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Hi, I am trying to add a function in a linear quantile regresion to find a breakpoint. The function I want to add is: y=(k+ax)(x<B)+(k+(a-d)B+dx)(x>B) How do I write it in the rq() function? Do I need to define the parameters in any way and how do I do that? I'm a biologist new to R. Thanks! -- View this message in context:

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

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

Paul Welty wrote: > collection_select(:client, :company_id, @companies, :id, :name, { > :selected_value => 2 , :prompt => "Please select a company..." }) > > Returns a functioning SELECT element, but it won¹t select the second item. > (BTW, using the selected_value as a string doesn¹t work either) > > Am I doing something wrong, or is this impossible? >

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 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

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

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

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" >

Hi everybody! I would like to export the results of a test statistic in a *.csv file, but get an error. The code is below. The data are attached as .txt with tab as separator. I tried to get a sample dataset, but for some reason it didn't work, so please excuse the length of the file. I also attached a txt with the output (copy/paste) in case that helps. ---- > library(WRS) >

Hi, I am doing kernel density plots, and am trying to make the lines thicker. I comparing three groups, in sm.density.compare. I tried changing lwd to make the line sthicker right on the density compare call, but was not able to do it. There is not an option in sm.options to specify line thickness, as well as cex.ylab or cex.xlab- I tried it and it does not change the thickness of the lines.

RNG in R and Splus 3.4 Prof. Ripley asked the details of the example. We were doing parametric bootstrap, so it is similar to simulation. Anyway here is the details. We start with a sample of 19 positive numbers. We know the sample is from truncated exp(0.3)...only the truncation point, theta, is unknown. In other words, the sample can be generated from something like x1 <- rexp(100,

RNG in R and Splus 3.4 Prof. Ripley asked the details of the example. We were doing parametric bootstrap, so it is similar to simulation. Anyway here is the details. We start with a sample of 19 positive numbers. We know the sample is from truncated exp(0.3)...only the truncation point, theta, is unknown. In other words, the sample can be generated from something like x1 <- rexp(100,

Hi R people. &nbsp; I have a problem. What can I create by using functions the combinations of&nbsp;distances in multiple comparisons tests such as duncan multiple range test? &nbsp; cur=sample(rnorm(15),15,replace=TRUE) varr=factor(c(rep("var1",5),rep("var2",5),rep("var3",5))) dat&lt;-data.frame(cur, varr) lmm&lt;-lm(cur~ varr,data=dat)

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

Hi, I'm trying to plot the forecasts I generated using the Plot.Fore function of the BootPR package. But I got an error from zoo: My data: Time Series: Start = 1 End = 18 Frequency = 1 [1] 38731 38628 39117 92809 71984 31226 58613 72360 107956 92066 [11] 95208 99098 95848 120383 110717 105680 98469 101916 Script: y1<-ts(y1);

hi, I'm mustafa. I'm a master student Dokuz Eylül University in Izmir/Turkey. &nbsp; I focus on Statistical programming language R. I learn architecture of R. But i don't find out any material in internet. Would you like to&nbsp;help me this subject? If you have documentations, would you like to share with me? &nbsp; If you help me, i have informations&nbsp;about