On 11-09-05 02:13 PM, Billy wrote: > Dear Dr. Ben Bolker and 'JLucke', > > Thanks for your comments, but I'm still facing some problems. > For example, using the gls() function, I receive an error message and > I'm not sure I'm writing the arguments in the right way.
Well, (if you still need help with this -- your comments below seem like you're making progress with a different approach) what did you try, and what is the error message? We can't help you without details ... > Instead, I thought about my original models and realized that I was > modelling variance as a linear function of the predictor variable, which > could drop off to zero values. Changing > > sd = c0 + c1*x > > to > > sd = c0*x^c1 > > avoids the zero values and all problematic models have converged. Paying > attention on the estimates, they also make sense. Good. > > The new problem now (and probably due to my weak Mathematical skills) is > that one set o candidate models includes models that consider the effect > of not only one, but two predictor variables on the response (y). > > How could be the right way to model that? > > sd = c0 * (x ^ c1) * (w ^ c2) > > or > > sd = (c0 * x ^ c1) + (c0 * w ^ c2) ? > > In which c0, c1, and c2 are constant parameters, and x and w are > different predictor variables. > > Thanks again > > Billy It's not clear that either of them is necessarily more "right" than the others, but you could (much) more easily implement the first in gls(); take a look at ?varComb ... you would use something like weights=varComb(varPower(form=~x),varPower(form=~w)) > > On Mon, Aug 29, 2011 at 3:50 PM, Ben Bolker <bbol...@gmail.com > <mailto:bbol...@gmail.com>> wrote: > > Billy.Requena <billy.requena <at> gmail.com <http://gmail.com>> writes: > > > > > Hi everybody, > > > > I'm interested in evaluating the effect of a continuous variable > on the mean > > and/or the variance of my response variable. I have built functions > > expliciting these and used the 'mle2' function to estimate the > coefficients, > > as follows: > > > > func.1 <- function(m=62.9, c0=8.84, c1=-1.6) > > { > > s <- c0+c1*(x) > > -sum(dnorm(y, mean=m, sd=s,log=T)) > > } > > > > m1 <- mle2(func.1, method="SANN") > > > > However, the estimation of the effect of x on the variance of y > usually has > > dealt some troubles, resulting in no convergencies or sd of estimates > > extremely huge. I tried using different optimizers, but I still > faced the > > some problems. > > > > When I had similar troubles in 'GLMM' statistical universe, I used > bayesian > > functions to solve this problem, enjoyning the flexibility of > different > > start points to reach the maximum likelihood estimates. However, I > have no > > idea which package or which function to use to solve the specific > problem > > I'm facing now. > > Does anyone have a clue? > > Thanks in advance > > Unless I'm missing something, you can fit this model > (more easily) in gls() from the nlme package, which allows models > for heteroscedasticity. See ?nlme::varConstPower > > gls(y~1,weights=varPower(power=1,form=~x),data) > > This gives you a standard deviation proportional to (t1+|v|); > that is, if the baseline residual standard deviation is S, then > the standard deviation is S*(t1+|v|), so S would correspond to > your c1 and S*t1 would correspond to your c0. > > Ben Bolker > > ______________________________________________ > R-help@r-project.org <mailto:R-help@r-project.org> mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > > > > -- > Gustavo Requena > PhD student - Laboratory of Arthropod Behavior and Evolution > Universidade de São Paulo > Correspondence adress: > a/c Glauco Machado > Departamento de Ecologia - IBUSP > Rua do Matão - Travessa 14 no 321 Cidade Universitária, São Paulo - SP, > Brasil > CEP 05508-900 > Phone number: 55 11 3091-7488 > > http://ecologia.ib.usp.br/opilio/gustavo.html ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
