similar to: Comparison of SAS & R/Splus

My thanks to Drs. Armstrong, Bates, Harrell, Liaw, Lumley, Prager, Schwartz, and Mr. Wang for their replies. I have pasted my original message and their replies below. After viewing http://www.itl.nist.gov/div898/strd/ as suggested by Dr. Schwartz, it occurred to me that it might be educational to search for some data repositories on google. I was able to find some,though I'm sure many of

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XLSolutions Corporation (www.xlsolutions-corp.com) is proud to announce: (1) Introduction to R/S+ programming: Microarrays Analysis and Bioconductor *** San Francisco / July 17-18, 2006 *** *** Chicago / July 24-25, 2006 *** *** Baltimore / July 27-28, 2006 *** *** Raleigh / July 17-18, 2006 *** ***

Hi All I am really very interested in starting to use R within our company. I particularly like the open source nature of the product. My company is a medical research company which is part of the University of London. We conduct contract virology research for large pharma companies. My question is how do we validate this software? I wonder if anyone else has had the problem and might be able to

Does anybory knows any work comparing R with other (charged) statistical softwares (like Minitab, SPSS, SAS)? I work in a brasilian government bureau and I intend to use R as our preferable statistical software, but I have to show it's as good as the others. I also intend to use Weka, and for this one I have the same problem. Can anyone help me? Thanks René M. Raupp e-mail: rener@mpdft.gov.br

Folks: I thought the following excerpt from Bruce McCullough's post would be a good candidate for the R fortunes package -- except that it's about Excel, not R! So I nominate it... but leave it to others to say whether it's really "qualified" to be nominated. ---- "The idea that the Excel solver "has a good reputation for being fast and accurate" does not

Hello Everyone,   I’m a SAS user who has recently become interested in sequential clinical trials designs. I’ve discovered that the SAS based approaches for these designs are either too costly or are “experimental.” So now I’m looking for alternative software. Two programs that seem promising are SPLUS Seqtrial and R.   I recently obtained a 30 day trial for the SPLUS Seqtrial add-on and have

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

I'd like to estimate an ordinal logistic regression with a random effect for a grouping variable. I do not find a pre-packaged algorithm for this. I've found methods glmmML (package: glmmML) and lmer (package: lme4) both work fine with dichotomous dependent variables. I'd like a model similar to polr (package: MASS) or lrm (package: Design) that allows random effects. I was

My institute has been heavily dependent on SAS for the past while, and SAS is starting to charge us a very deep amount for license renewal. Since we are a non-profit organization that is definitely not sustainable. The team is brainstorming possibility of switching to R, at least gradually. I am talking about the entire institute with considerable number of analysts using SAS their entire

Hi, this is not an R-help post, but I found this extract below that was written by a leading mathematician back in 1999 when he was talking about statistics and computing. I found it interesting to share and I ask your opinion do you think this still holds today or things have changed? Thanks. ?...we would also like to mention that in our opinion Mathematica provides an excellent and indeed

Hello there, I spent all day yesterday trying to get a small data set from Splus into R, no luck! Both, Splus and R, are run on a 64-bit RedHat Linux machine, the versions of the softwares are 64-bit and are as what follows: Splus: TIBCO Software Inc. Confidential Information Copyright (c) 1988-2008 TIBCO Software Inc. ALL RIGHTS RESERVED. TIBCO Spotfire S+ Version 8.1.1 for Linux 2.6.9-34.EL,

I'm using FC4 and R 2.3.1 to fit a mixed effects logistic regression. The response is 0/1 and both the response and the age are the same for each pair of observations for each subject (some observations are not paired). For example: id response age 1 0 30 1 0 30 2 1 55 2 1 55 3 0 37 4 1 52 5 0 39 5 0 39 etc. I get the

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that

Hi Folks, I'm seeking confirmation of something which is probably true but which I have not managed to find in the documentation. I have a binary response y={0.1} and a variable x and have fitted a probit response to the data with f <- glm( y~x, family=binomial(link=probit) ) and then, with a specified set of x-value X I have used the predict.glm function as p <- predict( f, X,

Hi everybody: I?m trying to rewrite some routines originally written for SAS?s PROC NLMIXED into LME4's glmer. These examples came from a paper by Nelson et al. (Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Models with Nonnormal Random Effects - 2006). Firstly the authors fit a Poisson model with canonical link and a single normal random effect bi ~ N(0;Sigma^2).The

Hi there, I am using SAS Proc NLMIXED to maximize a likelihood with multivariate normal random effects. An example is the two part random effects model for repeated measures semi-continous data with a cluster at 0. I use the "model y ~ general(loglike)" statement in Proc NLMIXED, so I can specify a general log likelihood function constructed by SAS programming statements. Then the

I'm estimating two models for data with n = 179 with four clusters (21, 70, 36, and 52) named siteid. I'm estimating a logistic regression model with random intercept and another version with random intercept and random slope for one of the independent variables. fit.1 <- lmer(glaucoma~(1|siteid)+x1 +x2,family=binomial,data=set1,method="ML",

Hello, ? I believe that in R, it is not possible to analyze mixed effect-models when the distribucion is not gaussian (p.e. binomial or poisson), isn't? ? Somebody can suggest me alternative? ? thanks ? xavi ? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",