similar to: Re: Factor analysis of categorical or mixed categorical/continuousdata in

Are there any suitable routines that perform factor analysis of categorical or mixed categorical/continuous data in R? Stuart Dr Stuart Leask MA MB BChir (Cantab) MRCPsych Clinical Lecturer in Psychiatry, University of Nottingham Dept Psychiatry Duncan Macmillan House, Porchester Road, Nottingham. NG3 6AA. UK tel. 00 44 115 969 1300 xtn 40784 fax 0115 955 5352

Dear R-help group, I have a two-way ANOVA with two crossed random factors, no nesting. Each factor has three levels, resulting in 9 cells for the experiment. Each cell contains 10 repetitions. According to the ANOVA model I assume equal variances for all levels per factor. I would like to get REML-estimates for the variances of the two factors and moreover get confidence intervals for these

We're running rsync 2.5.7 on a Windows2000 server, in combination with cygwin/ssh. The server who receives the data is a Linux server. The amount of data from the Windows server is about 100 Gb. Represented by 532.000 files of different nature. Mostly doc, ppt and xls files. It takes about 2 hours to create only the file list. Is the amount of data/files to big, should I segmentate the

Dear all, I am trying to read in big amounts of data with scan. It's only one variable, numeric values, separated by tabs,.. and it's many of them. So I was thinking that I could use the skip option and read in 100000 values at a time - but skip doesn't work, probably because I don't have line breaks in the txt file. So any value specified for skip makes the scan function jump to

Dear all, I am attempting to model some one-dimensional data using a mixture model of non-central Student's t distributions. However, I haven't been able to find any R package that provides this functionality. Could there be a way to "manipulate" the EM algorithms from the mixdist or mixtools package to fit the model, or do you have any other suggestions? If anyone could help

Dear useRs, A new package 'drm', version 0.5-4, is available on CRAN. The drm package provides functions for marginal regression analysis of repeated (or otherwise clustered) binary, ordinal and nominal responses. This package can be considered as a likelihood-based alternative to GEE approach for marginal regression. In addition to regression modelling, several temporal and latent

Dear useRs, A new package 'drm', version 0.5-4, is available on CRAN. The drm package provides functions for marginal regression analysis of repeated (or otherwise clustered) binary, ordinal and nominal responses. This package can be considered as a likelihood-based alternative to GEE approach for marginal regression. In addition to regression modelling, several temporal and latent

Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: 7bit Received-SPF: none (hypatia: domain of sokamp at web.de does not designate permitted sender hosts) X-Virus-Scanned: by amavisd-new at stat.math.ethz.ch X-Spam-Checker-Version: SpamAssassin 2.63 (2004-01-11) on hypatia.math.ethz.ch X-Spam-Level: *** X-Spam-Status: No, hits=3.2 required=5.0

Hi, trying to install a package containing C code and requiring non-default configure argument settings the incantation (this has worked for R <= 2.8.1 on the same architectures) R CMD INSTALL --configure-args="--with-opt1 --with-opt2" packname does always result in a warning Warning: unknown option '--with-opt2' and consequently the option is ignored. Reverting the order

Hi! Is it possible to avoid this error condition by specifying a command line option? I haven't found an obvious one in the manual. Background: We are using rsync for a live backup of our home directories. Emails are stored in Maildir format within the homedirectories. The rsync process is running for some time and in the meantime emails get deleted because users still have IMAP access. The

Hi, after updating R 2.5.0 devel yesterday we today observed many new unexpected failures in our daily package build and test system runs, which can be traced to recent changes in the implementation in try() (as noted in NEWS). Investigating this new implementation I come across an issue in conjuntion with using S4 classes and methods. try(expr) does not return an object with attribute

Dear R developers, The trace of the hat matrix H~(n,n) is computed as follows: tr(H) = tr(BS^-1B') = tr(S^-1B'B) := tr(X) = sum(diag(X)) with B~(n,p), S~(p,p). Since p is of the order 10^3 but S is sparse I would like to employ Taucs linear solver ( http://www.tau.ac.il/~stoledo/taucs/ ) on SX = B'B. (Further improvement by implying a looping over i=1,...,p, calling

Thanks. I was "believing" an Excel import of the file - as you say, a re-import into R still has 333 columns. I shall look elsewhere for the cause of the mysterious truncation. Stuart > ----- Original Message ----- > From: "Thomas Lumley" <tlumley at u.washington.edu> > To: "Stuart Leask" <stuart.leask at nottingham.ac.uk> > Cc:

Dear R-help listers, I am a new convert to R. I am trying to use a r package to conduct latent class analysis as a triangulation check of my cluster analysis using the cluster package in R. I have about 30 cases and 6 indicators, some of which are binary indicators and others are ratio-level variables (percentages). I looked around for information in flexmix, lca, and poLCA, and couldn't find

There appears to be a bug in RUnit. Given a testsuite testsuite.math, say, when I run: runTestSuite(testsuite.math) this works fine, provided there are no extraneous files in the unit test subdirectory. But if there are any Emacs temp files (with names that end with '~') then runTestSuite gets confused and tries to run functions from the temp files as well. [[alternative HTML version

Brian Ripley pointed out that there is already an R function scale() that does the work of my standardize(). -Greg -----Original Message----- From: Warnes, Gregory R Sent: Friday, January 18, 2002 9:38 AM To: 'Stuart Leask' Cc: 'r-help at stat.math.ethz.ch' Subject: RE: z-scores for different factor levels Hi Stuart, I often use this small function standardize <-

Removing rows with NAs, using na.omit(), doesn't seem to be working for me. Dataset: > str ( ex10s ) 'data.frame': 2189576 obs. of 5 variables: $ LOPNR : int 58 58 58 58 64 64 64 64 64 64 ... $ DIAGNOS: Factor w/ 173 levels "F20","F200","F2000",..: 128 128 128 128 105 105 105 160 105 105 ... $ X_DATE : int 20060821 20061207 20080102 20090904

Dear List Has anyone used R to distnguish between alternative forecasting models? In particular is the Diebold Mariano test available for use within R. Any assistance would be greatly appreciated. Graham Leask Lecturer in Strategy Economics & Strategy Group Aston University Aston Triangle Birmingham B4 7ET Tel: 0121 204 3150 E Mail: g.leask at aston.ac.uk

Has anyone implemented Tibrishani's gap statistic in R or S plus? If so I would greatly appreciate the relevant script file. Any help will be much appreciated Kind regards Dr Graham Leask Economics and Strategy Group Aston Business School Aston University Aston Triangle Birmingham B4 7ET Tel: Direct line 0121 204 3150 email g.leask@aston.ac.uk [[alternative HTML version deleted]]

Hello all, I'm using R2.7.0 (on Windows 2000) and I'm trying do run a robust regression on following model structure: model = "Y ~ x1*x2 / (x3 + x4 + x5 +x6)" where x1 and x2 are both factors (either 1 or 0) and x3.....x6 are numeric. The error code I get when running rlm(as.formula(model), data=daymean) is: error in rlm.default(x, y, weights, method = method, wt.method =