similar to: Please use colMeans()! was: Re: Calculate Mean of Column Vectors?

This inconsistency recently came to my attention: > df <- data.frame(A = 1:10, B = rnorm(10)) > min(df) [1] -1.768958 > max(df) [1] 10 > mean(df) [1] NA Warning message: In mean.default(df) : argument is not numeric or logical: returning NA I recall the times where `mean(df)` would give `colMeans(df)` and this behaviour was deemed inconsistent. It seems though that the change has

Having searched and searched I still haven't found what's the problem with my data (I've attached the relevant file). Every time I tried to use the CANCOR-Function I got error messages. So I turned to check my two sets of variables separately by using the "colMeans"-Function. With one set no problem .. but with the other. My input : > struktur <-

Hello, I've got an array defined as y <- rnorm(3000), dim(y) <- c(3, 1000). I'd like to produce a 1000-element vector z that is the mean of the corresponding elements of y (like z[1,1] <- mean(y[1,1], y[2,1], y[3,1])), but being new to R, I'm not sure how to do this for all elements at once (or, at least, simply). Any help is appreciated. Thanks, Tom

Dear R users, the colSums function from the base package returns zero if there are no values in a range to be summed over (after removing missing values with na.rm = TRUE). Is there some way to make it return NA in that case just like colMeans does? Or any other function or some good way to achieve that? Any ideas are be welcome. Regards, Martin Ivanov

Hi all, I would like to know if it is posible by, someway, to get colMeans from a data.frame with numeric as well as character data, dispersed all over the object. Note that I would like to get colMeans neglecting character data. I am really in need of some function proceeding in that way… All the best Diogo André Alagador [[alternative HTML version deleted]]

Hi, Currently, the "base" has colSums, colMeans. It seems that it would be useful to extend this to also include colMin, colMax (of course, rowMin and rowMax, as well) in order to facilitate faster computations for large vectors (compared to using apply). Has this been considered before? Please forgive me if this has already been discussed before. Thanks, Ravi Ravi Varadhan, Ph.D.

Hi people, If there is a fn "colMeans" why isn't there a "colSDs" etc Thanks, Phil. -- Philip Rhoades Pricom Pty Limited (ACN 003 252 275) GPO Box 3411 Sydney NSW 2001 Australia Mobile: +61:0411-185-652 Fax: +61:2:8923-5363 E-mail: pri at chu.com.au -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I read from external data source containing several columns. Each column represents value of a metric. The columns are time series data. I want to sort the resulting dataframe such that the column with the largest mean is the leftmost column, descending in colMean values to the right. I see many solutions for sorting rows based on some column characteristic, but haven't found any

[text file is also attached in case you find the format of email difficult to understand] Dear useRs,You must all the planning for the christmas, but i am stucked in my office on the following issue i had a file containg information about station name, year, month, day, and discharge information. i opened it by using following command > dat1<-read.table("EL.csv",header=TRUE,

Dear all, I am currently analyzing count data from a hierarchical design, and I?ve tried to follow the suggestions for a correct estimation of p-values as discusssed at R-Wiki (http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests&s=lme%20and%20aov). However, I have the problem that my model only consists of parameters with just 1 d.f. (intercepts, slopes), so that the

Is it possible to compute the variance of every column in a matrix without a loop? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch

In a newly downloaded version (today) of R-2-4-0alpha, with all packages from CRAN also installed today, I get: > library(Matrix) Erro en loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'Matrix' methods specified for export, but none defined: BIC, anova, coef, confint, deviance, fitted, fixef, formula, head, lmer, logLik, mcmcsamp, plot,

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Dear All, After upgrading to R-2.4.0-dapper2 (my system is ubuntu 6.06 LTS), I often met problems when loading some packages like Matrix. Here is the details: > library(Matrix) Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'Matrix' methods specified for export, but none defined: Arith, Math, Math2, +, %*%, Schur, as.matrix, chol,

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

Dear List, I have some problem with my simulation code. Here is output from R: > sim.sp <- function(data,CM,n,N) + { + C <- matrix(rep(NA,N),ncol=1) + for(i in 1:N) + { + j <- n + xx <- which(colSums(CM[j,])==1) + V <- names(xx) + V <- paste(V, collapse="+") + V <- paste("SBA~", V) + rd <- round(nrow(data)*(2/3)) + d <-

(To the list moderator: I just subscribed to the list. Apologies for not having done so longer before trying to post.) Hi all, I am currently using lmer to analyze data from an experiment with a single fixed factor (treatment, 6 levels) and a single random factor (block). I've been trying to follow the online guidance for estimating p-values for parameter estimates on these and other

Hi all I have a 3d array containing missing values. > Xa , , 1 [,1] [,2] [1,] 1 3 [2,] NA 4 , , 2 [,1] [,2] [1,] 5 7 [2,] NA NA , , 3 [,1] [,2] [1,] 9 11 [2,] 10 12 I want to replace the missing values with the mean, but the mean of each 'page' in the array (wrong terminology I'm sure). So - for the array above - [2,1,2] and

I have a fair bit of experience with S-Plus and have been asked to port some of my S-Plus bootstrapping functions to R, to which I am relatively new, Needless to say, I've run into some problems. In particular, I need to perform bootstrap resampling of the colMeans function using a moving blocks bootstrap, where the blocks are row indices. It's easy to build a function that allows me

Hello, Another way would be to use ?replicate and ?colMeans. set.seed(2511) # Make the results reproducible fun <- function(n){ f <- function(){ a <- runif(5, 1, 10) b <- runif(5, 10, 20) colMeans(cbind(a, b)) } replicate(n, f()) } fun(10) Hope this helps, Rui Barradas On 1/30/2018 8:58 AM, Daniel Nordlund wrote: > On 1/29/2018