search for: likelhood

...oup + angles * hands, data = my.data, random = ~ 1 |subject) then I think I would have to compare the model above with a more elaborated one, including more interactions: my.lme2 <- lme(rt ~ age.group * angles * hands, data = my.data, random = ~ 1 |subject) and comparing them by performing a likelhood-ratio test, yes? I think, if I would like to generalize the influence of the experimental conditions on the rt I should define angles and hands as a random effect, yes? ? thanks for a short feedback. It seems, repeated-measures anova's aren't a trivial topic in R :) Cheers! Christoph...

...for calculating the model likelihood and AIC take into account the non-independence of data points? Or are the log-likelihood and AIC calculated using some standard R algorithm that is not valid for phylog.gls models or gls? 2. Is the logLik extracted from phylog.gls suitable for a type-III log-likelhood ratio test? Thanks so much, Miguel Rodriguez-Girones Estación Experimental de Zonas Aridas, CSIC [[alternative HTML version deleted]]

...he equivalent +offset(log(Eff)) would produce the desired effect. Incidentally my code was: glm(Count~md+ms+rf+sg+offset(Eff),family=poisson,data=DepthHabGen) (Making use of glm{stats}) However, offset does not seem to be equivalent to 'exposure' in Stata. As coefficients and log likelhood estimates differ. So I asked the following questions: 1. Do both programs produce the same results without 'exposure' i.e. glm models Yes, log likelihoods and coefficients are the same. 2. How about using the unintuitive non logged " offset=Eff" ? Coefficients and log likel...

...m nls, which does non-linear least squares. I found the documentation opaque, and I'd prefer to stay in the generalized linear model framework and, ideally, maximum likelihood estimators. (A recent review found maximum likelihood estimators using quadrature performed better than penalized likelhood methods, which specifically included glmmPQL in MASS: http://www.ncbi.nlm.nih.gov/pubmed/20949128). The lme4 package apparently supports generalized linear models. The title of the package is "lme4: Linear mixed-effects models using S4 classes" but the brief description is "Fit...

...what I want. (My congrats to the developers of R and nlme, they are extremely useful!). To summarize my questions: I''ve tested lme on a very simple test case (with just 1 parameter, the mean, and 1 random effect), but the results are somewhat different than I get from some simple maximum-likelhood formulas. It appears that some quantities calculated by lme are corrected for the reduction in the degrees of freedom due to the number of fit parameters. But these corrections are slightly different (0.3%-3%) than what I would have expected, and I''d like to understand these differences...

I have not found in your documentation any discussion of Graphical frontends to PXElinux (a la Syslinux). Is this possible, or likely in the future? Steve Kuervers Systems Engineer Simulation Centre LFWA (Edmonton) (780) 973-4011 Ext 5831 (780) 973-1557 Fax kuervers.sj at forces.gc.ca

...r and I would like to try this, as well as with exponential and log-logistic. Of course, author in that paper did not say how they performed the analyses, but I guess they took each group (several group were compared) separately and estimated parameters for distribution, say Weibull, via maximum likelhood. I do not like this approach, since not all data are used in one run, and would like to use model to get parameter estimates and perform inference. With Weibull I am aware that it does not fit in exponential family, unless one is read to specify a value/estimate for one parameter. Any comment are...

I'm fitting glm models and the summary gives the deviance of the model . I would like to obtain the log likelihood How can I do ? Thanks -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not

Dear R users I have been looking for functions that can deal with overdispersed poisson models. Some (one) of the observations are negative. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. The presence of negatives is not