Re: [R] Alternatives to linear regression with multiple variables

On Feb 22, 2010, at 7:46 AM, Guy Green wrote: 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:

Re: [R] Alternatives to linear regression with multiple variables

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.s...@imail.org 801.408.8111 > -

Re: [R] Alternatives to linear regression with multiple variables

Guy Green wrote: > > I wonder if someone can give some pointers on alternatives to linear > regression (e.g. Loess) when dealing with multiple variables. > > For two variables, there is also interp.loess in package tcp. It can be rather slow depending on the parameters, so I fear a generaliza

Re: [R] Alternatives to linear regression with multiple variables

You can try the locfit package, which I believe can handle up to 5 variables. E.g., R> library(locfit) Loading required package: akima Loading required package: lattice locfit 1.5-6 2010-01-20 R> x <- matrix(runif(1000 * 3), 1000, 3) R> y <- rnorm(1000) R> mydata <- data.frame(x, y) R> str(m