Well, I don't think this is ANCOVA as you seem to want to specify a random slope for a covariate. aov() is not designed for that. It is also not designed for assessing the size of fixed effects which seems the question here.
As I understand it, you have only one observation for each value of `rt' for each subject, and `rt' is an explanatory variable. For lme you have specified a subject-dependent intercept and coefficient of `rt'. You cannot do that in aov, where the argument of Error is supposed to be a factor or a combination of factors. This is in the reference given by help for aov or Error (on pp 157-9). On Thu, 15 Jan 2004, Christoph Lehmann wrote: > Hi > I compouted a multiple linear regression with repeated measures on one > explanatory variable: > BOLD peak (blood oxygenation) as dependent variable, > > and as independent variables I have: > -age.group (binaray:young(0)/old(1)) > -and task-difficulty measured by means of the reaction-time 'rt'. For > 'rt' I have repeated measurements, since each subject did 12 different > tasks. > -> so it can be seen as an ANCOVA > > subject age.group bold rt > > subj1 0 0.08 0.234 > subj1 0 0.05 0.124 > .. > subj1 0 0.07 0.743 > > subj2 0 0.06 0.234 > subj2 0 0.02 0.183 > .. > subj2 0 0.05 0.532 > > subjn 1 0.09 0.234 > subjn 1 0.06 0.155 > .. > subjn 1 0.07 0.632 > > I decided to use the nlme library: > > patrizia.lme <- lme(bold ~ rt*age.group, data=patrizia.data1, random= ~ > rt |subject) > > summary(patrizia.lme) > Linear mixed-effects model fit by REML > Data: patrizia.data1 > AIC BIC logLik > 272.2949 308.3650 -128.1474 > > Random effects: > Formula: ~rt | subject > Structure: General positive-definite, Log-Cholesky parametrization > StdDev Corr > (Intercept) 0.2740019518 (Intr) > rt 0.0004756026 -0.762 > Residual 0.2450787149 > > Fixed effects: bold ~ rt + age.group + rt:age.group > Value Std.Error DF t-value p-value > (Intercept) 0.06109373 0.11725208 628 0.521046 0.6025 > rt 0.00110117 0.00015732 628 6.999501 0.0000 > age.group -0.03750787 0.13732793 43 -0.273126 0.7861 > rt:age.group -0.00031919 0.00018259 628 -1.748115 0.0809 > Correlation: > (Intr) rt ag.grp > rt -0.818 > age.group -0.854 0.698 > rt:age.group 0.705 -0.862 -0.805 > > Standardized Within-Group Residuals: > Min Q1 Med Q3 Max > -3.6110596 -0.5982741 -0.0408144 0.5617381 4.8648242 > > Number of Observations: 675 > Number of Groups: 45 > > --end output > #-> if the model assumptions hold this means, we don't have a > significant age effect but a highly significant task-effect and the > interaction is significant on the 0.1 niveau. Nope. It means that you have two lines with a common non-zero intercept and probably different slopes for the two age groups. However, as I understand it rt=0 is an extraplolation to an physically impossible value, so interpreting the intercept makes little sense. > I am now interested, if one could do the analysis also using aov and the > Error() option. > > e.g. may I do: > > l <- aov(bold ~ rt*age.group + Error(subject/rt),data=patrizia.data1) > > summary(l) > > Error: subject > Df Sum Sq Mean Sq > rt 1 0.0022087 0.0022087 > > Error: subject:rt > Df Sum Sq Mean Sq > rt 1 40.706 40.706 > > Error: Within > Df Sum Sq Mean Sq F value Pr(>F) > rt 1 2.422 2.422 10.0508 0.001592 ** > age.group 1 8.722 8.722 36.2022 2.929e-09 *** > rt:age.group 1 0.277 0.277 1.1494 0.284060 > Residuals 669 161.187 0.241 > --- > Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 > > > which looks weird > > or what would you recommend? > > thanks a lot > > Christoph > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
