I haven't seen a reply to this, so I will offer a comment in case
you haven't already solved the problem.
Have you consulted the Mixed-Effects Bible for S-Plus / R,
Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus
(Springer)? If yes, have you worked through
I am examining the following nlme model.
asymporig-function(x,th1,th2)th1*(1-exp(-exp(th2)*x))
mod1-nlme(fa20~(ah*habdiv+ad*log(d)+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2),
fixed=ah+ad+ads+ads2+at+th1+th2~1,
random=th1+th2~1,
I am having trouble figuring out the right form for the nlme arguments. I do
have examples in Modern and Applied Statistics with S and from other sources,
but I still can't figure it out.
I am trying to estimate species richness (sr) in streams across minnesota. My
predictor variables are
Dear All,
I'm trying to model heteroscedasticity using a multilevel model. To
do so, I make use of the nlme package and the weigths-parameter.
Let's say that I hypothesize that the exam score of students
(normexam) is influenced by their score on a standardized LR test
(standLRT).
Rense,
how about
weights = varPower(form = ~ schavg)
or
weights = varConstPower(form = ~ schavg)
or even
weights = varPower(form = ~ schavg | type)
Yuo might find Pinheiro and Bates (2000) to be a valuable investment.
I hope that this helps,
Andrew
On Sun, Jun 10, 2007 at 04:35:58PM
Just to provide some closure on this thread, let me add two comments:
1. Doug's version of my sweep function:
diffid1 -
function(h, id) {
id - as.factor(id)[ , drop = TRUE]
apply(as.matrix(h), 2, function(x) x - tapply(x, id, mean)[id])
}
is far more elegant than my original, and
Ivo,
I don't know whether you ever got a proper answer to this question.
Here is a kludgy one -- someone else can probably provide
a more elegant version of my diffid function.
What you want to do is sweep out the mean deviations from both y
and x based on the factor fe and then estimate the
On 5/4/07, ivo welch [EMAIL PROTECTED] wrote:
hi doug: yikes. could I have done better? Oh dear. I tried to make
my example clearer half-way through, but made it worse. I meant
set.seed(1);
fe = as.factor( as.integer( runif(100)*10 ) ); y=rnorm(100); x=rnorm(100);
print(summary(lm( y ~
On 5/3/07, ivo welch [EMAIL PROTECTED] wrote:
dear R experts:
sorry, I have to ask this again. I know that the answer is in section
7.2 of S Programming, but I don't have the book (and I plan to buy
the next edition---which I hope will be titled S/R programming ;-) ).
I believe the
hi doug: yikes. could I have done better? Oh dear. I tried to make
my example clearer half-way through, but made it worse. I meant
set.seed(1);
fe = as.factor( as.integer( runif(100)*10 ) ); y=rnorm(100); x=rnorm(100);
print(summary(lm( y ~ x + fe)))
deleted
Coefficients:
dear R experts:
sorry, I have to ask this again. I know that the answer is in section
7.2 of S Programming, but I don't have the book (and I plan to buy
the next edition---which I hope will be titled S/R programming ;-) ).
I believe the following yields a standard fixed-effects estimation:
I am not certain how nlme works so I followed an example from the web (
http://www.menne-biomed.de/gastempt/gastempt1.html). I was able to
successfully reproduce the example. However, when I modified my the example
to use my data and with my formula, I get a set of errors having to do with
the
Dear R-user,
I am trying to use the R nlme function to fit a non linear mixed
effects model. The model I wand to fit is an individual somatic growth
model with 4 parameters. For all parameters both fixed and random
effects have to be estimated, as well as their covariance matrix (see
--
David A. Fournier
P.O. Box 2040,
Sidney, B.C. V8l 3S3
Canada
Phone/FAX 250-655-3364
http://otter-rsch.com
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
Sorry list I twitched and sent the wrong stuff.
Maybe I had enough fun for today.
--
David A. Fournier
P.O. Box 2040,
Sidney, B.C. V8l 3S3
Canada
Phone/FAX 250-655-3364
http://otter-rsch.com
__
R-help@stat.math.ethz.ch mailing list
Hello!
I am trying to fit a mixed non-linear model using nlme.
How can I constrain the fixed parameter space (add bounds) as in nls.
Thank you
Ronen
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On 11/19/06, Fluss [EMAIL PROTECTED] wrote:
Hello!
I am trying to fit a mixed non-linear model using nlme.
How can I constrain the fixed parameter space (add bounds) as in nls.
