From: Douglas Bates <dmbates_at_gmail.com>

Date: Wed 07 Sep 2005 - 00:47:46 EST

> anova(fm1)

Analysis of Variance Table

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Sep 07 00:51:28 2005

Date: Wed 07 Sep 2005 - 00:47:46 EST

On 9/6/05, Ronaldo Reis-Jr. <chrysopa@gmail.com> wrote:

> Hi Spencer,

*>
**> Em Dom 04 Set 2005 20:31, Spencer Graves escreveu:
**> > Others may know the answer to your question, but I don't. However,
**> > since I have not seen a reply, I will offer a few comments:
**> >
**> > 1. What version of R are you using? I just tried superficially
**> > similar things with the examples in ?aov in R 2.1.1 patched and
**> > consistently got F and p values.
**>
**> I'm using the R version 2.1.1 on Linux Debian
**> Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0
**>
**> > 2. My preference for this kind of thing is to use lme in
**> > library(nlme) or lmer in library(lme4). Also, I highly recommend
**> > Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer).
**>
**> Yes, this is my preference too, but I need aov for classes.
**>
**> > 3. If still want to use aov and are getting this problem in R 2.1.1,
**> > could you please provide this list with a small, self contained example
**> > that displays the symptoms that concern you? And PLEASE do read the
**> > posting guide! "http://www.R-project.org/posting-guide.html". It might
**> > increase the speed and utility of replies.
**> >
**> > spencer graves
**>
**> I send the complete example. This is a example from the Crwaley's book
**> (Statistical Computing: An introdution to data analysis using S-Plus.
**>
**> This is a classical experiment to show pseudoreplication, from Sokal and Rohlf
**> (1995).
**>
**> In this experiments, It have 3 treatmens applied to 6 rats, for each rat it
**> make 3 liver preparation and for each liver it make 2 readings of glycogen.
**> This generated 6 pseudoreplication per rat. I'm interested on the effect os
**> treatment on the glycogen readings.
**>
**> Look the R analyses:
**>
**> --------------------
**> > Glycogen <-
**> c(131,130,131,125,136,142,150,148,140,143,160,150,157,145,154,142,147,153,151,155,147,147,162,152,134,125,138,138,135,136,138,140,139,138,134,127)
**> > Glycogen
**> [1] 131 130 131 125 136 142 150 148 140 143 160 150 157 145 154 142 147 153
**> 151
**> [20] 155 147 147 162 152 134 125 138 138 135 136 138 140 139 138 134 127
**> > Treatment <- factor(rep(c(1,2,3),c(12,12,12)))
**> > Treatment
**> [1] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3
**> Levels: 1 2 3
**> > Rat <- factor(rep(rep(c(1,2),c(6,6)),3))
**> > Rat
**> [1] 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2
**> Levels: 1 2
**> > Liver <- factor(rep(rep(c(1,2,3),c(2,2,2)),6))
**> > Liver
**> [1] 1 1 2 2 3 3 1 1 2 2 3 3 1 1 2 2 3 3 1 1 2 2 3 3 1 1 2 2 3 3 1 1 2 2 3 3
**> Levels: 1 2 3
**> >
**> > ### Model made identical to the book
**> >
**> > 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
**> >
**> > ### Model made by myself, I'm interested only in Treatment effects
**> >
**> > model <- aov(Glycogen~Treatment+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 F value Pr(>F)
**> Residuals 3 797.67 265.89
**>
**> Error: Treatment:Rat:Liver
**> Df Sum Sq Mean Sq F value Pr(>F)
**> Residuals 12 594.0 49.5
**>
**> Error: Within
**> Df Sum Sq Mean Sq F value Pr(>F)
**> Residuals 18 381.00 21.17
**> --------------------
**>
**> What it dont calculate the F and P for treatment?
*

Would it be easier to do it this way?

> library(lme4)

Loading required package: Matrix

Loading required package: lattice

> (fm1 <- lmer(Glycogen ~ Treatment + (1|Treatment:Rat) + (1|Treatment:Rat:Liver)))

Linear mixed-effects model fit by REML

Formula: Glycogen ~ Treatment + (1 | Treatment:Rat) + (1 | Treatment:Rat:Liver)

AIC BIC logLik MLdeviance REMLdeviance 231.6213 241.1224 -109.8106 234.297 219.6213 Random effects: Groups Name Variance Std.Dev. Treatment:Rat:Liver (Intercept) 14.167 3.7639 Treatment:Rat (Intercept) 36.065 6.0054 Residual 21.167 4.6007# of obs: 36, groups: Treatment:Rat:Liver, 18; Treatment:Rat, 6

Fixed effects:

Estimate Std. Error DF t value Pr(>|t|) (Intercept) 140.5000 4.7072 33 29.8481 <2e-16 Treatment2 10.5000 6.6569 33 1.5773 0.1243 Treatment3 -5.3333 6.6569 33 -0.8012 0.4288

> anova(fm1)

Analysis of Variance Table

Df Sum Sq Mean Sq Denom F value Pr(>F) Treatment 2 123.993 61.996 33.000 2.929 0.06746

The degrees of freedom for the denominator are an upper bound (in this case a rather gross upper bound) so the p-value is a lower bound. It is on my "To Do" list to improve tthis but I have a rather long "To Do" list.

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Sep 07 00:51:28 2005

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