Re: [R] 2 lme questions

Spencer Graves Mon, 05 Apr 2004 15:56:31 -0700

The "print" method and the "summary" command display the STANDARD DEVIATIONS (not the variances) on the screen (or in a sink file). However, when I do attributes(lme(...)) and attributes(summary(lme(...))), I don't see anything I can use. Fortunately, the "interval" function produces a list, from which the variance estimates can be extracted. Consider the following example:

DF <- data.frame(group=c(1,1,2,2), y=c(1, 2, 11, 12))
library(nlme)
fit <- lme(y~1, random=~1|group, DF)

Linear mixed-effects model fit by REML
 Data: DF
 Log-restricted-likelihood: -6.559401
 Fixed: y ~ 1
(Intercept)
       6.5

Random effects:
Formula: ~1 | group
       (Intercept)  Residual
StdDev:    7.053597 0.7070954

Number of Observations: 4
Number of Groups: 2
> lme.int <- intervals(fit)
> lme.int$reStruct^2
Error in lme.int$reStruct^2 : non-numeric argument to binary operator

> lme.int$reStruct$group^2
                 lower     est.    upper
sd((Intercept)) 3.06859 49.75323 806.6845
> lme.int$sigma^2
   lower      est.     upper
0.0704255 0.4999839 3.5496217
attr(,"label")
[1] "Within-group standard error:"

There may be a better way; if there is, I hope someone will enlighten us all. If not, at least this works in R 1.8.1

hope this helps. spencer graves

Harold Doran wrote:

There are two way to accomplish this in nlme. First try using the summary() command, which will produce all variance components and estimates for the fixed effects. Also, try the following to extract the point estimates and approximate CIs for the variance comonents.

intervals(model.lme, which="var")

Harold
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] Behalf Of Steve Roberts
Sent: Monday, April 05, 2004 3:32 PM
To: [EMAIL PROTECTED]
Cc: Steve Roberts
Subject: [R] 2 lme questions
Greetings,

1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object.

2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess it is a numerical/optimisation problem but don't know enough about the lme fitting algorithm to know which tollerance/scale parameter to mess about with. Any suggestions where to start?

Thanks,

Steve.

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