similar to: export test results to csv

Hi everybody, To run some statistical tests from the package WRS (from Rand R Wilcox), I need to store my data in a list, which fac2list() from this package does very well. But I would like to do it in a loop for each numerical variable. It would be easier! For now, I have the loop with the extraction and storage into a list. The code is below. ----- # Defines variables file <- ssfamed

Hi everybody! I'm working on R today so I have a lot of questions (you may have noticed that it's the 3rd email today). I'm new on R, so please excuse the "spam"! I have a dataset "ssfa" with many rows and the column names are: > names(ssfa) [1] "SPECSHOR" "BONE" "TO_POS" "MEASUREM" "FACETTE"

Hi everybody! To use some functions, I have to transform my dataset into a list, where each element contains one group, and I have to prepare a list for each variable I have (altogether I have 15 variables, and many entries per factor level) Here is some part of my dataset: SPECSHOR BONE Asfc Smc epLsar cotau tx 454.390369 29.261638 0.001136 cotau tx 117.445711

Hi everybody! I'm trying to write a script to plot a histogram, a boxplot and a qq-plot (under Windows XP, R2.10 if it matters) What I want to do: define the variables (x and y) to be used at the very beginning, so that I don't have to change all occurrences in the script when I want to plot a different variable. The dataset is called "ssfa". TO_POS is a categorical

Greetings, I am running a buch of wilcox tests and need to be able to rapidly export the results into a csv file. I have attached example code as well as my attempts to get what I need. I have tried unlist,cbind,rbind etc but I am obvously missing something simple. FYI I am actually running about 50 WRS tests per dataset, this is just an example. Thanks 10^6 Mike AKCCR <-

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

caching_thread()s do all their work under read access to extent_commit_sem. They give up on this read access only when need_resched() tells them, or when they exit. As a result, somebody that wants a WRITE access to this sem, might wait for a long time. Especially this is problematic in cache_block_group(), which can be called on critical paths like find_free_extent() and in commit path via

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 <-

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, 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,