# [R] Trying to make a nested lme analysis

Douglas Bates bates at stat.wisc.edu
Thu Apr 3 15:38:00 CEST 2003

```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." <chrysopa at insecta.ufv.br> 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
> 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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>
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--
Douglas Bates                            bates at stat.wisc.edu
Statistics Department                    608/262-2598
University of Wisconsin - Madison        http://www.stat.wisc.edu/~bates/

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