R AnalyticFlow

R AnalyticFlow seems to be a nice tool to have a good overview over the analysis. The same kind of mode is available in Orange and SAS Enterprise Guide. I have not tried it yet, though. Does anyone have any experiences with it?

Update on 2010-07-31: there is also Red-R, which is a "sort of mixture" of R and Orange.

R AnalyticFlow
Orange

SAS Enterprise Guide

SAS goes with sparse matrices

SAS has introduced experimental procedure HPMIXED (High Performance MIXED) in version 9.2. This is a welcome addition and now SAS could probably solve the problem I encountered lately with a large mixed model described here. I really like R and its community (which is getting bigger and bigger and more and more connected to other communities!), but I must say that for me SAS has made quite some important moves lately - see here, here, and here.

Slick spline plot in SAS

Here is an example of a slick spline (using penalized b-splines) plot in SAS. The plot is created with SGPLOT procedure. I hope SAS will add such "functions" also to model based procedures such as GLM, MIXED, GENMOD, ...

R in SAS

Another "proof" that R definitely is one of mainstream statistical packages is the news that SAS will provide an interface to R via SAS/IML Studio (today known as SAS Stat Studio).

SAS Macros for BUGS

Rodney Sparapani wrote some SAS macros for interfacing with BUGS. Here is a site with descriptions and links. See also "SAS now goes with Bayesian methods".

SAS now goes with Bayesian methods

New versions of SAS (9.2) procedures GENMOD, LIFEREG, and PHREG enable users to use Bayesian approach for estimating model parameters. The computations are done with McMC methods. There is also a new experimental procedure MCMC. See more here. I wonder how fast these procedures are, since SAS is known to be fast and very efficient with big datasets, but see also one of my previous posts on PROC MIXED vs lmer().

PROC MIXED vs. lmer()

I am using R for a few years now. I like it a lot. Previously I used SAS (notably its modules SAS/BASE, SAS/STAT, SAS/IML, ...), because that is the statistical package of choice at my department. I had a hard time when I switched to R, mainly because I was thinking in the SAS way. However, I made a switch because I liked Rs' open-source nature and its vibrant community. Up to now I did not want to make any comparisons between them. I simply use R because I like it and get along well with using it. Recently I helped a student to fit a quite big model. She used SAS and PROC MIXED failed due to "out of memory". I was surprised, since SAS is known for its great stability and performance with big datasets. I was almoast sure that function lmer() in lme4 package in R will fail also, but could not resist to try it out. To my surprise, the model was fitted without problems. Perhaps I could tweak SAS to use more memory, but I did not invest time to study that option. Maybe new versions of SAS would perform better.

I used the following code in SAS 8.02:

proc mixed data=podatkiratio;
class genotype litter eage sex type year month herd hys;
model bw = genotype litter eage sex type year month;
random herd hys;
run;
quit;

This is the code for R 2.6.0, lme4 0.99875-9:

lmer(bw ~ genotype + litter + eage + sex + type +
         year + month +
         (1|herd) + (1|hys),
         data=podatki)

And the results, which include also the description of the model size:

Linear mixed-effects model fit by REML
Formula: bw ~ genotype + litter + eage + sex + type + year + month + (1|herd)  (1|hys)
  Data: podatki
   AIC    BIC logLik MLdeviance REMLdeviance
152403 152899 -76149     152011       152297
Random effects:
Groups   Name        Variance Std.Dev.
herd     (Intercept) 0.313    0.559  
hys      (Intercept) 0.147    0.383  
Residual             0.297    0.545  
number of obs: 85035, groups: herd, 346; hys, 9653

Fixed effects:
...
## genotype, 13 levels
## litter, 3 levels
## eage, 9 levels
## sex, 2 levels
## type, 2 levels
## year, 16 levels
## month, 12 levels

I am using SAS and R under Windows XP, running on Intel(R) Core(TM)2 CPU 6300 @ 2x1.86GHz with 2 GB RAM.