similar to: possible bug in sd

R 1.8.1 with Windows XP. I have a question about how sd() behaves with NA's: > mean(c(1,2,3,NA)) [1] NA > median(c(1,2,3,NA)) [1] NA > mad(c(1,2,3,NA)) [1] NA > sd(c(1,2,3,NA)) Error in var(x, na.rm = na.rm) : missing observations in cov/cor > What is so special about the standard deviation, relative to other descriptive statistics, that the presence of NA's deserves an

Dear all, at the following address http://linaria.dst.unive.it/~claudio/R/Rmaterial.html#ga you can download a package to build simple genetic algorithms. I wrote this package to introduce genetic algorithms to student in labs, which means, the package emphasize "how a ga works" instead of "how fast it could be". Please, let me know any comments or suggestions, Bests,

Hello, I think this qualify as a bug > x<-c(1,2,3,4,NA,6,7) > mean(x) [1] NA > mean(x,na.rm=T) [1] 3.833333 > sd(x) Error in var(as.vector(x)) : missing observations in cov/cor > sd(x,na.rm=T) Error in sd(x, na.rm = T) : unused argument(s) (na.rm ...) > var(x) Error in var(x) : missing observations in cov/cor > var(x,na.rm=T) [1] 5.366667 > why sd() does not

Hello all, I just upgraded to R 2.7.0 and found that the behavior of 'var' and 'sd' have changed in the presence NAs (this wasn't explicit in the NEWS file, though I see it probably has to do with the change for cor/cov). Anyway, I just want to make sure that it was intentional to produce an error when there was all NAs and na.rm=TRUE, rather than returning an NA (like R

Dear All, Ulric Lund and me are developing a new package called 'circular' that as soon as possible will substitute 'CircStats' package. A pre-released is available at: http://www.dst.unive.it/~claudio/R/circular_0.1.tar.gz I kindly ask everybody is using CircStats to check this new package and send me comments, suggestions and bugs to claudio at unive.it and not to the list. As

Dear All, Ulric Lund and me are developing a new package called 'circular' that as soon as possible will substitute 'CircStats' package. A pre-released is available at: http://www.dst.unive.it/~claudio/R/circular_0.1.tar.gz I kindly ask everybody is using CircStats to check this new package and send me comments, suggestions and bugs to claudio at unive.it and not to the list. As

Hi, I installed "circular" package and I wanted to load it automatically when R starts up, so I added the following lines in file Rprofile.site, .First <- function() library(circular) When R starts up, it gives me the following error: ---------- Loading required package: boot Loading required package: graphics Loading required package: stats Error : .onAttach failed in

Dear Prof. Ripley I noted a change in the behaviour of "cov", which is very reasonable: ## R version 2.7.0 Under development (unstable) (2007-11-30 r43565) > cov(as.numeric(NA), as.numeric(NA), use="complete.obs") Error in cov(as.numeric(NA), as.numeric(NA), use = "complete.obs") : no complete element pairs whereas earlier behavior was, for example: ## R

Dear R-users, Running R 2.1.1 in WindowsXP, there seems to be a 'bug' in sd() If >x<-c(1,2,3,NA,5) >mean(x) [1] NA But >sd(x) Or >var(x) give Error in var(x, na.rm = na.rm) : missing observations in cov/cor There are obvious work-rounds, like >sd(x, is.na(x)==F) which gives the result (with error message) [1] 1.707825 Warning message: the condition has length > 1

In using cor(data.frame), it is annoying that you have to explicitly filter out non-numeric columns, and when you don't, the error message is misleading: > cor(iris) Error in cor(iris) : missing observations in cov/cor In addition: Warning message: In cor(iris) : NAs introduced by coercion It would be nicer if stats:::cor() did the equivalent *itself* of the following for a data.frame:

Dear R-helpers, I´m in the context of writing a general function for error propagation in R. There are somehow a few questions I would like to ask (discuss), as my statistical knowledge is somewhat restricted. Below is the function I wrote, the questions are marked. Many thanks in advance. propagate <- function(expr, varList, type = c("stat", "raw"), cov = TRUE) {

Dear all, I have a data frame with one factor and four numeric variables and wish to obtain the var-cor matrix separately by factor. I tried by() and sapply() but getting nowhere. I understand this can be done by subsetting the dataframe, but there should have some sleek ways of doing it. Here is a simulated dataframe; s <- rep(c("A","B","C"), c(25,22,18)) d

Dear R-experts, Searching the help archives I found a recommendation to do multivariate outlier identification by mahalanobis distances based on a robustly estimated covariance matrix and compare the resulting distances to a chi^2-distribution with p (number of your variables) degrees of freedom. I understand that compared to euclidean distances this has the advantage of being scale-invariant.

Hello, I used the function cor to calculate the pearson correlation coefficient between variables. However, the resulting values do not correspond to the outcome of my excel-calculations, for which I used the formula Cor(x,y)=Cov(x,y)/(SD(x)*SD(y)) So my question is: How does the function "cor" compute the pearson correlation coefficient? Thank you in advance, Ake Nauta

Hi all, [this is a bit hard to describe, so if my initial description is confusing, please try running my code below] #WHAT I'M TRYING TO DO I'd appreciate any help in trying to speed up some code. I've written a script that converts a matrix of integers (usually between 1-10,000 - these represent allele names) into two new matrices of normally distributed values (representing

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers

Does anyone have code that will methodically process R sourcecode, turning T's into TRUE and F's into FALSE? I got bored doing this by hand, after the first 30-odd functions-- there are hundreds left to do. I don't want to simply deparse everything, because that would destroy my beautiful formatting. The reason it's not trivial, is that comment lines, quotes, and split lines need

On 4 Jan 2003 at 12:51, Wolfgang Lindner wrote: > Please excuse me, if the following questions are *too* off-topic, but I found it > interesting. In inspecting your code I came across an R feature, I could not > find in the online manuals: > > Q1. Looking at the left-handside in your function def: > > "cov.cor" <- function ( covmat ) { >

I am trying to run the following function (a hierarchical bayes linear model) and receive the error in solve.default. The function was originally written for an older version of SPlus. Can anyone give me some insights into where the problem is? Thanks R 2.4.1 on MAC OSX 2mb ram Mark Grant markg at uic.edu > attach(Aspirin.frame) > hblm(Diff ~ 1, s = SE) Error in solve.default(R, rinv)