R help - Apr 2007 - to findout maximized log likelihoods by using rlarg.fit (for several r order statistics)

Dear R helpers,

I need to find out maximized log likelihoods,
parameters estimates and standard errors (in
parentheses) of r largest-order statistics model, with
different values of r by using the function rlarg.fit.
I want to specify required number of order statistics
to the model. I attached my data file with this
mail.please help me.

Ruposh
 



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----------------------------------------------------------------------
> 
> Message: 1
> Date: Tue, 3 Apr 2007 11:58:27 +0200
> From: Adelchi Azzalini <azzalini at stat.unipd.it>
> Subject: Re: [R] bivariate interpolation
> To: "eric lee" <ericlee100 at gmail.com>
> Cc: r-help at stat.math.ethz.ch
> Message-ID:
> <20070403115827.99d67de3.azzalini at stat.unipd.it>
> Content-Type: text/plain; charset=ISO-8859-1
> 
> On Tue, 3 Apr 2007 11:43:55 +0200, Adelchi Azzalini
> wrote:
> 
> AA> On Mon, 2 Apr 2007 22:26:06 -0400, eric lee
> wrote:
> AA> 
> AA> EL> Hi.  I'm trying to take a data set with two
> independent and one
> AA> EL> dependent variable and enter a x,y value to
> predict the dependent
> AA> EL> with a nonparametric technique.  I've been
> using interpp in the akima
> AA> EL> package, (windows xp, R 2.4.1), but get
> values that are orders of
> AA> EL> magnitude off when the predictors are
> slightly out of the range of
> AA> EL> the data set.  Can you recommend a function
> for me?  I've read that
> AA> EL> predict.loess has the same problem or just
> lists NA.  Smooth.spline
> AA> EL> looks good, but can only do one predictor
> instead of bivariate.  I've
> AA> EL> tried help.search("predict"), but couldn't
> find what I needed.
> AA> EL> Thanks.
> AA> EL> 
> AA> 
> AA> one option is to use sm.regression of package
> sm, along these lines
> AA> 
> AA>  x <- cbind(runif(100,-2, 2), runif(100,-2, 2))
> AA>  y <- x[,1]^2 + x[,2]^2 + 0.7*x[,1]*x[,2] +
> rnorm(100)/3
> AA>  sm.regression(x,y)
> AA>  ev.pt <- rbind(c(-2,-2),c(2,2),
> c(2,-2),c(-2,2))
> AA>  a <- sm.regression(x,y, eval.points=ev.pt, 
> eval.grid=FALSE)
> 
> 
=== message truncated ==


 
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