search for: nsampl

Hi all, I have a problem with getting my code to do what I want! This is the code I have: create.means.one.size<-function(nsample,var,nboot){ mat.x<-matrix(0,nrow=nboot,ncol=nsample) for(i in 1:nboot){ mat.x[i,]<-sample(var,nsample,replace=T) } mean.mat<-rep(0,nboot) for(i in 1:nboot){ mean.mat[i]<-mean(mat.x[i,]) } sd.mean<-sd(mean.mat) return(mean.mat) } where nsample here is a scalar. Then this is w...

Hi there, I have a the following function which takes a name(fname), total number of rows (Total), number of times to make a sample(nSample), number of records/rows to be included in each sample(nPatient). then it generates required samples and calculates the mean and standard deviation of the columns for each sample and finally in calculates the grand mean and standard deviation. The problem is if I run each line of the code separa...

Dear R developers and users: I have the following data, x is the response vaiable, nsample(individual) nested within trt, and subsample nested within nsample, I want to fit trt as fixed effect, and block, nsample(trt) as random effects using lme, is the following coding correct? dat$vgrp <- getGroups(dat, form = ~ 1|trt/nsample, level = 2) ge.lme1 <- lme(fixed=x~trt, data=dat, r...

...When the number of interaction terms exceed 20, the code stops working. Here is a piece of toy code. rm(list=ls()) library(BayesX) library(R2BayesX) #data generating model f2<-function(x1,x2,x3,x4) { y<-2*sin(pi*x1)*1.5+exp(2*x2)/3+2 * sin(4 * pi * (x3 - 0.2) *(x4 - 0.7)) } #the dataset nsample<-40 #sample size x1.tot<-runif(nsample,0,1) x2.tot<-runif(nsample,0,1) x3.tot<-runif(nsample,0,1) x4.tot<-runif(nsample,0,1) x5.tot<-runif(nsample,0,1) x6.tot<-runif(nsample,0,1) x7.tot<-runif(nsample,0,1) pnoise<-0.2 eta<-f2(x1.tot,x2.tot,x3.tot,x4.tot) y.tot<-e...

Hi, Can anyone help to write a more elegant version of my code? I am sure this can be put into a loop but I am having trouble creating the objects b1,b2,b3,...,etc. b1 <- rigamma(50,1,1) theta1 <- rgamma(50,0.5,(1/b1)) sim1 <- rpois(50,theta1) b2 <- rigamma(50,1,1) theta2 <- rgamma(50,0.5,(1/b2)) sim2 <- rpois(50,theta2) b3 <- rigamma(50,1,1) theta3 <-

this is the command i made for a normal distribution, but when i try to plot the histograms, i dont know why the bars don't stick on the line... nsamples<-1000 sampsize<-15 Samples<-matrix(rnorm(nsamples*sampsize,0,1),nrow=nsamples) a<-apply(Samples,1,var) NC14<-a*14 x<-0:40 plot(x,dchisq(x,14),type='h') hist(NC14,freq=F,add=T) -- View this message in context: http://www.nabble.com/Need-help-on-ploting-Histograms-tp236...

...cause the QR decomposition of the model matrix is stored with the fitted model. So far, so good. Then I got fancy and returned the result as a data frame. "bsCoefSample" <- ## Construct a bootstrap sample of coefficients from a ## fitted regression model function(fittedModel, Nsampl) { value <- as.data.frame(t(coef(fittedModel) + qr.coef(fittedModel$qr, matrix(sample(resid(fittedModel), length(resid(fittedModel)) * Nsampl, repl = T), ncol = Nsampl)))) names(value) <- names(coef(fittedModel)) value } Later I thought I could cut...

...ably effective ways of doing the resampling. The following is a function I wrote to create bootstrap samples of coefficients from a fitted linear regression model. bsCoefSample <- ## Construct a bootstrap sample of coefficients from a ## fitted regression model function(fittedModel, Nsampl, ...) { coef(fittedModel) + qr.coef(qr(model.matrix(fittedModel)), matrix(sample(resid(fittedModel), length(resid(fittedModel)) * Nsampl, repl = T), ncol = Nsampl)) } It works ok with S-PLUS but the R version of model.matrix encounters difficulties. R> fm1 &...

...emoteErrors(val) : 2 nodes produced errors; first error: no applicable method for 'lapply' applied to an object of class "list" ". Where this is the problem line of code "yusum <- parLapply(cl, yu, sum)" The function is CallSnow <- function (Nnodes = 2, Nsamples = Nnodes) { require(Rmpi) require(snow) cat("Using snow package, asking for ", Nnodes, "nodes \n") cl <- makeCluster(Nnodes, type="MPI") on.exit(stopCluster(cl)) #print(do.call("rbind", clusterCall(cl, function(cl) Sys.info()["nodename&quot...

Hi, I want to generate 10000 samples from normal distribution with replacement case and every sample size is 50. What should I do ? -- View this message in context: http://r.789695.n4.nabble.com/IMPORTANT-PLEASE-HELP-ME-tp4650676.html Sent from the R help mailing list archive at Nabble.com.

