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From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Kyeong Soo (Joseph) Kim
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
[snip]
By the way, I
]
Sent: Tuesday, April 27, 2010 2:33 PM
To: Gabor Grothendieck
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like M/M/1 queue, but way
too
-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like
M/M/1 queue, but way too complicated to mathematically
analyze it using classical queueing
.
-Original Message-
From: Kyeong Soo (Joseph) Kim [mailto:kyeongsoo@gmail.com]
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Dear Keith,
Thanks for the suggestion and taking your time to respond
-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like
M/M/1 queue, but way too complicated to mathematically
analyze it using classical
30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Dear Keith,
Thanks for the suggestion and taking your time to respond to it.
But, you misunderstand something and seems that you do not read all my
previous e-mails
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Kyeong Soo (Joseph) Kim
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
[snip
-boun...@r-
project.org] On Behalf Of Kyeong Soo (Joseph) Kim
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
[snip]
By the way, I wonder why most of the responses I've received from this
list are so
Message-
From: Kyeong Soo (Joseph) Kim [mailto:kyeongsoo@gmail.com]
Sent: Friday, April 30, 2010 5:24 PM
To: Greg Snow
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
I have already learned a lot from the list, both technical
-
From: Kyeong Soo (Joseph) Kim [mailto:kyeongsoo@gmail.com]
Sent: Tuesday, April 27, 2010 2:33 PM
To: Gabor Grothendieck
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm
I recently came to realize the true power of R for statistical
analysis -- mainly for post-processing of data from large-scale
simulations -- and have been converting many of existing Python(SciPy)
scripts to those based on R and/or Perl.
In the middle of this conversion, I revisited the problem
-project.org] On
Behalf Of Kyeong Soo (Joseph) Kim
Sent: Tuesday, April 27, 2010 10:31 AM
To: r-help@r-project.org
Subject: [R] Curve Fitting/Regression with Multiple Observations
I recently came to realize the true power of R for statistical
analysis -- mainly for post-processing of data from large
This will compute a loess curve and plot it:
example(loess)
plot(dist ~ speed, cars, pch = 20)
lines(cars$speed, fitted(cars.lo))
Also this directly plots it but does not give you the values of the
curve separately:
library(lattice)
xyplot(dist ~ speed, cars, type = c(p, smooth))
On Tue, Apr
Hello Gabor,
Many thanks for providing actual examples for the problem!
In fact I know how to apply and generate plots using various R
functions including loess, lowess, and smooth.spline procedures.
My question, however, is whether applying those procedures directly on
the data with multiple
If you are looking for a framework for statistical inference you could
look at additive models as in the mgcv package which has a book
associated with it if you need more info. e.g.
library(mgcv)
fm - gam(dist ~ s(speed), data = cars)
summary(fm)
plot(dist ~ speed, cars, pch = 20)
fm.ci -
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like M/M/1 queue,
but way too complicated to mathematically analyze it using classical
queueing theory) and the conclusion I want to make is qualitative
rather than quantatitive -- a
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