By rewriting the nlme function, an option I wouldn't recommend. :-)
__
A more sensible option in my experience would be to transform the
parameter space to send the boundaries to +/-Inf. Suggested reading for
'nlme' includes Pinheiro and Bates (2000) Mixed-Effects Models in S and
S-Plus (Springer) and Bates and Watts (1988) Nonlinear Regression
Analysis
I can't begin to guess. If your example were self contained (and
preferably simple), it would be easier to diagnose.
When I have problems like this, I often try to find a simple
example that produced the same error message. That process often leads
me to a solution. If it
Hello, I am new to non-linear growth modelling in R and I am trying to
reproduce an analysis that was done (successfully) in S-Plus.
I have a simple non-linear growth model, with no nesting. I have attempted
to simplify the call as much as possible (by creating another grouped
object,
Hi,
the following R lines work fine in R 2.4.0 alpha (and older R versions), but
not in R
2.4.0 beta (details below):
library(drc) # to load the dataset 'PestSci'
library(nlme)
## Starting values
sv - c(0.328919, 1.956121, 0.097547, 1.642436, 0.208924)
## No error
m1 - nlme(SLOPE ~ c +
Christian Ritz [EMAIL PROTECTED] writes:
Hi,
the following R lines work fine in R 2.4.0 alpha (and older R versions), but
not in R
2.4.0 beta (details below):
library(drc) # to load the dataset 'PestSci'
library(nlme)
## Starting values
sv - c(0.328919, 1.956121, 0.097547,
I have a quick question regarding the use of identify to interact with
points on a scatterplot. My question is essentially: can identify be used
when one is plotting model objects to generate diagnostic plots?
Specifically I am using NLME.
For example, I am plotting the fitted values on the x
Most plotting functions in the nlme package use lattice graphics
functions based on the grid package. Identify will not work with
lattice graphics. I'm not sure if there is a replacement.
On 8/17/06, Greg Distiller [EMAIL PROTECTED] wrote:
I have a quick question regarding the use of identify
Hi
Take a look at panel.identify() (in the 'lattice' package).
I'm not sure if it will help you because I cannot run your example code.
Paul
Douglas Bates wrote:
Most plotting functions in the nlme package use lattice graphics
functions based on the grid package. Identify will not work
Many useful diagnostic plots can be recreated in the usual plot()
framework, with only a little coding effort. In this case, I would
imagine that
plot(dframe$log2game, fitted(D2C29.nlme))
abline(0,1)
should get pretty close, if the name of the dataframe containing the
variable is 'dframe'.
Recently I started using nlme intensively, and since it is all new for
me, I have some questions. I am running nlme with
control=list(verbose=TRUE) and during one lengthy fitting, I started
watching the output for some clues, how to speed up the process. I
noticed that in one case, the
Hi all,
Recently I started using nlme intensively, and since it is all new for
me, I have some questions. I am running nlme with
control=list(verbose=TRUE) and during one lengthy fitting, I started
watching the output for some clues, how to speed up the process. I
noticed that in one case, the
Your question is entirely too complex for me to try to answer in a
reasonable amount of time, especially since your example in not self
contained.
If you would still like help on this, I suggest you try to generate a
self contained example that is as simple as you can make
Hi
I am following the model building strategy that is outlined in the Pinheiro and
Bates book wrt including covariates but am having a problem with the plot.
Basically I am using 4 covariates (1 of them is continuous) and 3 of them are
fine but the 4th one is being shown as a scatterplot
Greg,
be careful using attach() and detach(). From the syntax snippets you
showed it seems that you did create an object pcat (factor
variable), but you did not change the respective variable in your
data frame.
Try to remove pcat and see what happens do the results of lme()!
Dirk
Thanks for providing such a self-contained example by which 'nlme'
crashes R. Could you please also give us 'sessionInfo()'? I don't have
time to test it myself now, but perhaps if you identify your platform,
you might interest someone else in checking it.
I'm sorry I
Thanks Spencer. Here is my sessionInfo():
Version 2.3.1 (2006-06-01)
i386-pc-mingw32
attached base packages:
[1] methods stats graphics grDevices utils datasets
[7] base
other attached packages:
nlme
3.1-75
On Thu, 20 Jul 2006, Spencer Graves wrote:
Thanks
Joris De Wolf a écrit :
Have you tried to define 'an' as a group? Like in
gls(IKAfox~an,correlation=corExp(2071,form=~x+y|an,nugget=1.22),data=renliev)
A small data set might help to explain the problem.
