As lowess() is mentioned another in similar vein is locfit() from
package locfit
Duncan
Duncan Mackay
Department of Agronomy and Soil Science
University of New England
ARMIDALE NSW 2351
Email home: mac...@northnet.com.au
At 00:07 14/04/2012, you wrote:
Since you have only one dependent varia
Since you have only one dependent variable, try using lowess()
instead. It is less flexible -- only does local linear robust fitting
-- but has arguments built in that allow you to sample and interpolate
and limit the number of robustness iterations. It runs considerably
faster as a result.
-- Ber
Alternatively, use only a subset to run loess(), either a random sample or
something like every other k-th (sorted) data value, or the quantiles. It's
hard for me to imagine that that many data points are going to improve your
model much at all (unless you use tiny span).
Andy
From: r-help-b
On 12.04.2012 05:49, arunkumar wrote:
Hi
The function loess takes very long time if the dataset is very huge
I have around 100 records
and used only one independent variable. still it takes very long time
Any suggestion to reduce the time
Use another method that is computationally
Hi
The function loess takes very long time if the dataset is very huge
I have around 100 records
and used only one independent variable. still it takes very long time
Any suggestion to reduce the time
-
Thanks in Advance
Arun
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