[R-sig-ME] suggestions for estimating confidence intervals with only 1.5 GB of ram?

Darren Norris doon75 at hotmail.com
Fri Apr 1 02:07:22 CEST 2011


Many thanks for the reply David,
I am probably being thick - but it is not the estimates for random effects 
that I am interested in.....
I am trying to get the confidence intervals around the predicted values from 
the fixed effects.

for example:
The data is available at this link as a R workspace  "DarrensSpace.RData" 
(size is approx 70 kb):
http://cid-f0a9fa3480208398.office.live.com/self.aspx/lmeData/DarrensSpace.RData

####   the random effect (1|yearMonth) is used to model serial temporal 
autocorrelation,
####   data is  observational & ecological so unbalanced
#### "yall " is abundance of the turtle species of interest
library("lme4")
fmer2f<-lmer(yall~yearSeason+sun+total_precip_trip+mean_temp_trip+surveyArea+obscat+hour_period+(1|yearMonth),REML=FALSE,data=df.p)

library(AICcmodavg) # another very helpful library
p.fmer2f<-predictSE.mer(fmer2f, df.p,type="response")

#### I can then plot and see predicted values and SE if I wanted, for 
example....
plot(df.p$yall) #observed values
lines(p.fmer2f$fit) #predicted response

How can I calculate / approximate 95 CI intervals to go around this line?

Many thanks again!
Darrren



-----Original Message----- 
From: David Duffy
Sent: Thursday, March 31, 2011 8:42 PM
To: Darren Norris
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] suggestions for estimating confidence intervals with 
only 1.5 GB of ram?

On Thu, 31 Mar 2011, Darren Norris wrote:

> I would like to obtain (then plot) the fitted (?predicted - sorry never 
> know correct term) response values and in an ideal world 95% confidence 
> (or prediction) intervals for a lmer model.

Possibly I'm being obtuse, but in your fmer2f model has

Random effects:
  Groups    Name        Variance Std.Dev.
  yearMonth (Intercept) 0.000000 0.00000  <----
  Residual              0.096815 0.31115
Number of obs: 333, groups: yearMonth, 50

Cheers, David Duffy.




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