similar to: parallel bootstrap linear model on multicore mac

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, I have access to my database via command line and through workbench, and have access on the grant tables: mysql> SELECT host,user,password,select_priv,insert_priv FROM user; +------+-------+-------------------------------------------+-------------+-------------+ | host | user | password | select_priv | insert_priv |

Hi all, I am trying to use snow package to do a parallel MCMC. I have read a few guides and articles, the following is that I came up with. When I run it I got the error message: Error in checkForRemoteErrors(lapply(cl, recvResult)) : 4 nodes produced errors; first error: could not find function "ui.Next" The data is a longitudinal data with few repeated readings on a number of

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 <-

Hello- I would like to remove the columns of a matrix that contain all zeros. For example, from x<-matrix(c(1,5,3,2,1,4,0,0,0), ncol=3,nrow=3) I would like to remove the third column. However, because this is in a loop I need a way to first determine which columns are all zeros, and only then remove them. I.e., I don't know which column of x contains all zeros until after x is

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

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

Does anyone know if the sem package in R can implement a stacked model comparison, for example as in LISREL or AMOS? Thanks, Anthony -- Anthony Steven Dick, Ph.D. Post-Doctoral Fellow Human Neuroscience Laboratory Department of Neurology The University of Chicago 5841 S. Maryland Ave. MC-2030 Chicago, IL 60637 Phone: (773)-834-7770 Email: adick at uchicago.edu Web:

Hello- If I input a variance-covariance matrix and specify NA for start values, how does sem determine the start value? Is there a default? Anthony -- Anthony Steven Dick, Ph.D. Post-Doctoral Fellow Human Neuroscience Laboratory Department of Neurology The University of Chicago 5841 S. Maryland Ave. MC-2030 Chicago, IL 60637 Phone: (773)-834-7770 Email: adick at uchicago.edu Web:

Hello all, This is a general stats question--I realize it is an R help list, so tell me to go away if it is inappropriate. I have a 2 X 2 design, and I have specified four identical path models (one for each level of each factor). I want to test for an interaction at each path--essentially (A1 - A2) - (B1 - B2) != 0. I was thinking of computing a contrast for each path of interest, such that

Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)

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

Hello- I am using R to build my initial models, but need to use AMOS to compare the models of two groups (adults vs. kids). The problem is I am getting different results with R and AMOS for the initial models of the separate groups (and the R results make more sense). The parameter estimates (path coefficients and variances) from both programs are nearly identical, but the model chi-squares

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 <-

Hi: I try to construct a Document-Term Meatrix from a corpus. The commands I used are: > library(parallel)> library(tm)> library(RWeka)> library(topicmodels)> library(RTextTools)> cl=makeCluster(detectCores())> invisible(clusterEvalQ(cl, library(tm)))> invisible(clusterEvalQ(cl, library(RWeka))) > invisible(clusterEvalQ(cl, library(topicmodels)))>

Dear R-users, we are using library(snow) for computation on a linux cluster with RMPI. We have a problem with clusterEvalQ: after launching clusterEvalQ it seems loading the required library on each node but if we type a function belonging to the loaded package R doesn't find it. > library(snow) # making cluster with 3 nodes > cl <- makeCluster(3, type = "MPI") Loading

Please, check a tcpdump session on localhost while running the following script: library(parallel) library(tictoc) cl <- makeCluster(1) Sys.sleep(1) for (i in 1:10) { tic() x <- clusterEvalQ(cl, iris) toc() } The initialization phase comprises 7 packets. Then, the 1-second sleep will help you see where the evaluation starts. Each clusterEvalQ generates 6 packets: 1. main ->

I forgot, you should also set.seed() before calling boot() to make the results reproducible. Rui Barradas On 5/22/2018 10:00 AM, Rui Barradas wrote: > 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,

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

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