Joris
Thanks. Seems to work with a small artificial data set:
Dear listers,
I am trying to model the distribution of fox density over years in the
Doubs department. Measurements have been taken on 470 plots in March
each year and georeferenced. Average density is supposed to be different
each year.
In a first approach, I would like to use a general
I'm not certain what you want, but it sounds like the answer might be
in '?nlme' help page. Toward the end, it describes the 'Value' returned
as follows:
an object of class 'nlme' representing the nonlinear mixed-effects
model fit. Generic functions such as 'print', 'plot'
Dear Reader,
Is it possible to extract the random part of nlme fitting analysis (non linear
mixed effect model) i.e.sigma(b), in R?
Thank you for respond
Farinaz
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https://stat.ethz.ch/mailman/listinfo/r-help
Hi Greg,
Since you haven't yet had a response, you could distill this. It uses the
pixel dataset from nlme() as an example.
## To get separate files, do this
postscript(c:\MyGraph%03.ps, onefile=F)
plot(Pixel, display = Dog, inner = ~Side, layout=c(4,1))
dev.off()
## To get your layout into
Hi
This is the 2nd time I am posting this question as I never got a reply the
1st time round - apologies to anybody who might take offense at this but I
dont know what else to do.
I am struggling to split up the plots of the grouped objects in nlme in a
usable way. The standard plot command
hi greg
If you are using windows, set up a plot window and click the Record
option in the menu. Then run the command. Now you can scroll back
through previous pages by hitting Page Up.
Beware that if you save your workspace without clearing the history,
you may have a lot of bloat from the
Dear all,
I am looking for a function to extract, from an nlme object, the estimated
variance-covariance matrix of the random effects.
many thanks,
Bernard,
__
[[alternative HTML version deleted]]
Marc Bernard bernarduse1 at yahoo.fr writes:
I am looking for a function to extract, from an nlme object, the estimated
variance-covariance matrix of the random effects.
VarCorr
D.Menne
__
R-help@stat.math.ethz.ch mailing list
Thanks for providing such a simple, replicatable example. When I
tried that and similar examples, the response matched what you report.
I also tried the following slight modification of the 'nlme' call that
worked for you:
mod4 -
+nlme(circumference ~ SSlogis(age, Asym, xmid,
Hi folks,
I am tearing my hair out on this one.
I am using an example from Pinheiro and Bates.
### this works
data(Orange)
mod.lis - nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal),
data=Orange )
### This works
mod - nlme(circumference ~ SSlogis(age, Asymp, xmid, scal),
Your example is not self contained, and I don't know enough to
replicate it myself, so I can't tell you what caused it or how to fix
it. However, I can outline the type of thing I've often done with this
kind of problem:
1. First, can you get the plots you want using
dear list:
I used the nlme library according to the great Pinheiro/Bates book, on
R2.3, WinXp
Lac.lme is an lme object with unbalanced data, group is a factor
variable with three levels, when I tried to plot the residuals by
group I got this error msg:
1. Have you read Pinheiro and Bates (2000) Mixed-Effects Models in S
and S-Plus (Springer)? If no, I believe your study of that book will be
well rewarded; mine has.
2. If you've looked at Pinheiro and Bates and still have questions
about this, PLEASE do read the
What you think about the following:
set.seed(1)
DF0.3 - data.frame(X=c(a,a, b), y=rnorm(3))
lme(y~1, random=~1|X, data=DF0.3)
Linear mixed-effects model fit by REML
Data: DF0.3
Log-restricted-likelihood: -2.148692
Fixed: y ~ 1
(Intercept)
-0.4261464
Random effects:
HLM question?
Is there a minmum number of observations required for a category..I have
individusals in work teams.I have incomplete data for all the teams
..sometimes I only have data for one person in a team.I assume that HLM
can't work here! But what would be the mimimal.at the moment I
Hello,
I am having trouble specifying a suitable nlme model.
My data structure is described by
gd - groupedData(ppath ~ lcut | exp, outer = ~ bait, inner = ~ weight, data
= d)
i.e. the response (ppath) of several subjects (sub) was measured at levels
of a continuous variable (lcut). Subjects
Emilio == Emilio A Laca [EMAIL PROTECTED]
on Sat, 18 Mar 2006 22:01:05 -0800 writes:
Emilio Berton, What you say makes sense and helps. I could
Emilio do a parametric bootstrapping. However, I cannot
Emilio make predict.nlme work at all, even without the
Emilio se's. It
Berton,
What you say makes sense and helps. I could do a parametric
bootstrapping. However, I cannot make predict.nlme work at all, even
without the se's. It would save me a lot of time to be able to use
the predict as the statistic in boot.
