Thanks! I think the minor differences taking the values with rnorm result of the homogen distribution without an effect. But the results of aov and lme should be similiar for data with effects, too (at least for simple and balanced designs).
Karl --- "Pascal A. Niklaus" <[EMAIL PROTECTED]> schrieb: > Karl Knoblick wrote: > > >Dear R-community! > > > >I still have the problem reproducing the following > >example using lme. > > > >id<-factor(rep(rep(1:5,rep(3,5)),3)) > >factA <- factor(rep(c("a1","a2","a3"),rep(15,3))) > >factB <- factor(rep(c("B1","B2","B3"),15)) > >Y<-numeric(length=45) > >Y[ 1: 9]<-c(56,52,48,57,54,46,55,51,51) > >Y[10:18]<-c(58,51,50,54,53,46,54,50,49) > >Y[19:27]<-c(53,49,48,56,48,52,52,52,50) > >Y[28:36]<-c(55,51,46,57,49,50,55,51,47) > >Y[37:45]<-c(56,48,51,58,50,48,58,46,52) > >df<-data.frame(id, factA, factB, Y) > >df.aov <- aov(Y ~ factA*factB + Error(factA:id), > >data=df) > >summary(df.aov) > > > >Is there a way to get the same results with lme as > >with aov with Error()? HOW??? > > > >One idea was the following: > >df$factAid=factor(paste(as.character(df$factA),":",as.character(df$id),sep="")) > >df.lme <- > >lme(Y~factA*factB,df,random=~1|factAid,method="REML") > > >The degrees of freedom look right, but the F values > >don't match aov. > > > >Hope somebody can help! Thanks!! > > > >Karl > > > > > Hmmm, strange, it works if I use factB:id as plot... > it also works when > I use factA:id as plot and replace your Y's by > random numbers... is this > a problem with convergence? > > Pascal > > > > df$Y=rnorm(45) > > summary(aov(Y ~ factB*factA + > Error(id:factA),data=df)) > > Error: id:factA > Df Sum Sq Mean Sq F value Pr(>F) > factA 2 2.9398 1.4699 0.9014 0.4318 > Residuals 12 19.5675 1.6306 > > Error: Within > Df Sum Sq Mean Sq F value Pr(>F) > factB 2 7.1431 3.5716 7.4964 0.002956 ** > factB:factA 4 4.2411 1.0603 2.2254 0.096377 . > Residuals 24 11.4345 0.4764 > --- > Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' > 0.1 ` ' 1 > > > anova(lme(Y ~ factB*factA ,data=df, random = ~ 1 > | plot)) > numDF denDF F-value p-value > (Intercept) 1 24 0.014294 0.9058 > factB 2 24 7.496097 0.0030 > factA 2 12 0.901489 0.4318 > factB:factA 4 24 2.225317 0.0964 > > Pascal > > > > summary(aov(Y ~ factA*factB + Error(factB:id))) > > Error: factB:id > Df Sum Sq Mean Sq F value Pr(>F) > factB 2 370.71 185.36 51.488 1.293e-06 *** > Residuals 12 43.20 3.60 > --- > Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' > 0.1 ` ' 1 > > Error: Within > Df Sum Sq Mean Sq F value Pr(>F) > factA 2 9.911 4.956 1.6248 0.21788 > factA:factB 4 45.556 11.389 3.7341 0.01686 * > Residuals 24 73.200 3.050 > > > df$plot <- factor(paste(df$factB,df$id)) > > anova(lme(Y ~ factB*factA , data=df, random = ~1 > | plot)) > numDF denDF F-value p-value > (Intercept) 1 24 33296.02 <.0001 > factB 2 12 51.47 <.0001 > factA 2 24 1.63 0.2178 > factB:factA 4 24 3.73 0.0168 > > > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help