Where is the rats data available?
It looks as if you have an lme model with both a fixed effect for
Treatment and a random effect for Treatment. I would guess that you
want to have a fixed effect for treatment and random effects for
Rat %in% Treatment
and
Liver %in% Rat %in% Treatment
If so you would first create a factor for Rat %in% Treatment, say rTrT
by
rats$rTrt = getGroups(~ 1 | Treatment/Rat, data = rats, level = 2)
then fit the lme model as
lme(Glycogen ~ Treatment, data = rats, random = ~ 1|rTrT/Liver)
"Ronaldo Reis Jr." <[EMAIL PROTECTED]> writes:
> Hi,
>
> I'm trying to understand the lme output and procedure.
> I'm using the Crawley's book.
>
> I'm try to analyse the rats example take from Sokal and Rohlf (1995).
> I make a nested analysis using aov following the book.
>
> > summary(rats)
> Glycogen Treatment Rat Liver
> Min. :125.0 Min. :1 Min. :1.0 Min. :1
> 1st Qu.:135.8 1st Qu.:1 1st Qu.:1.0 1st Qu.:1
> Median :141.0 Median :2 Median :1.5 Median :2
> Mean :142.2 Mean :2 Mean :1.5 Mean :2
> 3rd Qu.:150.0 3rd Qu.:3 3rd Qu.:2.0 3rd Qu.:3
> Max. :162.0 Max. :3 Max. :2.0 Max. :3
>
> > attach(rats)
> > Treatment <- factor(Treatment)
> > Rat <- factor(Rat)
> > Liver <- factor(Liver)
>
> > model <- aov(Glycogen~Treatment/Rat/Liver+Error(Treatment/Rat/Liver))
> > summary(model)
>
> Error: Treatment
> Df Sum Sq Mean Sq
> Treatment 2 1557.56 778.78
>
> Error: Treatment:Rat
> Df Sum Sq Mean Sq
> Treatment:Rat 3 797.67 265.89
>
> Error: Treatment:Rat:Liver
> Df Sum Sq Mean Sq
> Treatment:Rat:Liver 12 594.0 49.5
>
> Error: Within
> Df Sum Sq Mean Sq F value Pr(>F)
> Residuals 18 381.00 21.17
> >
>
> OK,
>
> Then I try to make this analysis using lme.
>
> > model <- lme(Glycogen~Treatment, random=~1|Treatment/Rat/Liver)
> > summary(model)
> Linear mixed-effects model fit by REML
> Data: NULL
> AIC BIC logLik
> 233.6213 244.0968 -109.8106
>
> Random effects:
> Formula: ~1 | Treatment
> (Intercept)
> StdDev: 3.541272
>
> Formula: ~1 | Rat %in% Treatment
> (Intercept)
> StdDev: 6.00658
>
> Formula: ~1 | Liver %in% Rat %in% Treatment
> (Intercept) Residual
> StdDev: 3.764883 4.600247
>
> Fixed effects: Glycogen ~ Treatment
> Error in if (any(wchLv <- (as.double(levels(xtTab[, wchPval])) == 0))) { :
> missing value where logical needed
> In addition: Warning message:
> NaNs produced in: pt(q, df, lower.tail, log.p)
> >
>
> The random effects are correct, the variance component is OK:
>
> In nested aov | In nested lme
> Residual
> 21.1666 | 21.16227
> Liver in Rats
> 14.16667 | 14.17434
> Rats in Treatment
> 36.0648 | 36.079
>
> But I not understand why the Fixed effects error?
>
> What is the problem in my formula to make this analysis using lme?
>
> Thanks for all
> Inte
> Ronaldo
> --
> Anger kills as surely as the other vices.
> --
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Douglas Bates [EMAIL PROTECTED]
Statistics Department 608/262-2598
University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/
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