Does predict.nlme work at all? It is a listed
I am trying to make predictions with se's using a nlme (kew11.nlme
below). I get an error indicating levels for a factor are not allowed.
I have searched and read Rnews, MEMSS, MASS, R-Help, and other lists
in Spanish where I found questions similar to mine but not solution.
I do not really
To: R-help@stat.math.ethz.ch
Subject: [R] nlme predict with se?
I am trying to make predictions with se's using a nlme (kew11.nlme
below). I get an error indicating levels for a factor are
not allowed.
I have searched and read Rnews, MEMSS, MASS, R-Help, and other lists
in Spanish where I
I see you got an error message from R. Did you have both either the
lme4 or the Matrix packages in the search path at the same time you ran
nlme to get the result you got below? If yes, please rerun with only
nlme in the search path. (This may not be necessary, but I always quite
Dear R-Users,
I am comparing the nlme package in S-Plus (v. 7.0) and R (v. 2.2.1, nlme
package version 3.1-68.1; the lattice, Matrix, and lme4 have also just
been updated today, Jan. 23, 2006) on a PC (2.40 GHz Pentium 4 processor
and 1 GHz RAM) operating on Windows XP. I am using a real data
I meant fitting not maximising, it is a nonlinear mixed effects
model, with both fixed and random effects. My assumption is that for
the function I am using the approximation approach used in nlme is
not quite close enough, and nothing much that I can do, except for
looking at starting
Are you familiar with Pinheiro and Bates (2000) Mixed-Effects Models
in S and S-Plus (Springer)? I suspect that book, especially the latter
half, might contain the information you seek.
spencer graves
p.s. PLEASE do read the posting guide!
Ken Beath kbeath at efs.mq.edu.au writes:
I'm maximising a reasonably complex function using nlme (version
3.1-65, have also tried 3.1-66) and am having trouble with fixed
parameter estimates slightly away from the maximum of the log
likelihood. I have profiled the log likelihood and
I'm maximising a reasonably complex function using nlme (version
3.1-65, have also tried 3.1-66) and am having trouble with fixed
parameter estimates slightly away from the maximum of the log
likelihood. I have profiled the log likelihood and it is a parabola
but with sum dips.
Deepayan,
Yes, thanks for confirming my suspicions. I know mixed models are
different but, I did not think they were so different as to preclude
estimating the var-cov matrix (via the Hessian in Maximum likelihood, as
you point out).
Thanks for prompting me to think about MCMC. Your
On 11/21/05, Wassell, James T., Ph.D. [EMAIL PROTECTED] wrote:
Deepayan,
Yes, thanks for confirming my suspicions. I know mixed models are
different but, I did not think they were so different as to preclude
estimating the var-cov matrix (via the Hessian in Maximum likelihood, as
you point
Mora [EMAIL PROTECTED]
cc:r-help@stat.math.ethz.ch
Subject:Re: [R] nlme questions
Both your questions seem too vague to me. You might get more useful
replies if you provide a simple example in a few lines of R code that a
reader could copy from your email into R and see the result
]
[mailto:[EMAIL PROTECTED] Behalf Of Christian Mora
Sent: Friday, November 18, 2005 4:01 AM
To: Spencer Graves
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] nlme questions
Spencer;
Thanks for your suggestions. I found the problem is in the
library nlme. If
you define phi1
Mora' [EMAIL PROTECTED], Spencer Graves
[EMAIL PROTECTED]
cc:r-help@stat.math.ethz.ch
Subject:RE: [R] nlme questions
Warning: non-expert thoughts to follow.
When passing an object to a predict method, the method looks at (a copy) of
the original information from the dataframe
-Original Message-
From: Wassell, James T., Ph.D.
Sent: Thursday, November 17, 2005 9:40 AM
To: 'Deepayan Sarkar'
Subject: RE: nlme question
Deepayan,
Thanks for your interest. It's difficult in email but I need the
variance of Kappa = mu + 1.645*tau + 1.645* sigma
Just using the
PROTECTED] On Behalf Of Wassell, James
T., Ph.D.
Sent: Thursday, November 17, 2005 10:29 AM
To: r-help@stat.math.ethz.ch
Subject: [R] nlme question
-Original Message-
From: Wassell, James T., Ph.D.