...ursive elimination of least important genes ### author: Dr. Xin Lu, Research Scientist ### Biostatistics Department, Harvard School of Public Health library(e1071) ## read in SVM formated data in filename ## the format following the defination of SVMTorch ## the first line contains 2 integer: nSample nFeature+1 ## followed by a matrix, each row for one sample, with the last column being +/1 1 for class label ReadSVMdata <- function(filename) { dd <- read.table( filename, header=F, skip=1) x <- as.matrix( dd[, 1:(ncol(dd)-1)] ) y <- factor( dd[, ncol(dd)] ) ret <- list(x...

Dear R users, Can anyone please tell me how to generate a large number of samples in R, given certain distribution and size. For example, if I want to generate 1000 samples of size n=100, with a N(0,1) distribution, how should I proceed? (Since I dont want to do "rnorm(100,0,1)" in R for 1000 times) Thanks for help Debbie

...ning or may I run "regsubsets" on the original model bringing in all possible frequencies (88 in all as sums of sinuoids) ? Thank you so much. Kind regards, Maura ******************************************************************** T <- xx$timestamp[end] - xx$timestamp[start] nsamples <- end +1 - start nfr <- ceiling(nsamples/2) yy <- xx[start:end,"amplitude"] tt <- xx[start:end,"timestamp"] cosmat <- matrix(nrow=nsamples,ncol=nfr) coscol <- NULL sinmat <- matrix(nrow=nsamples,ncol=nfr) sincol <- NULL for(i in...

Hi everyone, I have a question about functions. I have two functions: Sampling=function(fname,Total,nSample,nPatient){......return(list(Gmean,Gsd))} Power=function(alfa,m1,m2,s1,s2,n1,n2){....return(powe)} I want to use "Gmean" and "Gsd" which are the returned values from "Sampling" function, in "Power" function. i.e. when I call "power" I want to be a...

...[[3]] [1] "residuals.rpart(tr, \"deviance\")" [[4]] [1] "residuals(tr, \"deviance\")" The problem is that tr$frame does not contain a field called 'yprob'. I created the dat matrix above as follows set.seed(123) Nvars <- 4; Nsamples <- 1000; N <- Nvars*Nsamples; dat <- matrix(sample(1:2, N, c(0.5,0.5), replace=TRUE), Nsamples, Nvars) #dat <- sample.bnet(bnet, 30) dat <- as.data.frame(dat) for (i in 1:Nvars) { dat[,i]<- factor(dat[,i], 1:2) } names(dat) <- c(paste("X", 1:3, sep=""),...

...hannels() vd = ogg.vorbis.VorbisInfo(channels = channels, rate = inwav.getframerate(), quality = 0.2).analysis_init() os = ogg.OggStreamState(5) map(os.packetin, vd.headerout()) og = os.flush() while og: og.writeout(fout) og = os.flush() nsamples = 1024 eos = 0 total = 0 while not eos: data = inwav.readframes(nsamples) total = total + nsamples if not data: vd.write(None) break vd.write_wav(data) #print 100.0 * total / inwav.getnframes() vb = vd.blockout() while vb: vb.analysis()...

...3) CPD$tr <- tree(V4 ~ ., fam.vals) else stop(paste("too many parents for tree CPD", CPD$id)) end list(CPD = CPD, dev = deviance(CPD$tr), score = dev) } Thanks for your help! Kevin P.S. You can reproduce the above problem using the following script Nvars <- 4; Nsamples <- 10; N <- Nvars*Nsamples; dat <- matrix(sample(1:2, N, c(0.5,0.5), replace=TRUE), Nsamples, Nvars) #dat <- sample.bnet(bnet, 10) dat <- as.data.frame(dat) for (i in 1:Nvars) { dat[,i]<- factor(dat[,i], 1:2) } tr <- tree(V4 ~ ., dat) #deviance(tr) foo <- function(dumm...

Hey, I am hoping someone can help me with a sampling question. I have a data frame of 8 variables (the first column is the subjects' id): SubID CSE1 CSE2 CSE3 CSE4 WSE1 WSE2 WSE3 WSE4 1 6 5 6 2 6 2 2 4 2 6 4 7 2 6 6 2 3 3 5 5 5 5 5 5

Hello everyone. Like others on this list, I'm new to R, and really not much of a programmer, so please excuse any obtuse questions! I'm trying to repeat a function across all possible combinations of vectors in a data frame. I'd hugely appreciate any advice! Here's what I'm doing: I have some data: 40 samples, ~460 000 different readings between 1 and 0 for each sample. I

Hi, I'm doing some modelling (lm) for my 3rd year dissertation and I want to do some resampling, especially as I'm working with microbes, getting them to evolve resistance to antimicrobial compounds, and after each exposure I'm measuring the minimum concentration required to kill them (which I'm expecting to rise over time, or exposures), I have 5 lineages per cleaner, and