similar to: Version of L-BFGS-B used in optim etc

Hi, Mark et al.: Thanks, Mark. Three comments: 1. Rvmmin was one of the methods I tried after Ravi directed me to optimx. It returned NAs for essentially everything. See my email of this subject stamped 4:43 PM Central time = 21:43 UTC. 2. It would be interesting to know if the current algorithm behind optim and optimx with

Hi there, If I do an lm, I get p-vlues as p-value: < 2.2e-16 Suppose am interested in exact value such as p-value = 1.6e-16 (note = and not <) How do I go about it? stephen

I don't know of any R package that can match all the functionality of LEM eg fitting equality constraints to model parameters a la LISREL. WRT dumping tables, I would have thought that as.data.frame.table does pretty much what you want, [not tested] newtab <- as.data.frame(table(a,b,c)) cat("dim\n") for(i in seq(1, ncol(newtab)-1) { cat(nlevels(newtab[,1]," ") }

>>>>> Spencer Graves <spencer.graves at prodsyse.com> >>>>> on Sat, 8 Oct 2016 18:03:43 -0500 writes: [.............] > 2. It would be interesting to know if the > current algorithm behind optim and optimx with > method='L-BFGS-B' incorporates Morales and Nocedal (2011) > 'Remark on ?Algorithm 778:

A bit late, but you might like to look at http://www.qimr.edu.au/davidD/polyr.R Regarding the original posters queries: You can analyse polychoric correlations as if they were Pearson correlations using standard software (eg sem), and this usually doesn't do too badly, or go to AWLS (Browne) in LISREL etc, or ML analysis of the full multidimensional contingency table using programs such as

Hello: The development version of Ecdat on R-Forge contains a vignette in which optim(?, method=?L-BFGS-B?) stops with an error message while violating the lower bound. To see all the details, try the following: install.packages("Ecdat", repos="http://R-Forge.R-project.org") Then do "help(pac=Ecdat)" -> "User guides, package

> Date: Mon, 20 Feb 2006 16:43:55 -0600 > From: Aldi Kraja <aldi at wustl.edu> > > Hi, > Using package gclus in R, I have created some graphs that show the > trends within subgroups of data and correlations among 9 variables (v1-v9). > Being interested for more details on these data I have produced also the > var-covar matrices. > Question: From a pair of two

I couldn't resist adding a more literal answer unback <- function(x) { chars <- unlist(strsplit(deparse(x),"")) chars <- chars[-c(1,length(chars))] paste(gsub("\\\\","/",chars),collapse="") } unback("\n") | David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph:

I couldn't resist adding a more literal answer unback <- function(x) { chars <- unlist(strsplit(deparse(x),"")) chars <- chars[-c(1,length(chars))] paste(gsub("\\\\","/",chars),collapse="") } unback("\n") | David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph:

If I run an analysis which generates statistical tests on many SNPs I would naturally want to get more details on the most significant SNPs. Directly from within R one can get the information by loading RSNPer (from Bioconductor) and simply issuing a command SNPinfo(2073285). Unfortunately, the command cannot handle a vector and therefore only wants to do one at a time. I tried the lapply and

Hi. I don't know if this a bug or just annoying to me: > x <- c(1,2,3,NA) > table(x, exclude=NULL) x 1 2 3 <NA> 1 1 1 1 > table(factor(x), exclude=NULL) 1 2 3 1 1 1 I don't think many people use factor(x, exclude=NULL): it is not the default handling of character data by read.table(). Cheers, David Duffy. -- | David Duffy (MBBS PhD)

Hi, I am looking to do some quantitative genetic analyses using animal models and was wondering if someone could suggest an appropriate package in R. It would help if it was similar to the ASReml genetic analyses software. Thanks, Deepa Senapathi Deepa Senapathi Centre for Agri-Environmental Research (CAER) School of Agriculture, Policy & Developement. University of Reading

Does anyone know if the sib TDT has been implemented in R 1. Spielman, R.S., and Ewens, W.J. (1998) A sibship test for linkage in the presence of association: the sib transmission/disequilibrium test. Am J Hum Genet 62, 450-458 -- Farrel Buchinsky, MD Pediatric Otolaryngologist Allegheny General Hospital Pittsburgh, PA

Hi I was wondering whether anyone can provide any help or suggestions on the boostrap method for latent class analysis please. My main question is: can the bootstrap procedure sometimes be less precise than the non-bootstrap procedure when carrying out latent class analysis? I am asking this question because I carried out some analyses and when I *did not* use the bootstrap procedure I found

Dear all, I'm using the package "Genetics", and I'm interested in the computation of D' statistics for Linkage Disequilibrium, for which the LD() command has been realised. Unfortunately I don't find any reference on "how" the D' is computed by the LD() function. In the package documentation it is generally referred as "MLE" estimation, but

Full_Name: Rod Montgomery Version: 1.1.0 OS: Windows-95 Submission from: (NULL) (38.26.56.3) File R-1.1.0/src/appl/lbfgsb.c -- Comment in code says two papers describing underlying math are in ftp://ece.nwu.edu/pub/lbfgs/lbfgs_bcm/ but that directory seems not to exist. (Actually, the comment does not give a URL - it just says to look in that directory on that FTP server.) Paper [1] does

Hello All R Users, Function loglm() in library MASS can be cajoled to accomodate structural zeros in a cross-classification table. An example from Fienberg demonstrates how this can be done. My question is: Can the function glm() perform the same task? Can glm() estimate a log-linear model with fixed zeros like loglm()? Thanks for your help, Andrew ## Fienberg, The Analysis of Cross-Classified

Dear Colleagues, I am looking for a package or previous implemented R to subgroup and exaustively divide a vector of squence into 2 groups. For example: 1, 2, 3, 4 I want to have a group of 1, (2,3,4) (1,2), (3,4) (1,3), (2,4) (1,4), (2,3) (1,2,3), 4 (2,3), (1,4) ... Can someone help me as how to implement this? I get some imaginary problem when the sequence becomes large. Thanks much in

Hi all, I'm new to R. Having a functional background, I was wondering what's the idiomatic way to iterate. It seems that for loops are the default given there's no tail-call optimization. I'm curious to know whether there is a way to transform the following toy snippet into something that doesn't eat up gigabytes of memory (like it's for loop counterpart) using laziness:

If I do not make a mistake,the partial association model is an extension of log-linear model.I read a papers which gives an example of it.(Sloane and Morgan,1996,An Introduction to Categorical Data Analysis,Annual Review of Sociology.22:351-375) Can R fit such partial association model? ps:Another somewhat off-topic question.What's the motivations to use log-linear model?Or why use