Displaying 20 results from an estimated 4000 matches similar to: "removing random effect from nlme or using varPower() in nls"
2005 Mar 02
1
Using varPower in gnls, an answer of sorts.
Back on January 16, a message on R-help from Ravi Varadhan described a
problem with gnls using weights=varPower(). The problem was that the
fit failed with error
Error in eval(expr, envir, enclos) : Object "." not found
I can reliably get this error in version 2.0.1-patched 2004-12-09 on
Windows XP and 2.0.1-Patched 2005-01-26 on Linux.
The key feature of that example is that the
2005 Jul 26
1
evaluating variance functions in nlme
Hi,
I guess this is a final plea, and maybe this should go to R-help but
here goes.
I am writing a set of functions for calibration and prediction, and to
calculate standard
errors and intervals I need the variance function to be evaluated at new
prediction points.
So for instance
fit<-gnls(Y~SSlogis(foo,Asym,xmid,scal),weights=varPower())
2004 Oct 01
4
gnls or nlme : how to obtain confidence intervals of fitted values
Hi
I use gnls to fit non linear models of the form y = alpha * x**beta
(alpha and beta being linear functions of a 2nd regressor z i.e.
alpha=a1+a2*z and beta=b1+b2*z) with variance function
varPower(fitted(.)) which sounds correct for the data set I use.
My purpose is to use the fitted models for predictions with other sets
of regressors x, z than those used in fitting. I therefore need to
2004 Jan 14
2
Generalized least squares using "gnls" function
Hi:
I have data from an assay in the form of two vectors, one is response
and the other is a predictor. When I attempt to fit a 5 parameter
logistic model with "nls", I get converged parameter estimates. I also
get the same answers with "gnls" without specifying the "weights"
argument.
However, when I attempt to use the "gnls" function and try to
2009 Oct 15
2
Proper syntax for using varConstPower in nlme
Hello,
Excuse me for posting two questions in one day, but I figured it would be
better to ask my questions in separate emails. I will again give the caveat
that I'm not a statistician by training, but have a fairly decent
understanding of probability and likelihood.
As before, I'm trying to fit a nonlinear model to a dataset which has two main
factors using nlme. Within the dataset
2008 Apr 29
2
function to generate weights for lm?
Hi,
I would like to use a weighted lm model to reduce heteroscendasticity. I am
wondering if the only way to generate the weights in R is through the
laborious process of trial and error by hand. Does anyone know if R has a
function that would automatically generate the weights need for lm?
Thanks,
--
Tom
[[alternative HTML version deleted]]
2002 Sep 11
1
lme with/without varPower - can I use AIC?
I want to compare the following two models in AIC
(Treat, Spotter are categorial, p is pressure, Pain is
continuous)
PainW.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat,
weights=varPower(form=~Pain))
# AIC= -448
Pain.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat)
#AIC = -19.7
Note the huge differences in AIC, and the estimated power of 6.
A plot of the residual
2007 Oct 17
2
nmle: gnls freezes on difficult case
Hi,
I am not sure this is a bug but I can repeat it, The functions and data
are below.
I know this is nasty data, and it is very questionable whether a 4pl
model
is appropriate, but it is data fed to an automated tool and I would
have hoped for an error. Does this repeat for anyone else?
My details:
> version
_
platform i686-pc-linux-gnu
2008 May 09
1
Which gls models to use?
Hi,
I need to correct for ar(1) behavior of my residuals of my model. I noticed
that there are multiple gls models in R. I am wondering if anyone
has experience in choosing between gls models. For example, how
should one decide whether to use lm.gls in MASS, or gls in nlme for
correcting ar(1)? Does anyone have a preference? Any advice is appreciated!
