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...ut singular gradient matrix at initial parameter estimates. What I am interested in is to make inference about parameters b and g, so this has to be taken into account in the model formulation. What options do I have? Also, how is it possible to fit a partially linear model? Thank you!! Irene Mantzouni ---------------- PhD student DIFRES

Dear all, I have a for loop which includes nls model estimation. The loop breaks after the first non-convergence error. How can I make the loop continue and try to estimate all models? I suppose it should be sth like: if(...) { next } but I have no idea how to setup the arguements... Thank you! Irene

I would like to combine time-series data to test for correlations and interactions using random and fixed effects meta-analysis. So, I am looking for the right packages and documentation. I know about meta and rmeta packages of R. Are there any more? What are the diffrences in brief? Can you please suggest some references that could be used as a guide for meta-analysis in R (or S-plus)?

Dear list, I would like to produce a plot of variables where the size of the points will be indicative of their standard errors. How is that possible? Thank you! [[alternative HTML version deleted]]

Hi all! I try to estimate a statistic of the form: (x1-x2)/(y1-y2), where x1,x2,y1,y2 represent variable means, so each has an estimate and standard error associated with it. How is it possible to estimate the mean and the variance of this ratio? Thank you! [[alternative HTML version deleted]]

I am a new R user and so I thought I could start with "Using R for Introductory statistics" by Verzani. In order to use some of the functions and datasets I have to install the simpleR package which is is now inside the UsingR package. I did so using >install.packages("UsingR"). However, the functions such as "simple.freqpoly.R" do not work. I also tried to

Dear all, I am trying to define a selfStart function for a non-linear model, which is a log-transformed SSmicmen model with multiplicative errors and so it is required to make them additive: log(y)=log(a)+log(x)-log(1+x/b) Any ideas about how to use the "peeling" method to derive the "initial" argument and get the initial values? Thank you for being always there!:)

Hi all! How is it possible to estimate standard errors for coef obtained from lme? Is there sth like se.coef() for lmer or what is the anaytical solution? Thank you!

I would like to fit a 2-level mixed model: yit=a+a[i]+a[it] +(b+b[i]+b[it])*xit+eps[it] However, the variance of the second level components should depend on the group, i.e. sigma for a[it] and b[it] should be [i] specific. I do not know whether this is conceptually right in the mixed model context... In case it stands, how should the formula look like? Also, the data are unbalanced with

Hi all! this is a rather statistical question: is it meaningful to consider an interaction effect between 2 continuous covariates? for example: lm(y~x1+x2+x1:x2) Should one of continuous x1, x2 be "transformed" to a categorical variable, i.e. be classified into groups? Is it easier to interpret the effect if 1 or both are centered to the mean or z-transformed? Thank you!

Hi all! I am trying to run a regression where the predictor values are not real data but each is estimated from a different model. So, for each value I have a mean and variance. Which package/function should I use in this case? Thank you! Irene [[alternative HTML version deleted]]

Dear all, I have an annual time-series of population numbers and I would like to estimate the auto-correlation. Can I use acf() function and judge whether auto-correlation is significant by the plots? The acf array, eg: Autocorrelations of series 'x$log.s.r', by lag 0 1 2 3 4 5 6 7 8 9 10 11 12 1.000 0.031 -0.171

Is there a formal way to prove the need of a mixed model, apart from e.g. comparing the intervals estimated by lmList fit? For example, should I compare (with AIC ML?) a model with seperately (unpooled) estimated fixed slopes (i.e.using an index for each group) with a model that treats this parameter as a random effect (both models treat the remaining parameters as random)? Thank you!

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

...ior - JAMAJ) 19. Re: Problems with garch() function tseries package R 2.6.0 (Jos? Augusto Morais de Andrade J?nior - JAMAJ) 20. install problem 2.6 mac os x 10.5 (Oscar Moreno) 21. Re: R CMD SHLIB gives error: bad value (generic) for -mtune= switch (McKay Curtis) 22. hierarchical mixed model (Irene Mantzouni) 23. Re: Problems with garch() function tseries package R 2.6.0 (Jos? Augusto Morais de Andrade J?nior - JAMAJ) 24. Re: Problems with garch() function tseries package R 2.6.0 (Jos? Augusto Morais de Andrade J?nior - JAMAJ) 25. Re: unable to install package ff (Matthew Keller) 26. Re: install proble...

I am a new R user and so I thought I could start with "Using R for Introductory statistics" by Verzani. In order to use some of the functions and datasets I have to install the simpleR package which is is now inside the UsingR package. I did so using >install.packages("UsingR"). However, the functions such as "simple.freqpoly.R" do not work. I also tried to

Hi all! This is a rather statistical question; Which effect sizes (parametric or not) could I use in order to estimate the amount of non-linear correlation between 2 variables? Is it possible to correct for auto-correlation within the correlated times series? Any suggestions for the appropriate packages/ functions are more than welcome!! Thank you! [[alternative HTML version

Hi all! I would like to estimate confidence intervals for a non lm model. For example, I use a mixed model of the form: md=lme(y~x1+I(x1^2)+x2 ...) Parameters x1+I(x1^2) are fixed effects and I would like to plot the predicted (partial) curve corresponding to these ones, along with 90% CI bands. Thus, I simulate (partial) predictions: Lx=c() Ux=c() Mx=c() z=c() for (j in

Dear all, I am trying to estimate partial residuals for the multiple regression lm model: a.lm=lm(y~x1+x2) I use the function residuals(a.lm, type="partial") However, the results are much different when I use the "manual" method to get partial residuals for x2 (or for x1): residuals(a.lm) +b1*x1 Where b1 is the estimated coefficient for x1 in the