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
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