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What you're fitting with aov is called a strip plot, and it's tricky to fit with lme because the subject:A and subject:B random effects are crossed.

Your first attempt is equivalent to aov(Y ~ A*B + Error(subject), data=d), which doesn't include all the random effects; your second attempt is the right idea, but the syntax for crossed random effects using lme is very tricky.

Using lme from the nlme package, the code would be

lme(Y ~ A*B, random=list(subject=pdBlocked(list(~1, pdIdent(~A-1), pdIdent(~B-1)))), data=d)

Using lmer from the lme4 package, the code would be something like

lmer(Y ~ A*B + (1|subject) + (1|A:subject) + (1|B:subject), data=d)    

These threads from R-help may be helpful (and to give credit, that's where I got the nlme code from).

http://www.biostat.wustl.edu/archives/html/s-news/2005-01/msg00091.html

http://permalink.gmane.org/gmane.comp.lang.r.lme4.devel/3328

http://www.mail-archive.com/[email protected]/msg10843.html

This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.

EDIT: Also note that in the data set you have, some of variance components are negative, which is not allowed using random effects with lme, so the results differ. A data set with all positive variance components can be created using a seed of 8. The results then agree. See this answer for details.

Also note that lme from nlme does not compute the denominator degrees of freedom correctly, so the F-statistics agree but not the p-values, and lmer from lme4 doesn't try too because it's very tricky in the presence of unbalanced crossed random effects, and may not even be a sensible thing to do. But that's more than I want to get into here.

What you're fitting with aov is called a strip plot, and it's tricky to fit with lme because the subject:A and subject:B random effects are crossed.

Your first attempt is equivalent to aov(Y ~ A*B + Error(subject), data=d), which doesn't include all the random effects; your second attempt is the right idea, but the syntax for crossed random effects using lme is very tricky.

Using lme from the nlme package, the code would be

lme(Y ~ A*B, random=list(subject=pdBlocked(list(~1, pdIdent(~A-1), pdIdent(~B-1)))), data=d)

Using lmer from the lme4 package, the code would be something like

lmer(Y ~ A*B + (1|subject) + (1|A:subject) + (1|B:subject), data=d)    

These threads from R-help may be helpful (and to give credit, that's where I got the nlme code from).

http://www.biostat.wustl.edu/archives/html/s-news/2005-01/msg00091.html

http://permalink.gmane.org/gmane.comp.lang.r.lme4.devel/3328

http://www.mail-archive.com/[email protected]/msg10843.html

This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.

EDIT: Also note that in the data set you have, some of variance components are negative, which is not allowed using random effects with lme, so the results differ. A data set with all positive variance components can be created using a seed of 8. The results then agree. See this answer for details.

Also note that lme from nlme does not compute the denominator degrees of freedom correctly, so the F-statistics agree but not the p-values, and lmer from lme4 doesn't try too because it's very tricky in the presence of unbalanced crossed random effects, and may not even be a sensible thing to do. But that's more than I want to get into here.

What you're fitting with aov is called a strip plot, and it's tricky to fit with lme because the subject:A and subject:B random effects are crossed.

Your first attempt is equivalent to aov(Y ~ A*B + Error(subject), data=d), which doesn't include all the random effects; your second attempt is the right idea, but the syntax for crossed random effects using lme is very tricky.

Using lme from the nlme package, the code would be

lme(Y ~ A*B, random=list(subject=pdBlocked(list(~1, pdIdent(~A-1), pdIdent(~B-1)))), data=d)

Using lmer from the lme4 package, the code would be something like

lmer(Y ~ A*B + (1|subject) + (1|A:subject) + (1|B:subject), data=d)    

These threads from R-help may be helpful (and to give credit, that's where I got the nlme code from).

http://www.biostat.wustl.edu/archives/html/s-news/2005-01/msg00091.html

http://permalink.gmane.org/gmane.comp.lang.r.lme4.devel/3328

http://www.mail-archive.com/[email protected]/msg10843.html

This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.

