similar to: New package: R to LaTeX Univariate Analyses

Hi all Well, first: happy new year... Second: I write a function in R that might interest some other people. On the other hand, I am closer to beginners than experts; I don't know how valuable my code is. I don't know how long it will take to me to create a library and I don't know if it's worth to. So before starting this long process, I would like some advices, both on the

Hi again Sorry, in looking again at sammon and isoMDS I see that they seem to do exactly what I want, except that they are non-metric, which means, as I understand it, that they relate the rank orders of the variables rather than the actual distances. Could I use these non-metric MDS packages even if my distances are metric? Thanks Karen -- Karen Kotschy Centre for Water in the Environment

Hi all, In the package rtlu, I use the function savePlot. It is convenient since it let the user decide in which graphic format he wants his graph to be export. But when I run R CMD check, I get the following message : > rtlu(V1,fileOutput="First.tex",textBefore="\\section{Variable 1 to 3}",graphName="V1") Error in savePlot(filename = nomBarplot, type = type)

Hi all, I have relatively small dataset on which I would like to perform a PCA. I am interested about a package that would also combine a method for determining the number of components (I know there are plenty of approaches to this problem). Any suggestions about a package/function? thanks, Nick -- View this message in context:

Hi. I have data which i would want to assess the degree of agreement between two assays, e.g., to evaluate reproducibility or for inter-rater reliability. I have used the Pearson product-moment correlation coefficient. It looks good ranginging between 0.90 to 0.998. Though this looks good. I am told the Concordance correlation coefficient will give a better picture of how reproducible the assay

I'm in this situation: factorlabels <- c("School", "College", "Beyond") with data for 8 families: education.man <- c(1,2,1,2,1,2,1,2) # Note : no "3" values education.wife <- c(1,2,3,1,2,3,1,2) # 1,2,3 are all present. My goal is to create this table: School College Beyond

I work for a Investment group with a very extensive training program and we are having our new hires take a statistics course at University of Chicago where they have to complete some assignments with R. I was wondering if there are any online tutorials that exist where we could get our participants comfortable with R before the class itself? I appreciate any help at all. Thanks, Matt Maxon

Dears, I'm a college student and In doing my statistics homework. I use R with SEM package as my tool for sem analysis, but my teacher told me AMOS is more suitable for such analysis. Could someone help tell me whether it is true that some commercial software is better accepted in academic fields? Sorry if I should not post such topics here. -- Best Regards, Reeyarn T. Lee Accounting

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2.

Greetings R users, My goal is to generate quartile groups of each variable in my data set. I would like each experiment to have its designated group added as a subsequent column. I can accomplish this individually with the following code: brks <- with(data_variables, cut2(var2, g=4)) #I don't want the actual numbers, I need a numbered group data$test1=factor(brks,

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565

Why am I getting a wrong result for quartiles? here is my code: > cbiomass = c(910, 1058, 929, 1103, 1056, 1022, 1255, 1121, 1111, 1192, > 1074, 1415) > summary(cbiomass) > IQR(cbiomass) The result R gives me is: For the summary > Min. 1st Qu. Median Mean 3rd Qu. Max. 910 1048 1088 1104 1139 1415 For IQR > 91.25 ********* The true Q1 is 1039

The Child and Adolescent Mental Health Services (CAMHS) Evidence Based Practice Unit (EBPU) is a dynamic, expanding and friendly academic and service development unit, currently consisting of 19 people including: researchers, clinicians and service development specialists (http://www.ucl.ac.uk/clinical-psychology/EBPU/). It is part of University College London (UCL) and the Anna Freud Centre

The CAMHS EBPU is a research and training unit that is part of University College London (UCL) and the Anna Freud Centre (a registered charity dedicated to excellence in child and adolescent mental health). Within the unit sits the central team for the CAMHS Outcome Research Consortium (CORC) ? a national collaboration between child and adolescent mental health services (CAMHS) across the UK who

>[Rd] boxplot() defaults {was "boxplot in extreme cases"} >Martin Maechler maechler at stat.math.ethz.ch >Mon Nov 8 10:36:42 CET 2004 > > AndyL> Try: > > AndyL> x <- list(x1=rep(c(0,1,2),c(10,20,40)), > x2=rep(c(0,1,2),c(10,40,20))) > AndyL> boxplot(x, pars=list(medpch=20, medcex=3)) > > AndyL> (Cf ?bxp, pointed to from

Respected R Users, I looking for help with generating theoretical confidence regions for any of non-symmetric bivariate statistical distributions (bivariate Chi-squared distribution<Wishart distribution>, bivariate F-distribution, or any of the others). I want to to used it as a benchmark to compare a few strategies constructing confidence regions for non-symmetric bivariate data. There is

_____ De : Pousset [mailto:maud.pousset@noos.fr] Envoyé : mardi 4 juillet 2006 18:38 À : 'r-help@stat.math.ethz.ch' Objet : Latent Class Analysis Hello everybody, I am working on latent class analysis and have already used the ‘R’ function « lca » (in the e1071 package). I ‘ve got interesting results but I can’t simply find out the methodology used by this routine : 1) What

Hello everybody, This is a bug involving functions in core R package: graphics::hist.default, grDevices::nclass.FD, and base::pretty.default. It is not yet on Bugzilla. I cannot submit it myself, as I do not have an account. Could somebody else add it for me, perhaps? That would be much appreciated. Kind regards, Sietse Sietse Brouwer Summary ------- Floating point errors can cause a data

I just got the same error message with > sessionInfo() R version 3.4.0 (2017-04-21) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS Sierra 10.12.4 Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

Hemant's problem is that the indicators are not distributed uniformly. With a uniform distribution, categorization gives a reasonably optimal separation of cases. One approach would be to drop categorization and calculate the overall score as the mean of the standardized indicator scores. Whether this is an option I do not know. I did offer an "eyeball" set of breaks in a previous