Sent: Thursday, November 17, 2005 9:40 AM
To: 'Deepayan Sarkar'
Subject: RE: nlme question
Thank you for taking the time to think about my problem.
The reference states: The covariance structure must be considered,
because for unbalanced data the estimates (i.e. mu, sigma and tau hats)
are not typically independent. Page 105. It would be nice to simply
assume zero covariance terms,
Subject: RE: [R] nlme question
Thank you for taking the time to think about my problem.
The reference states: The covariance structure must be considered,
because for unbalanced data the estimates (i.e. mu, sigma and tau hats)
are not typically independent. Page 105. It would be nice to simply
On 11/17/05, Doran, Harold [EMAIL PROTECTED] wrote:
I think the authors are mistaken. Sigma is random error, and due to its
randomness it cannot be systematically related to anything. It is this
ind. assumption that allows for the likelihood to be expressed as
described in Pinhiero and Bates
Both your questions seem too vague to me. You might get more useful
replies if you provide a simple example in a few lines of R code that a
reader could copy from your email into R and see the result (as
suggested in the posting guide! www.R-project.org/posting-guide.html).
The
I am using the package nlme to fit a simple random effects (variance
components model)
with 3 parameters: overall mean (fixed effect), between subject
variance (random) and
within subject variance (random).
I have 16 subjects with 1-4 obs per subject.
I need a 3x3 variance-covariance
On 11/16/05, Wassell, James T., Ph.D. [EMAIL PROTECTED] wrote:
I am using the package nlme to fit a simple random effects (variance
components model)
with 3 parameters: overall mean (fixed effect), between subject
variance (random) and within subject variance (random).
So to paraphrase,
p.s. You may also find useful the process I followed to diagnose this
problem.
1. I copied your example into R and confirmed that I could replicate
the error.
2. I read the documentation, invoked debug, and tried different
things to isolate the problem. For example, I
Going through the R-Dev list, I have found this (from Pedro Afalo),
dated 8 April 2004:
Dear Richard,
The problem that you report is documented (but no solution given) in the
file ch08.R in the scripts directory of nlme package.
I have found the following workaround just by chance, but it
You need repeated measures for a random effect to make any sense. I
modified your example as follows, and the error went away.
mytable$RIL2 - rep(1:4, 1:4)
cs2 - corCompSymm(value=0.5, form=~1|RIL2)
model2-lme(mytrait~myloc, data=mytable, random=~1|RIL2,
+
Dear listers,
I am trying to fit a nlme model with age and pds as reals, and
zone a factor with two levels Annaba and Boumalek . The best
model found is the following:
modm3
Nonlinear mixed-effects model fit by maximum likelihood
Model: pds ~ Asym/(1 + exp((xmid - age)/scal))
Data:
RSiteSearch(Singularity in backsolve) produced 33 hits, the second
of which was the following:
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/62691.html
This reply from Peter Dalgaard dates 8 Oct. 2005 asks, Which version
of R and NLME? R 2.2.0 ships with a version where
Dear R users;
Ive got two questions concerning nlme library 3.1-65 (running on R 2.2.0 /
Win XP Pro). The first one is related to augPred function. Ive been working
with a nonlinear mixed model with no problems so far. However, when the
parameters of the model are specified in terms of some
Guy Forrester ForresterG at landcareresearch.co.nz writes:
R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0
Jose Pinheiro, Douglas Bates, Saikat DebRoy and Deepayan Sarkar (2005).
...
I am trying to run the scripts from the
Dear All,
Using:-
R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0
and
Jose Pinheiro, Douglas Bates, Saikat DebRoy and Deepayan Sarkar (2005). nlme:
Linear and
nonlinear mixed effects models. R package version 3.1-65.
on a
Dear Friends,
I am seeking for any help on an error message in lme
functions. I use mixed model to analyze a data with
compound symmetric correlation structure. But I get an
error message: Error in corMatrix.corCompSymm(object) :
NA/NaN/Inf in foreign function call (arg 1). If I change
the
Hi,
I am hoping some one can help with this.
