Hi: I'm trying to fit a linear mixed effect model to two time series. My data base has 3 columns, number of observation, y, and x. Both y and x are market and specific asset return (both measured on a daily basis for the last 3 years). I want to explain y in terms of x. Response=y fixed=intercept random=x I've tried lme(y~1,random=~x) and got: Error en getGroups.data.frame(dataMix, groups) : Invalid formula for groups Then I tried lme(rcopec~1,random=~ripsa|obs) (I don´t have groups, each group is each period of time and have one observation per group). I got Error ein n lme.formula(rcopec ~ 1, random = ~ripsa | obs) : nlminb problem, convergence error code = 1 message = false convergence (8) Warning message: In lme.formula(rcopec ~ 1, random = ~ripsa | obs) : Fewer observations than random effects in all level 1 groups I can´t make it work. Is there any other function beside lme to do this? Can some one help me out? Thanks a lot in advance. Regards. María [[alternative HTML version deleted]]