[R] Alternatives to linear regression with multiple variables

[Greg Snow]

Well, the help page for the loess function says that the formula can include up to 4 predictor variables.  There are also additive models (mgcv or gam (or other) package).

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Guy Green
> Sent: Monday, February 22, 2010 5:47 AM
> To: r-help at r-project.org
> Subject: [R] Alternatives to linear regression with multiple variables
> 
> 
> I wonder if someone can give some pointers on alternatives to linear
> regression (e.g. Loess) when dealing with multiple variables.
> 
> Taking any simple table with three variables, you can very easily get
> the
> intercept and coefficients with:
> 	summary(lm(read_table))
> 
> For obvious reasons, the coefficients in a multiple regression are
> quite
> different from what you get if you calculate regressions for the single
> variables separately.  Alternative approaches such as Loess seem
> straightforward when you have only one variable, and have the advantage
> that
> they can cope even if the relationship is not linear.
> 
> My question is: how can you extend a flexible approach like Loess to a
> multi-variable scenario?  I assume that any non-parametric calculation
> becomes very resource-intensive very quickly.  Can anyone suggest
> alternatives (preferably R-based) that cope with multiple variables,
> even
> when the relationship (linear, etc) is not known in advance?
> 
> Thanks,
> 
> Guy
> --
> View this message in context: http://n4.nabble.com/Alternatives-to-
> linear-regression-with-multiple-variables-tp1564370p1564370.html
> Sent from the R help mailing list archive at Nabble.com.
> 
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