Displaying 20 results from an estimated 6000 matches similar to: "Nonlinear weighted least squares estimation"
2007 Jun 07
2
Nonlinear Regression
Hello
I followed the example in page 59, chapter 11 of the 'Introduction to R'
manual. I entered my own x,y data. I used the least squares. My function has
5 parameters: p[1], p[2], p[3], p[4], p[5]. I plotted the x-y data. Then I
used lines(spline(xfit,yfit)) to overlay best curves on the data while
changing the parameters. My question is how do I calculate the residual sum
of squares.
2003 Oct 30
2
'nls' and its arguments
Dear R experts!
I'd to fit data by 'nls' with me-supplied function 'fcn'.
1) I'd like 'fcn' to accept arbitrary arguments, i.e. I defined it
as f(...) {<body>}. (Ok, that's not actually impotant).
2) Second, I would NOT like to supply every parameter in the formula.
To illustrate this, let's look at the last example of 'nls' help
2005 Jul 11
2
Weighted nls
Dear R Community,
I am attempting to perform a weighted non-linear least squares fit. It has already been noted that the weights option is not yet implemented for the nls function, but no one seems to offer any suggestions for getting around this problem. I am still curious if a) anyone has code they have written which includes a weight options for nls, or b) if there is another model which
2005 Jan 27
3
weighting in nls
I'm fitting nonlinear functions to some growth data but I'm getting radically different results in R to another program (Prism). Furthermore the values from the other program give a better fit and seem more realistic. I think there is a problem with the results from the r nls function. The differences only occur with weighted data so I think I'm making a mistake in the weighting.
2011 Apr 20
1
How can I 'predict' from an nls model with a fit specified for separate groups?
Following an example on p 111 in 'Nonlinear Regression with R' by Ritz &
Streibig, I have been fitting nls models using square brackets with the
grouping variable inside. In their book is this example, in which
'state' is a factor indicating whether a treatment has been used or not:
> Puromycin.m1 <- nls(rate ~ Vm[state] *
+ conc/(K[state] + conc), data = Puromycin,
2003 Oct 28
2
Confidence ellipse for correlation
Hello,
SAS' point and click interface has the option of produce a scatterplot with a
superimposed confidence ellipse for the correlation coefficient. Since I
generally like R so much better, I would like to reproduce this in R. I've
been playing with the ellipse package. In order to have the points and the
ellipse on the same graph I've done the following.
(Load ellipse
2006 Nov 29
3
R2.4 xyplot + panel.number problem
Hi all;
I'm trying to display a 2 panel plot for the Puromycin data from R
with 2 different non-linear models fitted to each group. The problem
is that as far as I know panel.number doesn't work in the latest
version of R. Can anyone give a hint how to solve this?
Here is the code that I used before and now doesn't work
xyplot(rate ~conc| state,Puromycin,
2012 Sep 19
0
Discrepancies in weighted nonlinear least squares
Dear all,
I encounter some discrepancies when comparing the deviance of a weighted and
unweigthed model with the AIC values.
A general example (from 'nls'):
DNase1 <- subset(DNase, Run == 1)
fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1)
This is the unweighted fit, in the code of 'nls' one can see that 'nls'
generates a vector
2009 Nov 17
1
Define lm/glm object without evaluating them
For e.g. lm/glm type models I would like to separate model specification and model fitting and then only fit the models later 'when data arrives'. To be specific, I would like make a specification like
m1 <- lm(rate~conc)
m2 <- lm(rate~I(conc^2))
and then later I want to 'put data into' the objects and evaluate (fit the model), e.g. something like
update(m1, data=Puromycin)
2004 Feb 02
1
Robust nonlinear regression - sin(x)/x?
You reall have only one parameter in your model, c = a/b. You can't
identify both a and b from your model, therefore, you should fit the
linear model: lm(z ~ c* sin(x)/x)
Ravi.
----- Original Message -----
From: cstrato <cstrato at aon.at>
Date: Monday, February 2, 2004 2:28 pm
Subject: [R] Robust nonlinear regression - sin(x)/x?
