search for: lmeobject

Dear List, Is there a way to extract variance components from lmeObjects or summary.lme objects without using intervals()? For my purposes I don't need the confidence intervals which I'm obtaining using parametric bootstrap. Thanks, Mike [[alternative HTML version deleted]]

Hello All, Specifying 'type = "response"' when using predict() on a model fit using glm(...,family="binomial") returns fitted probabilities. Is it possible to get the same from a model object fit using glmmPQL() ? Thanks in advance, Rob _____________________________________________________ Rob Keefe Lab: (208) 885-5165 M.S. student

Hi R-users, I am using lme to fit a linear mixed model with the nlme package, does anyone know if it is possible to obtain standard error estimates of the variance components estimators and an adequate method to test the significance of the variance component? Thanks, Berta. [[alternative HTML version deleted]]

...inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or "nls" Yet, according to “?glmmPQL”, glmmPQL gives an object of class lme: Value: A object of class '"lme"': see 'lmeObject'. What should I do to be able to compare my models? Thank you. Emmanuelle Emmanuelle TASTARD UMR 5174 'Evolution et Diversité Biologique' Université Paul Sabatier Bat 4R3 31062 TOULOUSE CEDEX 9 France tel : 05 61 55 67 59 [[alternative HTML version deleted]]

I fit a random effects linear model to data, and then tried to use it to predict, but I got this error: > predict(lmeObject, newdata, level=0) Error in eval(mCall$fixed)[-2] : object is not subsettable This is a new error for me. It still occurs if I change the level to 1 or if I change the data for prediction back to the original dataset to which the lme model was fitted. Can anyone help? Thank you in advance! Reb...

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,