Thanks,
--
Tom
[[alternative HTML
2012 Sep 19
2
Warning Message: In if (deparse(params[[nm]][[3]]) != "1")
I am using the gnls procedure in nlme package to fit a nonlinear model as:
nl.fit<-gnls(Y ~ b0*exp(b1/X),
data = data1,
params=list(
b0~p1+I(p1^2)+p2+I(p2^2)+p3+I(p3^2)+p5+p6
b1~p8+p2+I(p2^2)+p3+p9+p10+p11),
start = c(25,0,0,0,0,0,0,0,0,-8.6,0,0,0,0,0,0,0),
weights=varPower(form =~ X)
2009 Oct 30
0
Interpreting gnls() output in comparison to nls()
Hi,
I've been trying to work with the gnls() function in the "nlme" package. My
decision to use gnls() was so that I could fit varPower and such to some of
the data. However, in working with a small dataset, I've found that the
results given by gnls() don't seem to make any sense and they differ
substantially from those produced by nls(). I suspect that I am just
2008 Sep 12
4
reading in results from system(). There must be an easier way...
Hello,
I am currently using R to run an external program and then read the results
the external program sends to the stdout which are tsv data.
When R reads the results in it converts it to to a list of strings which I
then have to maniuplate with a whole slew of commands (which, figuring out how
to do was a reall challenge for a newbie like myself)--see below.
Here's the code I'm
2009 Jan 07
1
Extracting degrees of freedom from a gnls object
Dear all,
How can I extract the total and residual d.f. from a gnls object?
I have tried str(summary(gnls.model)) and str(gnls.model) as well as gnls(), but couldn?t find the
entry in the resulting lists.
Many thanks!
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
2004 Oct 18
3
manual recreation of varConstPower using new fixed effects variables in nlme
Hello, I am trying to design new variance structures
by using fixed effects variables in combination with
the VarPower function. That is, I would like to
create and evaluate my own variance function in the
data frame and then incorporate it into the model
using varPower, with value=.5.
As a start, I am trying to recreate the function of
VarConstPower by introducing two new variables in the
2000 Mar 07
1
Problems with nlme (PR#471)
Dear R developers,
first of all let me join the chorus of congratulations for the release
of R 1.0.0. Well, done!
Unfortunately, I find it necessary to e-mail in a bug report regarding
the `nlme' package. On my office machine I experience the following
trouble:
bossiaea:/opt/R$ R CMD check -c nlme
Checking package `nlme' ...
Massaging examples into `nlme-Ex.R' ...
Running
2009 Oct 17
1
custom selfStart model works with getInitial but not nls
Hello,
I'm having problems creating and using a selfStart model with nlme. Briefly,
I've defined the model, a selfStart object, and then combined them to make a
selfStart.default model.
If I apply getInitial to the selfStart model, I get results. However, if I try
usint it with nls or nlsList, these routines complain about a lack of initial
conditions.
If someone could point out
2008 Feb 25
0
logLik calculation in gls (nlme)
I'm getting some odd results computing log-likelihoods
with gls using splines with increasing degrees of freedom --
the deviance *increases* substantially with increasing df.
(Since spline models with increasing df aren't nested, it
need not decline monotonically but I would expect it to
have a decreasing trend!)
I may just be confused, but I *think* the issue is somewhere
within the
2001 May 30
2
environments
I would like to be able, inside a function, to create a new function, and
use it as part of a formula as an argument to, say, gnls or nlme. for
example:
MyTop <- function(data=dta) {
Cexp <- function(dose,A,B,m){...}
Model <- as.formula(paste("y","~ Cexp(",paste(formals(Cexp),collapse
=", "),")"))
MyCall <-
2006 Jan 09
1
trouble with extraction/interpretation of variance structure para meters from a model built using gnls and varConstPower
I have been using gnls with the weights argument (and varConstPower) to
specify a variance structure for curve fits. In attempting to extract the
parameters for the variance model I am seeing results I don't understand.
When I simply display the model (or use "summary" on the model), I get what
seem like reasonable values for both "power" and "const". When I
2009 May 04
1
how to change nlme() contrast parametrization?
How to set the nlme() function to return the answer without the intercept parametrization?
#=========================================================================================
library(nlme)
Soybean[1:3, ]
(fm1Soy.lis <- nlsList(weight ~ SSlogis(Time, Asym, xmid, scal),
data = Soybean))
(fm1Soy.nlme <- nlme(fm1Soy.lis))
fm2Soy.nlme <- update(fm1Soy.nlme,