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:
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of Guy Green
> Sent: Monday, February 22, 2010 5:47 AM
> To: r-help@r-project.org
> Subject: [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
lf(X1, X2, X3), data=mydata)
R> plot(fit)
Andy
> -Original Message-
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Guy Green
> Sent: Monday, February 22, 2010 7:47 AM
> To: r-help@r-project.org
> Subject: [R] Alternatives to
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 c
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