similar to: Error in FrF2 example on Mac OS

Dear developeRs, I encounter the following problem: in the current version of my package FrF2, certain calls to a functioni do not work when package combinat is loaded, because function combn from combinat masks the function from utils that my package uses. I tried to solve this issue by importing function combn into the namespace of FrF2; I don't need to export it, I just want to use it

Hi R HELP, I consider the 2^3 factorial experiment described at page 177 of the book Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, J. Stuart Hunter, William G. Hunter (BHH2). This example use the following data in file BHH2-Data/tab0502.dat at ftp://ftp.wiley.com/ in /sci_tech_med/statistics_experimenters/BHH2-Data.zip run T C K y 1 1 -1 -1 -1 60 2

Dear all, I am working on a function formula.design that should automatically generate reasonable lm formulae for a number of different designs. All works well as long as all variables used are columns of the design data frame. For one function, I would like to incorporate a dummy variable for center points that is not a column of the design. Without this function, it would work like this (at

Hi, I'm trying to use commands in FrF2 and once I install FrF2 and use the console command of library(FrF2) it loads required packages and ends with: Loading Tcl/Tk interface ... > then when I enter another command (only got to DATA<-read.table without an enter or more text) I get a non-responsive R console window with this along the bottom of the console window executing:

hi all i know that one should try and limit the amount of looping in R programs. i have supplied some code below. i am interested in seeing how the code cold be rewritten if we dont use the loops. a brief overview of what is done in the code. ============================================== ============================================== ============================================== 1. the input

Dear developeRs, my package FrF2.catlg128 holds large catalogues and is supposed to gain additional ones. All the catalogues are intended for the user. So far, the catalogues were stored in the data directory, and LazyData was "no". I understand that this is not considered wise any more (if it ever was), so that I want to change to LazyData "yes" with the next release

I've been attempting to understand the one-sample run test for randomness. I've found run.test{tseries} and run.test{lawstat}. Both use a large sample approximation for distribution of the total number of runs in a sample of n1 observations of one type and n2 observations of another type. I've been unable to find R code to generate the exact distribution and would like to see how

Hi, I am new to bioconductor, trying to install KEGGSOAP package, but got warnings() when installing and error message when trying to load the package, can anyone suggest what went wrong? many thanks John > source("http://bioconductor.org/biocLite.R") Bioconductor version 2.11 (BiocInstaller 1.8.3), ?biocLite for help > biocLite("KEGGSOAP") BioC_mirror:

Hi Everyone, I found the doe.base package and the FrF2 package to do nice experimental planning and I'm very happy about this tool I was looking for such a long time. But I still try to find out how to add center points to a full factorial design. The FrF2-package has a center point option but is just supporting fractional designs? Is there a possibility to have center points within the

I am running a PCA, but would like to rotate my data and limit the number of factors that are analyzed. I can do this using the "principal" command from the psych package [principal(my.data, nfactors=3,rotate="varimax")], but the issue is that this does not report scores for the Principal Components the way "princomp" does. My question is: Can you get an

Dear All, In a package, I want to use PCA function. The structure I used follow this page: http://www.statmethods.net/advstats/factor.html. fit<-principle(mydata, nfactors=9, rotation=TRUE) or: result<-PCA(mydata) But I don't known why R language in my computer noticed: "not found principle", "not found PCA". I download and installed

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

Haj i try to perform a principal component analysis by using a tetrachoric correlation matrix as data input tetra <- tetrachoric (image_na, correct=TRUE) t_matrix <- tetra$rho pca.tetra <- principal(t_matrix, nfactors = 10, n.obs = nrow(image_na), rotate="varimax", scores=TRUE) the problem i have is to compute the individual factor scores from the pca. the code runs perfect

I am not an R-Head, hence I use nice utilities that integrate R into SPSS I have SPSS v24, R3.20 and R3.40 I have run IBM SPSS R Integration which requires linking to R3.20 I have installed R Factor v2.4.2 This package requires 'polycor' library Unfortunately, 'polycor' does not exist in R3.20 DATASET ACTIVATE DataSet1. *M?rio Basto, Jos? Manuel Pereira, IPCA *Required: SPSS 21

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and

Dear all, I am trying to conduct an enhanced version of factor analysis with a SPSS interface that allows to use R. This approach has been suggested in the recent article: Basto, M. and J.M. Pereira An SPSS R-Menu for Ordinal Factor Analysis. Journal of Statistical Software 46, pp. 1-29. My variables are ordinal-type and the tool of Basto allows to run polychoric correlations in the SPSS

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

Hi, I am trying to calculate Principal Component Scores per id per year using the psych package. The following lines provide the scores per obeservation pca = data.frame(read.table(textConnection(" id year A B C D 1001 1972 64 56 14 23 1003 1972 60 55 62 111 1005 1972 57 51 10 47 1007 1972 59 49 7 10 1009 1972 65 50 9 32 1011 1972 52 58 3 5 1013

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little