I am using nlme to fit a random coefficients model. It ran for hours before
returning
Error: Singularity in backsolve at level 0, block 1
The model is
Hello
I'm fitting a gls model with a variance-covariance structure and an
getting an error message I don't understand
I'm using gls() from the nlme library with the structure defined by
correlation = corSymm(form = ~1|Subject), weights = varIdent(form=~1|strata)
I get the error
Error in
Have you tried anova(fit1, fit2), where
fit1 - lme(one model...)
fit2 - lme(a submodel ... )
This anova does about the best that anyone knows how to do -- or at
lest did 7 years ago when it was written. If the submodel changes the
fixed effects, you should use
Dear R users,
I am using lme and nlme to account for spatially correlated errors as
random effects. My basic question is about being able to correct F, p, R2
and parameters of models that do not take into account the nature of such
errors using gls, glm or nlm and replace them for new F, p, R2
Hi.
I'm trying to perform what should be a reasonably basic analysis of some
spatial presence/absence data but am somewhat overwhelmed by the options
available and could do with a helpful pointer. My researches so far
indicate that if my data were normal, I would simply use gls() (in nlme)
and
You seem to want to model spatially correlated bernoulli variables.
That's a difficult task, especially as these are bernoulli and not
binomial(n1). With a much fuller description of the problem we may be
able to help, but I at least have no idea of the aims of the analysis.
glmmPQL is
to be most important.
Colin
-Original Message-
From: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
Sent: 13 July 2005 11:30
To: Beale, Colin
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] nlme, MASS and geoRglm for spatial autocorrelation?
You seem to want to model spatially correlated
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] Behalf Of Beale, Colin
Sent: 13 July 2005 10:15
To: Prof Brian Ripley
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] nlme, MASS and geoRglm for spatial autocorrelation?
My data are indeed bernoulli
Hello,
I am running this script from Pinheiro Bates book in R Version 2.1.1 (WinXP).
But, I can't plot Figure 2.3.
What's wrong?
TIA.
Rod.
-
library(nlme)
names( Orthodont )
[1] distance age Subject Sex
levels( Orthodont$Sex
On 7/11/05, R V [EMAIL PROTECTED] wrote:
Hello,
I am running this script from Pinheiro Bates book in R Version 2.1.1
(WinXP).
But, I can't plot Figure 2.3.
What's wrong?
There was a change in the way that R handles assignments of names of
components and that affected the construction of
Dear all
I am struggling with nlme and error message. Even going through
Pinheiro, Bates nlme book did not gave me a clue how to avoid
this.
fit - nlme(ce ~ fi1 / ((1+exp(fi2-fi3*tepl))^(1/fi4)), data =
temp1na.gr,
start = c(fi1=30, fi2=-100, fi3=-.05, fi4=40),
fixed = fi1+fi2+fi3+fi4~1,
On 6/22/05, Petr Pikal [EMAIL PROTECTED] wrote:
Dear all
I am struggling with nlme and error message. Even going through
Pinheiro, Bates nlme book did not gave me a clue how to avoid
this.
fit - nlme(ce ~ fi1 / ((1+exp(fi2-fi3*tepl))^(1/fi4)), data =
temp1na.gr,
start = c(fi1=30,
I assigned a class the first problem in Pinheiro Bates, which uses the
data set PBIB from the SASmixed package. I have recently downloaded
2.0.1 and its associated packages. On trying
library(SASmixed)
data(PBIB)
library(nlme)
plot(PBIB)
I get a warning message
Warning message:
replacing
On Tuesday 05 April 2005 18:40, Murray Jorgensen wrote:
I assigned a class the first problem in Pinheiro Bates, which uses
the data set PBIB from the SASmixed package. I have recently
downloaded 2.0.1 and its associated packages. On trying
library(SASmixed)
data(PBIB)
library(nlme)
Dear nlme-lovers,
I do a fit fo a 3-parameter fit to physiological data with nlme:
EmptD-deriv(~(vol+slope*t)*exp(-t/tempt)...
The approach described in Pinheiro/Bates by using nlsList
as a first approximator was somewhat unstable (many NA), but
a direct fit converges quickly and fits look
All,
I have been learning about mixed models and have been
able to successfully use lme( ) and nlme( ) to fit
some simple linear and 4PL logistic models. As a
relative newbie I am at a loss as to how I can do
the following:
(1) Import a SAS dataset with DATE9. formatted time
values and get
Dear List:
My question is more statistical than R oriented (although it originates
from my work with nlme). I know statistical questions are occasionally
posted, so I hope my question is relevant to the list as I cannot turn
up a solution anywhere else. I will frame it in the context of an R
-
specific coefficients (e.g. slopes), in the random effects sense.
Rich Raubertas
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Doran, Harold
Sent: Friday, October 08, 2004 1:27 PM
To: [EMAIL PROTECTED]
Subject: [R] nlme vs gls
Dear List
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