Hi Dan,
check the `type' argument of `anova.lme()' which defaults to "sequential". This is also discussed in Pinheiro and Bates but I don't have the book with me now to trace the page.
I hope it helps.
Best, Dimitris
---- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/16/396887 Fax: +32/16/337015 Web: http://www.med.kuleuven.ac.be/biostat http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
----- Original Message ----- From: "Dan Bebber" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Wednesday, November 17, 2004 3:35 PM
Subject: [R] summary.lme() vs. anova.lme()
Dear R list:
I modelled changes in a variable (mconc) over time (d) for individuals
(replicate) given one of three treatments (treatment) using:
mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate,
data=my.data)
summary(mconc.lme) shows that the linear coefficient of one of the treatments is significantly different to zero, viz. Value Std.Error DF t-value p-value ... ... ... ... ... treatmentf:poly(d, 2)1 1.3058562 0.5072409 315 2.574430 0.0105
But anova(mconc.lme) gives a non-significant result for the treatment*time
interaction, viz.
numDF denDF F-value p-value
(Intercept) 1 315 159.17267 <.0001
treatment 2 39 0.51364 0.6023
poly(d, 2) 2 315 17.43810 <.0001
treatment:poly(d, 2) 4 315 2.01592 0.0920
Pinheiro & Bates (2000) only discusses anova() for single arguments briefly
on p.90.
I would like to know whether these results indicate that the significant
effect found in summary(mconc.lme) is spurious (perhaps due to
multiplicity).
Many thanks, Dan Bebber
Department of Plant Sciences University of Oxford South Parks Road Oxford OX1 3RB UK Tel. 01865 275000
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