predict(glm(y ~ x, family=binomial, data=data.frame(y=c(1, 0, 1, 0), x=c(1, 1, 0, 0))), newdata=data.frame(x=c(0, 0.5, 1)), type="response")) which gives [1]

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) >

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

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]

Extremes.Rd, that documents 'max' and 'pmax', has this in "Details" section, in the paragraph before the last. By definition the min/max of a numeric vector containing an NaN is NaN, except that the min/max of any vector containing an NA is NA even if it also contains an NaN. ------------------ >>>>> Michal Burda <michal.burda at centrum.cz>

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

You have to put the tml in the function and deltat too. like VAR<-function(power,length,deltat,tml){ -----Mensagem original----- De: r-help-bounces em r-project.org [mailto:r-help-bounces em r-project.org]Em nome de mysimbaa Enviada em: sexta-feira, 4 de julho de 2008 11:22 Para: r-help em r-project.org Assunto: [R] initialize a matrix Dear R users, I'm trying to write a function

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

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,

> From: Ross Darnell > > Liaw, Andy wrote: > >>From: Liaw, Andy > >> > >> > >>>From: Ross Darnell > >>> > >>>A good point but what is the value of storing a large set of > >>>predicted > >>>values when the values of the explanatory variables are lost > >>>(predicted >

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,

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

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 <-

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

I append below a suggested update for p.adjust(). 1. A new method "yh" for control of FDR is included which is valid for any dependency structure. Reference is Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188. 2. I've re-named the "fdr" method to "bh" but

Hi, when running the following on a fresh R, library("IRanges") annotation showMethods("annotation") Biobase:::annotation showMethods("annotation") I get (see the "^^^^^" marked output at the bottom): > library("IRanges") Carico il pacchetto richiesto: 'IRanges' The following object(s) are masked from package:base :