similar to: S: expert mailing list for general statistical questions

Hello, I am looking for an elegant one-liner for the following operation: x <- rnorm(10) y <- runif(10) c(mean(x)-mean(y), mean(x)+mean(y)) I thought about apply(data.frame(x, y), 2, mean) but I don't know how to apply the +- operation on the result of apply. Thanks, *S* -- Sascha Vieweg, saschaview at gmail.com

Tinn-R (http://www.sciviews.org/Tinn-R/) is one of the topmost suggestions when googling an R-(text-)editor for Windows. However, to me it appears dissappointing that Tinn-R does not handle utf-8 (mac-roman, or any other) encoded R-scripts or, in general, text files. Besides Emacs and the R built-in editor, could you recommend a good editor for Windows, even some commmercial for a small

Hello, Vector y is an alphabetically sorted version of vector x. Will both samples, X and Y, be "absolutely" random or will they have systematic differences? And: Should I sort or shuffle a vector before sampling? Thank you, *S* x <- as.factor(LETTERS[sequence(10:1)]) y <- sort(x) X <- sample(x, 5) Y <- sample(y, 5) -- Sascha Vieweg, saschaview at gmail.com

Running a binary logit model on the data df <- data.frame(y=sample(letters[1:3], 100, repl=T), x=rnorm(100)) reveals some residual deviance: summary(glm(y ~ ., data=df, family=binomial("logit"))) However, running a multinomial model on that data (multinom, nnet) reveals a residual deviance: summary(multinom(y ~ ., data=df)) On page 203, the MASS book says that "here the

Hello Currently, I plot some coefficients with some intervals using function "plotCI()" (package "gplots") using the following code: (m1 <- matrix(0:5, nrow=2, byrow=T, dimnames=list(c("v1", "v2"), c("lo", "m", "hi")))) m2 <- m1 + 1 library(gplots) plotCI( x=1:length(m1[, 1]), pch="",

I run a multinomial regression on a data set with an outcome that has three values. First, I build an initial model, b.mod. Then I run a loop to bootstrap the coefficients. For the initial model, using "predict()", I can print the wrong/false predictions table. But how do I get this table (that is, the predictions) for the bootstrapped model? Thanks for hints, *S* df <-

Hello How can I apply functions along "layers" of a data matrix? Example: daf <- data.frame( 'id' = rep(1:5, 3), matrix(1:60, nrow=15, dimnames=list( NULL, paste('v', 1:4, sep='') )), rep = rep(1:3, each=5) ) The data frame "daf" contains 3 repetitions/layers (rep) of 4 variables of 5 persons (id). For some reason, I want to calculate

Hello How come that the function md.pattern() from package 'mice' delivers a warning when run over data sets with more than 31 variables? library( 'mice' ) x <- as.data.frame( matrix( sample( c(1:3, 1:3, 1:3, NA), 7000, repl=TRUE ), ncol=35, dimnames=list(NULL, paste('V', 11:45, sep="") ) ) ) md.pattern(x) # Warning message:

Hello Hello I have a data frame, x, holding 5 persons answering the question which cars they have used: # the data frame x <- as.data.frame( matrix( c('BMW', '', '', 'Mercedes', 'VW', '', 'Skoda', 'VW', 'BMW', '', '', '', 'VW', 'Skoda',

Hello R users I am quite new to bootstrapping. Now, having some data x, ---- R: set.seed(1234) R: x <- runif(300) ---- I want to bootstrap simple statistics, mean and quantiles (.025, .975). Currently, I run a loop ---- R: res <- as.data.frame(matrix(ncol = 3, dimnames = list(NULL, ... c("M", "Lo", "Hi")))) R: for (i in 1:100) { ... y <-

Hello R experts I am googling and reading around, however, I can't get it working (perhaps because I do not understand much C, however, I'll give it a try). I am trying to include C++ code into an R routine, where the C++ code looks: #include <iostream> using namespace std; void foo (double* x, double* y, double* out) { out[0] = x[0] + y[0]; } Back in R, the command R CMD

Some attribute x from 17 individuals was recorded repeatedly on 6 time points using a Likert scale with 7 distractors. Which statistical test(s) can I apply to check whether the changes along the 6 time points were significant? set.seed( 123 ) x <- matrix( sample( 1:7, 17*6, repl=T ), nrow = 17, byrow = TRUE, dimnames = list(1:17, paste( 'T', 1:6, sep='' )) ) I found

When Sweave outputs function code that spreads across many lines, the default indent of inner lines is 4 spaces (plus the prompt). How can I change that default to 2 spaces? I tried to adjust my Sweave.sty with the option tabsize but that doesn't show an effect. Also the Sweave documentation did not provide a cue. Thanks for hints, *S* -- Sascha Vieweg, saschaview at gmail.com

Hello, I need some form of a "permutative" operation on a numeric vector x x = (v1, v2, v3, ..., vN) that produces x.r = (v1, v1+2, v1+v2+v3, ... v1+v2+...+vN) If the operation is sum() I can run x <- 5:8 m <- matrix(rep(x, length(x)), ncol=length(x)) (x.r <- rowsum(m * upper.tri(m, diag=TRUE), rep(1, length(x)))) But there's two things I don't know and kindly

Just to add to this (I've been looking through the archive) problem with display unicode fonts in pdf document in R If you can use the Cairo package to create pdf on Mac, it seems quite happy with pushing unicode characters through (probably still font family dependant whether it will display) probstring <- c(' \u2264 0.2',' \u2268 0.4',' \u00FC 0.6',' \u2264

Hi there, I have a numeric vector let say: Vect <- c(12.234, 234.5675, 1.5) Now I want a string vector like: changedVec <- c("012.234", "234.568", "001.500") Would be grateful if somebody help me how can I do that. Thanks and regards, [[alternative HTML version deleted]]

Hello I am trying to solve these problems and I am not allowed to use loops or ifs. 1st Question My first question is that I have generated 100 random numbers from the uniform distribution then A)add only the negative integers. B)add elements until the first appearance of a negative element. I know how to choose the negative elements for A but how to find integers? And I dont know what to do

Hello I am trying to solve these problems and I am not allowed to use loops or ifs. 1st Question My first question is that I have generated 100 random numbers from the uniform distribution then A)add only the negative integers. B)add elements until the first appearance of a negative element. I know how to choose the negative elements for A but how to find integers? And I dont know what to do

I am looping through various models with different combinations of independent variables which are stored as columns in x glm(y ~ ??, data=x) How can I pass the colnames of the selected columns of x into ?? seperating them with a + ie I want to generate glm(y ~ x1 + x2, data=x) glm(y ~ x2 + x3, data=x) glm(y ~ x1 + x2 + x4, data=x) ... I have tried the sedit function

I'm preparing to submit my first package to CRAN, thanks to the help of too many people to mention. I've built and checked the package on Windows ( making a zip) and my path points to the 64 bit version of R. Everything builds and checks and the final warnings have been fixed. My package is pure R with no source from other languages. My questions are as follows. I've read the