similar to: cumulative sum by group and under some criteria

Hi, directory<- "/home/arunksa111/data.new" #first function filelist<-function(directory,number,list1){ setwd(directory) filelist1<-dir(directory) direct<-dir(directory,pattern = paste("MSMS_",number,"PepInfo.txt",sep=""), full.names = FALSE, recursive = TRUE) list1<-lapply(direct, function(x) read.table(x,header=TRUE, sep =

Hello With due respect, have a nice time. I would like to ask some command in R. It is regarding variable selection in linear regression. In R, there is one rebuild function called "step" which selecting variables according to AIC. let say i have data [y, x1,x2,x3,x4] we start with y~b0 i compute the partial F test and choose the variable with maximum partial F to enter the

HI, Using c11<- 0.01 c12<- 0.01 c1<- 0.10 c2<- 0.10 One possible problem is that: dim(res5) #[1] 513? 20 res6<-aggregate(.~m1+n1+m+n,data=res5[,c(1:6,9:12,21:24)] ,max) #Error in `[.data.frame`(res5, , c(1:6, 9:12, 21:24)) : ?# undefined columns selected A.K. ________________________________ From: Joanna Zhang <zjoanna2013 at gmail.com> To: arun <smartpink111 at

Dear List: I have a data manipulation problem that I was unable to solve in R. I did it in SQL, and it may be that the solution in R is to do it in SQL, but I wondered if people could imagine a vector-based solution. Imagine a list A[i] of observers who observe some set of events B[j]. Each observer i may observe one or more events, and each event j may have been observed by one or more

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

I have a vector of 2,1,0 I want to change to 0,1,2 respectively (the data is allele dosages) I have tried multiple nested if/else statements and looked at the ?if help and cannot work out what is wrong, other people have posted code which is identical and they state works. Any help would be greatly appreciated. > A[1:20] [1] 1 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 > B <-

But for my reporting purpose, I need to generate a bell curve like plot based on empirical PDF, that also contains original points. Any idea would be helpful. Thanks, On Mon, Mar 12, 2018 at 3:49 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > You need to re-read ?density and perhaps think again -- or do some study -- > about how a (kernel) density estimate works. The points at

Dear R-users,  I have a problem, I have the following dataframe:   d<-data.frame(  'y1'=c(1,2,1,2,1,NA,NA), 'y2'=c(1,2,1,1,1,2,1), 'y3'=c(1,NA,1,NA,NA,2,1), 'y4'=c(NA,2,NA,1,1,2,NA), 'a'=c(1,1,1,1,1,1,2) ) where the last variable counts the number of missing values in a row. Now, i want to set rows where a>1 to NA and arrive at something like the

I am estimating a large model by groups. How do you save the results and?returns the associated quantiles? For this example I need a data frame n?? ?xi??????? mu????????beta 1?? 0.1033614? 2.5389580 0.9092611 2? ?0.3401922? 0.5192882 1.5290615 3?? 0.5130798? 0.5668308 1.2105666 I also want to apply gevrlevelPlot() for each "n" or group. ? #Example n <- c(1, 1, 1, 1, 1, 1, 2, 2, 2,

On 3/11/2018 3:35 PM, Christofer Bogaso wrote: > But for my reporting purpose, I need to generate a bell curve like > plot based on empirical PDF, that also contains original points. > > Any idea would be helpful. Thanks, > Christofer, something like the following may get you what you want: ## get the kernel density estimate dens <- density(Dat) ## estimate the density at

Hi R-Group, I am stuck with the following problem: I am constructing a portfolio of 2 variables x and y x <- rnorm(100, mean=100, sd=4) y <- rnorm(100, mean=120, sd=10) which I am combining as follows to a portfolio for sampling purposes: portfolio <- c(rep(x, 8), rep(y, 2)) In this case I have assigned the weights of 8 and 2 to calculate the bootstrapped mean: mean.boot <-

I am finding with the patch that all of the Polyhedron 2005 benchmarks pass on i686-apple-darwin9. Could someone clarify the regression rules for releases? Not building a secondary language on a primary target is usually considered a P1 regression for FSF gcc. Not doing so here gives one the impression that llvm.org isn't playing by the same rules. No one is ever going to want to use these

Hi all Is there any reason why the parameter i in a "for" loop ignores a value of zero? For example sim=c() p=.2 for(i in 0:5) {sim[i]=dbinom(i,5,p) } sim [1] 0.40960 0.20480 0.05120 0.00640 0.00032 In this example the quantile i= 0 was ignored since dbinom(0,5,p) [1] 0.32768 The same behaviour occurs if I use a while loop to perform the same calculation: sim=c() p=.2 i=0

Dear members. I am having problems to understand the kmeans- results in R. I am applying kmeans-algorithms to my big data file, and it is producing the results of the clusters. Q1) Does anybody knows how to find out in which cluster (I have fixed numberofclusters = 5 ) which data have been used? COMMAND (kmeans.results <- kmeans(mydata,centers =5, iter.max= 1000, nstart =10000)) Q2) When I

Should I submit this as a bug report? --- reg-tests-3.Rout.save Thu Jul 3 09:55:40 2003 +++ reg-tests-3.Rout Sun Nov 23 13:10:57 2003 @@ -1,17 +1,18 @@ -R : Copyright 2003, The R Development Core Team -Version 1.8.0 Under development (unstable) (2003-07-03) +R : Copyright 2003, The R Foundation for Statistical Computing +Version 1.8.1 (2003-11-21), ISBN 3-900051-00-3 R is free software and

Hi, I'm new to R, and I'm a bit confused with the "convolve()" function. If I do: x<-c(1, 2, 3) convolve(x, rev(x), TRUE, "open") = 9 12 10 4 1 But I expected: 3 8 14 8 3 (like in Octave/MATLAB - conv(x, reverse(x)) ) 3 2 1 x 1 2 3 = 3 2 1 0 6 4 2 0 0 9 6 3 = 3 8 14 8 3 The thing is, that "convolve(x, x, TRUE, "open")" works. For me

Dear all, I have problem here : I'm using garchFit from fSeries package, here is part of the script : > data <- read.table("d:/data.txt") > a <- garchFit(~garch(1,1),ts(data)) I also attached the file here. In my experience, I got my R not responding. I also tried with > a <- garchFit(~garch(1,1),ts(data*10)) and it's worked. I

Dear All, I am not familiar with R yet I want to use it to perform some task, hence my posting here. I hope someone can help. I have a set of data, genes (rows) and samples (columns). I want to do a Pearson correlation on all the possible pairwise combinations of all the genes (2000). Does anyone have an idea of how to execute this in R? Thanks in advance. -- View this message in context:

Hi!, I'm really hoping someone out there will be able to help me. I recently started my MSc dissertation on Population Projection Matrices, which has been going well until now. I am trying to set-up a general script that does a pairwise comparison of all elements in my matrices. So for example, given that I have the following matrix S: > S [,1] [,2] [,3] [1,]

Dear R experts, I have a Matlab code which I am translating to R in order to examine and enhance it. First of all, I need to reproduce in R the results which were already obtained in Matlab (to make sure that everything is correct). There are some matrix manipulations and '\' operation among them in the code. I have the following data frame > ABS.df Pro syn