similar to: sm.options

Dear all, Does anybody know how to get more evaluation points in performing Nonparametric analysis of repeated measurements data with "sm" library. The following command gives the estimation for 50 points, by I would like to increase to 100 points But I do not know how to do that. library(sm) provide.data(citrate, options=list(describe=FALSE)) provide.data(dogs,

Hi all! I'm new to this forum so please excuse me if I don't conform perfectly to the protocols on this board! I'm trying to get an estimate of true prevalence based upon results from an imperfect test. I have various estimates of se/sp which could inform my priors (at least upper and lower limits even if with a uniform distribution) and found the following code on this website..

Hi, I am using this subset statement and it works outside a function. LIS[[i]]<- lapply(LI, subset, select=cov[[i]]) However, wrapped inside a function this statement produces the same values for every LIS[[1]] which is only the first subset of LI. Does anyone know why is not working correctly inside a function? ff = factor(covariate) nLev <- nlevels(ff) cov <-

Hello R useRs, I have a function which returns a list of functions : freq1 <- function(x) { lev <- unique(x[!is.na(x)]) nlev <- length(lev) args <- alist(x=) if (nlev == 1) { body <- c("{", "sum(!is.na(x))", "}") f <- function() {} formals(f) <- as.pairlist(args) body(f) <- parse(text = body) namef <-

Hello R-help group, I have a question about merging lists. I have two lists: Genes list (hSgenes) name chr strand start end transStart transEnd symbol description feature ENSG00000223972 1 1 11874 14412 11874 14412 DEAD/H box polypeptide 11 like 1DEAD/H box polypeptide 11 like 3DEAD/H box polypeptide 11 like 9 ;;

I apologize if this is not appropriate for this mailing list. In R, there is already functionality to drop unused factor levels. However, I am proposing the code below that I wrote. In some occasions, it was faster than applying function 'factor'. In any case, there is no restriction for anyone to use the code below. droplevels2 <- function(x) { if (is.null(levels(x))) stop("no

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

Hi, I'm new to R (and statistics) and my boss has thrown me in the deep-end with the following task: We want to evaluate the impact that sampling size has on our ability to create a robust model, or evaluate how robust the model is to sample size for the purpose of cross-validation i.e. in our current project we have collected a series of independent data at 250 locations, from which

I was trying to plot some data in R. I used the following code to draw a loess fit and got the output as >?lines(lowess(log(abs(t(res))), log(abs(t(synthesised)))), col="red") Error in lowess(log(abs(t(res))), log(abs(t(synthesised)))) :?? NA/NaN/Inf in foreign function call (arg 1) Then I thought to use your Limma package for background correction. Do you think it's a right

Hi list, I tried to load the package SAGx and failed because it complains it's looking for the Biobass which is not there. Then I looked up the package list and Biobass is not found. I'm trying to run the Jonckheere-Terpstra test and from what I see in the R archive, SAGx is the only place it's been implemented. > library(SAGx) Loading required package: multtest Loading required

Dear R-experts, I am trying to bootstrap (and average) the median squared error evaluation metric for a robust regression. I can't get it. What is going wrong ? Here is the reproducible example. ############################# install.packages( "quantreg" ) library(quantreg) crp <-c(12,14,13,24,25,34,45,56,25,34,47,44,35,24,53,44,55,46,36,67) bmi

I wonder if someone could help me adjusting the following code to parallelized snow code: #Creating a data set (not needed to be parallel) n<-100 p<-100 x<-matrix(rnorm(n*p),p) y<-rnorm(n) # Bootstrapping nboot<-1000 alpha<-0.05 rhoboot <- array(0, dim=c(p,nboot)) bootranks <- array(0, dim=c(p,nboot)) bootsamples <- array( floor(runif(n*nboot)*n+1), dim=c(n,nboot)) for

Hello, &nbsp; I want to sum first three terms of each column of matrix. But I don't calculate with "apply" function. &nbsp; skwkrt&lt;-function(N=10000,mu=0,sigma=1,n=100, nboot=1000,alpha=0.05){ x&lt;-rnorm(N,mu,sigma)#population samplex&lt;-matrix(sample(x,n*nboot,replace=T),nrow=nboot) #... } &nbsp; is that: suppose a is a 5x2 matrix. &nbsp;a={1,2,3,4,5

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-

Full_Name: Robert Kushler Version: 2.4.1 OS: Windows XP Submission from: (NULL) (69.245.71.40) In the constant leverage case, plot #5 is not correctly produced. The labels on the x-axis are sorted correctly by magnitude of the fitted value, but the data are plotted in the original factor order. I changed facval[ord] <- facval xx <- facval

Hi all, Small problem in generating the line charts. Question: Legend for the first graph is coming wrong., for second graph correctly. Please fix the legend postion at the down of graph. Plesae give me the solution. Thank you Devarayalu Orange1 <- structure(list(REFID = c(7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9), ARM = c(1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2,

Hi,     I want to use segChrom() method in tilingArray package. For that I need to create a probeAnno object. I could not find much much info by ?probeAnno. I need help in creating  probeAnno object. Snap shot of the file(.txt): chr1 2500014 2500038 + 0.232689943122845 chr1 2500039 2500063 + 2.60502410304227 chr1 2500062 2500086 + 0.0756595313279895 chr1 2500080 2500104 + 0.78574617788405 chr1

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

Hello, My question likely got buried so I am reposting it in the hopes that someone has an answer. I have thought more about the question and modified my question. I hope tha my specific question is: I am attempting to create a bootstrap procedure for a finite sample using the theory of Rao and Wu, JASA (1988) that replicates within each strata (h) n_h - 1 times. To this end, I require a

Hi Everyone, I have been trying to read some Arrayvision files( 2 channel cDNA) and am having some problem. My code is : setwd('C:/work/data/limma/ndd1'); files <- c('ndd1_1.txt','ndd1_2.txt','ndd1_3.txt'); RG=read.maimages(files,"arrayvision",sep="\t"); #Normalisation MA=normalizeWithinArrays(RG); #plotPrintTipLoess(MA); #Fit Linear