similar to: Problem in weighting standard deviations

Is it in bad form to double post to StackOverflow and R-help? Apologies if so. Here's my task: I've got a matrix of means like so means<-matrix(1:10,nrow=2) colnames(means)<-c("a","b","c","d","e") and a matrix of standard deviations like so sds<-matrix(seq(0.1,1,by=0.1),nrow=2)

Hello all, I am new to xtable. I have several datasets in the form of matrices. Consider the following two simple datasets which are 2 x 3 matrices. The rows in both matrices have the same meaning. For example the first row of both matrices are variable 1 and the second row of both matrices are variable 2. dataset1 = matrix( c(1,2,3,4, 5, 6 ), 2 , 3) dataset2 = matrix( c(4,3,5,10, 1, 0), 2, 3)

Hi all, I am trying to find sd of my rows in a matrix and i get column sd inspite of extracting rows. I tried to do the sqrt(var(x)) but that did'nt work as well, Here is my data genes 15 24 63 40 25 42 46 35 23 53 37 45 30 37 50 55 40 51 30 48 x<-sd(genes[1:5,]) y<-sqrt(var(genes[1:5,])) I get 4 sds for the 4 columns instead of 5 sds for my 5 rows. Thanks you in advance.

Hello, svyvar from the survey package computes variances (with standard errors) from survey design objects. Is there any way to compute standard deviations and their standard errors in a similar manner? Thanks a lot, Sebastian

Dear R-users, I am new to the list and I would like to submit (probably!!!!) a stupid question: I found in a paper a reference to a t-test for the evaluationg the difference between the standard deviations of 2 samples. This test is performed in the paper but the methodology is not explained and any reference is reported. Does anyone know where I can find references to this test and if it is

Hello,   I would like to do a meta-analysis with the package « metafor ». Ideally I would like to use a mixed model because I’m interested to see the effect of some moderators. But the data set I managed to collect from literature presents two limits.   -         Firstly, for each observation, I have means for a treatment and for a control, but I don’t always have corresponding standard

Hi, I am doing double for loops to calculate SDs with some weights and wondering if I can get rid of the outer for loop as well. I made a simple examples which is essentially what I am doing. Thanks for your help! -Young #------------------------------------------------------ # wtd.var is Hmisc package # you can replace the 3 lines inside for loop as # sdx[i,] =

Hi R-helpers, I have been struggling with calculating row and column statistics, e.g. standard deviation. I know that > datac$Mean<-rowMeans(datac,na.rm=TRUE) will give me row means. I have tried to replicate those row means with the apply function: > datac$Mean2<-apply(datac,2,mean) so that I can replace the function argument with "sd" (instead of mean) to get standard

Dear R-community, I'm struggling with a paper that reports only fragmented results of a 2by2by3 experimental design. However, Means and SDs for all cells are given. Does anyone know a package/function that helps computing an ANOVA with only Means and SDs as input (resulting in some sort of effect size and significance test)? The reason why I'm interested is simple: I'm conducting a

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Suppose I have a Variance-covariance matrix A. Is there any fast way to calculate correlation matrix from 'A' and vice-versa without emplying any 'for' loop? [[alternative HTML version deleted]]

Hello everyone, Peter (see my earlier post) recommended the following script for finding the means and standard deviations and putting them in table form. However, I would like the standard deviations under the means in brackets. Can anyone check this code to see how this can be adjusted? library(xtable) dataset1 = matrix( c(1,2,3,4, 5, 6 ), 2 , 3) dataset2 = matrix( c(4,3,5,10, 1, 0), 2, 3)

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

Hi I'm trying to create tables of means, standard deviations and numbers of observations (i) for each laboratory (ii) for each batch number (iii) for each batch at each laboratory for the attached data. I created these functions: summary.aggregate <- function(y, label, ...) { temp.mean <- aggregate(y, FUN=mean, ...) temp.sd <- aggregate(y, FUN=sd, ...) temp.length <-

Peter, This is a highly structured text. Just for the discussion, I separate the building blocks, where (D) and (E) and (F) are new: BEGIN OF TEXT -------------------- (A) Non-?NULL? ?weights? can be used to indicate that different observations have different variances (with the values in ?weights? being inversely proportional to the variances); (B) or equivalently, when the elements of

Dear All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

Hello: My problem is that I have a data frame of means, and a data frame of standard deviations which match up to each mean. I have been trying to create 500 random numbers in a given dataset for each mean/sd combination, but I am only able to generate the last value in each data set to create one dataset (there should be 28 in all). Examining my code, what can I change to generate 28 datasets

I have a list of 20 values. The first time through a loop I want to find the mean and stnd.dev. of the first two values; the second time through the loop I want to find the mean and stnd. dev. of the first 3 values, etc. until the last time through the loop I want to find the mean and stnd. dev. of all 20 values, so I end up with 19 means and stnd. deviations. How would I construct such a loop?

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

Dear R-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions - the Rsquared of