similar to: nlme: gnls with weights and correlation arguments

Displaying 20 results from an estimated 5000 matches similar to: "nlme: gnls with weights and correlation arguments"

2011 Sep 02
0
Standard errors of sexual dimorphism?
Hello! I am working on a manuscript on sexual dimorphism in an aquatic invertebrate, where we have estimated sexual dimorphism (SD) for 7 different traits in four populations (a total of 28 SD-estimates). We have used the following formula for estimating SD: 100 * (mean male trait value - mean female trait value)/overall trait mean). Then, we have used these SD-estimates to perform a GLM against
2002 Oct 04
1
gnls from library nlme
Dear all, I am trying to gain some experience with the function gnls from the nlme package. I tried to model the Theophyline data by trying to model the presumed dependency of the clearance on the body weight. This is my function call of gnls: gnls(conc~SSfol(Dose,Time,lKe,lKa,lCl),data=Theoph, params=list(lKe~1,lKa~1,lCl~Wt),start=c(-2.4,0.46,-3.22,0.01)) That's been the result: Error
2006 Oct 25
1
How to specify a constant in gnls{nlme}
Hi All, I have question about speficifying a constant in gnls() from package nlme. Here is a testing code: ############# library(nlme) x = exp( rnorm(100)) y = 1/(1+x) + rnorm(100)/10 plot( y ~ x) fm1 = gnls( y ~ 1/(1+(x/v)^w), start=list( v=1, w=1)) a =1; b=1; fm2 = gnls( y ~ a/(b+(x/v)^w), start=list( v=1, w=1)) #This won't work because I don't know to set $a$ and $b$ as
2008 Sep 27
1
seg.fault from nlme::gnls() {was "[R-sig-ME] GNLS Crash"}
>>>>> "VW" == Viechtbauer Wolfgang (STAT) <Wolfgang.Viechtbauer at STAT.unimaas.nl> >>>>> on Fri, 26 Sep 2008 18:00:19 +0200 writes: VW> Hi all, I'm trying to fit a marginal (longitudinal) VW> model with an exponential serial correlation function to VW> the Orange tree data set. However, R crashes frequently VW>
2005 Apr 11
0
correlation range estimates with nlme::gls
I'm trying to do a simple (?) analysis of a 1D spatial data set, allowing for spatial autocorrelation. (Actually, I'm comparing expected vs. observed for a spatial model of a 1D spatial data set.) I'm using models like gls(obs~exp,correlation=corExp(form=~pos),data=data) or gls(obs~exp,correlation=corLin(form=~pos),data=data) This form is supposed to fit a linear model of
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
2013 Jan 22
0
ordering in 'gnls' with 'corCompSymm' corStruct
Dear R-devel members, While writing a new correlation structure similar to 'corCompSymm' and intended to be used with 'gnls', I got puzzled with the 'Initialize' method. Using 'Initialize' before 'gnls' may be regarded as a mean to set an initial value for the corStruct parameter. However 'gnls' does not work properly with a
2004 Dec 01
0
gnls(0 error: invalid variable type
Dear R-helpers; While using gnls() to fit a function > Gbht0t.gnls <- gnls(h2 ~ Rht(b0, b1, b2, h1,t1, t2), data=gbht10, + params=list(b0 + b1 + b2 ~ Sisp -1), start=c(strssb0,strssb1,strssb2)) I encountered an error: "Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid variable type " Rht is a defined function to be
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
2003 Jul 24
0
nls.control in gnls
Hi, I've made a selfStart function for use with gnls and the following piece of code works nicely: check1 <- gnls(y ~ spot.shape.fct(xcord, ycord, background, spotintensity, rho, sigma, delta, mux, muy), start=getInitial(y ~ spot.shape.fct(xcord, ycord, background, spotintensity, rho,
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
2012 Feb 13
0
Error from GNLS (undefined columns selected)
 Dear R-helpers, I'm a new R-user and I was trying to gain some experience with the GNLS function of the NLME package.  This is an extract from my dataset (it's a 432x6 data.frame) called "input", in the first column I have the values that I need to fit, while the remaining columns are input variables for the theoretical model, the function "mymodel" (which returns a
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
2003 Aug 14
1
gnls - Step halving....
Hi all, I'm working with a dataset from 10 treatments, each treatment with 30 subjects, each subject measured 5 times. The plot of the dataset suggests that a 3-parameter logistic could be a reasonable function to describe the data. When I try to fit the model using gnls I got the message 'Step halving factor reduced below minimum in NLS step'. I´m using as the initial values of the
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
2009 Jun 24
1
gnls : Rho
Hello list: How to extract the value of "Rho" from a gnls() object. I am using gnls() function similar to res <- gnls(y~SSmicmen(),correlation=corCompSymm(form~1|b),data=dat) Thanks in advance, Mahbub. -- Mahbub Latif School of Mathematical Sciences Queen Mary, University of London United Kingdom [[alternative HTML version deleted]]
2003 Apr 19
1
nls, gnls, starting values, and covariance matrix
Dear R-Help, I'm trying to fit a model of the following form using gnls. I've fitted it using nlsList with the following syntax: nlsList(Y~log(exp(a0-a1*X)+exp(b0-b1*X))|K,start=list (a0=6,a1=0.2,b0=4.5,b1=0.001),data=data.frame(Y=y,X=X,K=k))) which works just fine: <snip> Coefficients: a0 a1 b0 b1 1 5.459381 0.5006811 5.137458 -0.0040548687
2001 Jun 01
1
nls works but not gnls
This works fine: fit42<-nls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) But this, identical except using gnls, doesn't converge: fit43<-gnls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs
2007 Apr 26
1
gnls warning message
Dear R users; I was trying to fit a nonlinear model using gnls (nlme version 3.1-80, R 2.5.0, WinXP) and I got the following error and warning message: Error in gnls(ht ~ a1 * hd * (1 - a2 * exp(-a3 * (dbh/dq2))), data = hdat, : Step halving factor reduced below minimum in NLS step In addition: Warning message: $ operator is deprecated for atomic vectors, returning NULL in:
2008 Sep 02
1
Non-constant variance and non-Gaussian errors with gnls
I have been using the nls function to fit some simple non-linear regression models for properties of graphite bricks to historical datasets. I have then been using these fits to obtain mean predictions for the properties of the bricks a short time into the future. I have also been calculating approximate prediction intervals. The information I have suggests that the assumption of a normal