> Dear all
>
> Since I did not receive any
2008 Nov 03
2
standard errors for predict.nls?
Dear all,
Is there a way to retrieve standard errors from nls models? The help page tells me that arguments
such as se.fit are ignored...
Many thanks and best wishes
Christoph
--
Dr. rer.nat. Christoph Scherber
University of Goettingen
DNPW, Agroecology
Waldweg 26
D-37073 Goettingen
Germany
phone +49 (0)551 39 8807
fax +49 (0)551 39 8806
Homepage http://www.gwdg.de/~cscherb1
2004 May 21
1
Buglet/omission in nls package (PR#6901)
Dear all,
I noticed the following under R 1.8.1 (when nls was still a separate
package) but the same problem occurs under R 1.9.0 (where most (all?)
of nls is now in the stats package):
> data(Puromycin)
> fm <- nls(rate~SSmicmen(conc,b0,b1), Puromycin, subset = state=="treated")
> coef(summary(fm))
NULL
The problem seems to be that summary.nls uses the name
2006 Feb 21
3
How to get around heteroscedasticity with non-linear leas t squares in R?
Your understanding isn't similar to mine. Mine says robust/resistant
methods are for data with heavy tails, not heteroscedasticity. The common
ways to approach heteroscedasticity are transformation and weighting. The
first is easy and usually quite effective for dose-response data. The
second is not much harder. Both can be done in R with nls().
Andy
From: Quin Wills
>
> I am
2003 Sep 16
2
gnls( ) question
Last week (Wed 9/10/2003, "regression questions") I posted
a question regarding the use of gnls( ) and its dissimilarity
to the syntax that nls( ) will accept. No one replied, so
I partly answered my own question by constructing indicator
variables for use in gnls( ). The code I used to construct
the indicators is at the end of this email.
I do have a nagging, unanswered
2012 Nov 21
2
Weighted least squares
Hi everyone,
I admit I am a bit of an R novice, and I was hoping someone could help me
with this error message:
Warning message:
In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
extra arguments weigths are just disregarded.
My equation is:
lm( Y ~ X1 + X2 + X3, weigths = seq(0.1, 1, by = 0.1))
--
View this message in context:
2010 Jan 28
3
weighted least squares vs linear regression
I need to find out the difference between the way R calculates weighted
regression and standard regression.
I want to plot a 95% confidence interval around an estimte i got from least
squares regression.
I cant find he documentation for this
ive looked in
?stats
?lm
?predict.lm
?weights
?residuals.lm
Can anyone shed light?
thanks
Chris.
--
View this message in context:
2012 Oct 19
2
Which package/function for solving weighted linear least squares with inequality and equality constraints?
Dear All,
Which package/function could i use to solve following linear least square
problem?
A over determined system of linear equations is given. The nnls-function may
would be a possibility BUT:
The solving is constrained with
a inequality that all unknowns are >= 0
and a equality that the sum of all unknowns is 1
The influence of the equations according to the solving process is
2006 Dec 11
1
Weighted averaging partial least squares regression
Hello,
is it possible in R to calculate a Weighted averaging partial least
squares regression? I'm not firm in statistics and didn't found anything
about weighted averaging in combination with PLS in the help archives.
Or is it possible to develop a workaround with the pls-package?
thanks for help in advance
Andreas Plank
--
_____________________________________________
Dipl. Biol.
2011 Jan 15
1
Weighted least squares regression for an exponential decay function
Hello,
I have a data set of data which is best fit by an exponential decay
function. I would like to use a nonlinear weighted least squares regression.
What function should I be using?
Thank you!
[[alternative HTML version deleted]]
2008 Jul 23
1
Questions on weighted least squares
Hi all,
I met with a problem about the weighted least square regression.
1. I simulated a Normal vector (sim1) with mean 425906 and standard deviation 40000.
2. I simulated a second Normal vector with conditional mean b1*sim1, where b1 is just a number I specified, and variance proportional to sim1. Precisely, the standard deviation is sqrt(sim1)*50.
3. Then I run a WLS regression without the