[R] Another NEWBIE
Frank E Harrell Jr
f.harrell at vanderbilt.edu
Sat Jun 19 14:18:31 CEST 2004
> Thank you all who anwered me.
> I think, I mainly thought to understand the difference between SPSS /SAS and
> R, but didn't really get the point (what explains the question, wich metods
> R can't do). Maybe, because I don't have much experience with programming
> (near to none). My background in stats goes also only back to indroductory
> classes and an advanced course in multivariate statistics. To this, I'm
> working with Hair, Anderson, Tatham & Blacks's "Multivariate Data Analysis"
> (5th Ed.) as my ressource, mainly with questionnaire analysis (Reliability
> Analysis and Factor Analysis, also MDS, Conjoint etc. plus sometimes
> standard MANOVA, Multiplke Regression etc.). So, maybe my stats aren't
> sophisticated enough to use R, I'm just a standard user of applied
> statistical methods, not an academic researcher or even a statistician. It
> was mainly a descision by costs, because R is free software.
> With the concept, I completely mistook the R concept as a programming
> environment more as a kind of advanced SPSS Syntax (because I also would
> call it "programming" when using it), which I now know, is completely wrong.
> So, I again thank for your help.
> Cheers, Frank.
You'll find (eventually) that you can do everything you need in R short
of accurately getting certain P-values in mixed effect linear models for
which SAS does a good job. It's a question of finding the right books,
online manuals (main R manuals as well as user contributed ones - see
especially "Simple R" to start), understanding specific functions, and
perhaps above all, finding examples to work from. For data manipulation
in particular (recoding, reshaping data, etc.), R is now superior to all
other systems unless the database is truly massive. For statistical
reporting R is also way ahead of the pack (e.g., Sweave with LaTeX).
SAS and SPSS no longer even complement R in my opinion, except for SAS's
scalability in processing massive databases and some features (but not
model diagnostics or graphics) of PROC MIXED.
You can think of using R as programming, but for application of many
popular analytic methods I prefer to think of it as finding example
scripts and modifying them according to your needs.
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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