search for: nbooting

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 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

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

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

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 <-

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

Hi, I am performing GEV analysis on temperature/precipitation data and want to use the ks.boot function but I am unsure of how to implement it using the pgev distribution. For example: ks.test(data,pgev,shape,location,scale) ks.boot(data,(distribution parameters?),alternative="pgev",nboots=1000) Any advice? Apologies in advance if I have used the wrong email address. Regards, Louise

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).

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 am using errorest function from ipred package. I am hoping to perform "bootstrap 0.632+" and "bootstrap leave one out". According to the manual page for errorest, i use the following command: ce632[i]<-errorest(ytrain ~., data=mydata, model=lda, estimator=c("boot","632plus"), predict=mypredict.lda)$error It didn't work. I then tried the

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

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

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,

Full_Name: Charles J. Geyer Version: 1.3.1 OS: linux-gnu-i686 Submission from: (NULL) (134.84.86.22) ltsreg gives incomprehensible (to me) warnings A homework problem for nonparametrics ########## start example ########## library(bootstrap) data(cell) names(cell) attach(cell) library(lqs) plot(V1, V2) fred <- ltsreg(V2 ~ V1 + I(V1^2)) curve(predict(fred, data.frame(V1 = x)), add = TRUE)

Hi all, I'm new to R and I'm struggling to decipher an error message. Briefly, I am trying to use the pvclust package to do hierarchical clustering of some CGH data. The data is from the Progenetix CGH database. It is arranged as a table where each column is a single case and each row is a single chromosome band. The value in each cell is either 0, 1, 2, or -1. Corresponding to no change,

Hi,I am currently doing a project in which we are to investigate the size and power of three different one sample tests over three different distributions using a number of different sample sizes and values for mu1. I have written a function and was trying to get my answer for each test into the right position in an array so the output is the power of each combination of test, distribution, sample

Hola, Quiero especificar una ecuación con varias variables explicativas de una manera eficiente sin necesidad de escribir todas y cada una. Tengo un conjunto de variables (junto con otras) dentro de una base de datos que se llaman pot23 pot311 pot312 pot 316 pot317........... pot80. No necesariamente están secuenciadas. Quisiera saber cómo indicar que incluya todas las variables de pot23 a pot80

Hallo everybody! I am trying to perform a TiTAN (Baker & King 2010) analysis with R 2.14.1. I have come that far: h89Abund <- read.csv("Fish89Abund.csv") > names (Fish89Abund) [1] "StationCode" "Abramisbrama" "Alburnoidesbipunctatus" "Alburnusalburnus" [5] "Ameiurusmelas" >

Dear R users, I've written the following functions to implement Silverman's modality test ("Using kernel density estimates to investigate multimodality", J.R. Stat. Soc. B, 43, (1981), 97-99.), and I tested them on the Chondrite data set (Good & Gaskins, J. Amer. Stat. Ass., 75, (1980), 42-56). Values for the critical window width seem OK, which is not the case for the