similar to: Strange behaviour with Homals

Dear R users and experts, I am using Homals to perform categorical PCA on some survey data. The documentation is a bit obscure, however, I followed the examples. X is a matrix with ordinal 1-5 Likert scale entries on 24 questions from about 2500 respondents. So I run X.nlpc = homals(X, rank = 1, level = "ordinal", ndim = 10) to get the first 10 pc's (the number of dimensions is

Function homals() is not part of R. It appears to be in a contributed package `homals', but you did not say so. To quote the FAQ Bug reports on contributed packages should be sent first to the package maintainer, and only submitted to the R-bugs repository by package maintainers, mentioning the package in the subject line. I have closed this bug report on R-bugs. On Fri, 2 Jul

The homals package http://www.jstatsoft.org/v31/i04 will get a major programming overhaul. This will take some time, but what's a few years on a 40-year project. Suggestions from the audience are welcome. homals() has a core loop over the m variables in which 1. tapply is used to compute category quantification (centroids) 2. category quantifications are then adjusted to satisfy the

The homals package http://www.jstatsoft.org/v31/i04 will get a major programming overhaul. This will take some time, but what's a few years on a 40-year project. Suggestions from the audience are welcome. homals() has a core loop over the m variables in which 1. tapply is used to compute category quantification (centroids) 2. category quantifications are then adjusted to satisfy the

Hello The Homals package and its plot options are excellent. However, I am unable to manipulate the colour in the plots. In a call such as: plot(mc_analysis, plot.dim = c(1,3), plot.type = "jointplot", col = 1) this should be straightforward - but I can't seem to effect the plotted colours I have tried various combinations for "col" commands in other plot packages I

homals-0.9.0 is on CRAN -- by Jan de Leeuw and Patrick Mair This package implements the methods discussed in Gifi, Nonlinear Multivariate Analysis, Wiley, 1990. In the Gifi terminology it covers homals, princals, canals, morals, criminals, and overals. The R implementation fills several gaps in Gifi, adding multiple ordinal, numerical, and polynomial data transformations. Differences with

homals-0.9.0 is on CRAN -- by Jan de Leeuw and Patrick Mair This package implements the methods discussed in Gifi, Nonlinear Multivariate Analysis, Wiley, 1990. In the Gifi terminology it covers homals, princals, canals, morals, criminals, and overals. The R implementation fills several gaps in Gifi, adding multiple ordinal, numerical, and polynomial data transformations. Differences with

Hi All is there "homals" package prepared for win32? kind regards, Valery A.Khamenya --------------------------------------------------------------------------- Bioinformatics Department BioVisioN AG, Hannover

Dear colleagues, I would like to run a cluster analysis on a number of variables. They are Likert Scale (0 to 10), but they also have a "Don't know' option at the end of the scale. Apparently, with the 'Don't Know' option in place, they cannot be considered to be linear or ordinal. How can these variables be analysed using R? One option would be to use poLC and treat

Dear R developers, While improving duplicated.array() and friends and developing equivalents for the new ff package for large datasets I came across two questions: 1) is it safe to use duplicated.default(), unique.default() and match() on arbitrary lists? If so, we can speed up duplicated.array and friends considerably by using list() instead of paste(collapse="\r") 2) while

Hi, is there a function in R already doing what I try to do below: # Let 'x' be an array with *any* number of dimensions (>=1). x <- array(1:24, dim=c(2,2,3,2)) ... x <- array(1:24, dim=c(4,3,2)) i <- 2:3 ndim <- length(dim(x)) if (ndim == 1) y <- x[i] else if (ndim == 2) y <- x[i,] else if (ndim == 3) y <- x[i,,] else ... and so on. My current

I want to call dll from R but encounter problem in compiling the fortran program. First I try "Rcmd shlib prog.f", it failed and warning: make[1]: `libR.a' is up to date. make: *** No rule to make target `'prog.o', needed by `prog.a'. stop. Then I try to compile it by absoft fortran compiler, it works and produces prog.dll. But when this routine is called in R, it

Dear guRus, I am doing a logistic regression using restricted cubic splines via rcs(). However, the fitted probabilities should be nondecreasing with increasing predictor. Example: predictor <- seq(1,20) y <- c(rep(0,9),rep(1,10),0) model <- glm(y~rcs(predictor,n.knots=3),family="binomial") print(1/(1+exp(-predict(model)))) The last expression should be a nondecreasing

Hello, I am trying to call a FORTRAN subroutine from R. The Fortran code is @: http://lib.stat.cmu.edu/apstat/206 It performs a bivariate isotonic regression on a rectangular grid (m X n) matrix. I used the g77 compiler and successfully created a dll file and it also loads successfully from R. But somehow the programs fails to run properly. (I do get the correct result when I compile the

A mi no me dio problemas. Intenta instalar nuevamente RCurl. > library(devtools) > install_github('likert','jbryer') Installing github repo likert/master from jbryer Downloading likert.zip from https://github.com/jbryer/likert/archive/master.zip Installing package from /tmp/RtmpJuHsLY/likert.zip arguments 'minimized' and 'invisible' are for Windows only

Is it just my installation or bug in 1.9.0 ? The same thing works fine in 1.8.1 Best regards, Ryszard # R-1.9.0 library(pls.pcr) nr <- 8; ndim <- 2 x <- matrix(rnorm(nr*ndim), nrow=nr) y <- as.matrix(x[,1]) for (i in 2:ndim) y <- y + x[,i] y <- y + rnorm(length(y)) m <- pls(x,y,validation='CV') # Error in paste(ncomp, "LV's") : Argument

I was wondering if it is possible to do the following in a smarter way. I want get the mean value across the columns of a matrix, but I want to do this on subrows of the matrix, given by some vector(same length as the the number of rows). Something like nObs<- 6 nDim <- 4 m <- matrix(rnorm(nObs*nDim),ncol=nDim) fac<-sample(1:(nObs/2),nObs,rep=T) ##loop trough different

Hola, Puedes hacerlo sin devtools. Descargas el ZIP del repositorio, lo descomprimes, instalas el paquete que hay en CRAN para tener todas las dependencias instaladas y finalmente instalas el paquete en la versión de GitHub. tmp <- paste0(tempdir(), '/likert.zip') download.file('https://github.com/jbryer/likert/archive/master.zip', destfile=tmp,

I'm creating a stacked bar chart using the likert command in the HH package. My data are in a data frame, with two numeric variables and a categorical variable, I can't get likert to use the column containing the categorical variable as a my y axis label. Here is a quick example: library(HH) #my data are:

Dear all, I hope to be the clearest I can. Let's say I have a dataset with 10 variables, where 4 of them represent for me a certain phenomenon that I call Y. The other 6 represent for me another phenomenon that I call X. Each one of those variables (10) contains 37 units. Those units are just the respondents of my analysis (a survey). Since all the questions are based on a Likert scale, they