EDIT: Also note that in the data set you have, some of variance components are negative, which is not allowed using random effects with lme, so the results differ. A data set with all positive variance components can be created using a seed of 8. The results then agree. See this answer for details.

Also note that lme from nlme does not compute the denominator degrees of freedom correctly, so the F-statistics agree but not the p-values, and lmer from lme4 doesn't try too because it's very tricky in the presence of unbalanced crossed random effects, and may not even be a sensible thing to do. But that's more than I want to get into here.

What you're fitting with aov is called a strip plot, and it's tricky to fit with lme because the subject:A and subject:B random effects are crossed.

Your first attempt is equivalent to aov(Y ~ A*B + Error(subject), data=d), which doesn't include all the random effects; your second attempt is the right idea, but the syntax for crossed random effects using lme is very tricky.

Using lme from the nlme package, the code would be

lme(Y ~ A*B, random=list(subject=pdBlocked(list(~1, pdIdent(~A-1), pdIdent(~B-1)))), data=d)

Using lmer from the lme4 package, the code would be something like

lmer(Y ~ A*B + (1|subject) + (1|A:subject) + (1|B:subject), data=d)    

These threads from R-help may be helpful (and to give credit, that's where I got the nlme code from).

http://www.biostat.wustl.edu/archives/html/s-news/2005-01/msg00091.html

http://permalink.gmane.org/gmane.comp.lang.r.lme4.devel/3328

http://www.mail-archive.com/[email protected]/msg10843.html

This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.

EDIT: Also note that in the data set you have, some of variance components are negative, which is not allowed using random effects with lme, so the results differ. A data set with all positive variance components can be created using a seed of 8. The results then agree. See this answer for details.

Also note that lme from nlme does not compute the denominator degrees of freedom correctly, so the F-statistics agree but not the p-values, and lmer from lme4 doesn't try too because it's very tricky in the presence of unbalanced crossed random effects, and may not even be a sensible thing to do. But that's more than I want to get into here.

What you're fitting with aov is called a strip plot, and it's tricky to fit with lme because the subject:A and subject:B random effects are crossed.

Using lme from the nlme package, the code would be

lme(Y ~ A*B, random=list(subject=pdBlocked(list(~1, pdIdent(~A-1), pdIdent(~B-1)))), data=d)

Using lmer from the lme4 package, the code would be something like

lmer(Y ~ A*B + (1|subject) + (1|A:subject) + (1|B:subject), data=d)    

These threads from R-help may be helpful (and to give credit, that's where I got the nlme code from).

http://www.biostat.wustl.edu/archives/html/s-news/2005-01/msg00091.html

http://permalink.gmane.org/gmane.comp.lang.r.lme4.devel/3328

http://www.mail-archive.com/[email protected]/msg10843.html

This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.

EDIT: Also note that in the data set you have, some of variance components are negative, which is not allowed using random effects with lme, so the results differ. A data set with all positive variance components can be created using a seed of 8. The results then agree. See this answer for details.

Also note that lme from nlme does not compute the denominator degrees of freedom correctly, so the F-statistics agree but not the p-values, and lmer from lme4 doesn't try too because it's very tricky in the presence of unbalanced crossed random effects, and may not even be a sensible thing to do. But that's more than I want to get into here.

What you're fitting with aov is called a strip plot, and it's tricky to fit with lme because the subject:A and subject:B random effects are crossed.

Your first attempt is equivalent to aov(Y ~ A*B + Error(subject), data=d), which doesn't include all the random effects; your second attempt is the right idea, but the syntax for crossed random effects using lme is very tricky.

Using lme from the nlme package, the code would be

lme(Y ~ A*B, random=list(subject=pdBlocked(list(~1, pdIdent(~A-1), pdIdent(~B-1)))), data=d)

Using lmer from the lme4 package, the code would be something like

lmer(Y ~ A*B + (1|subject) + (1|A:subject) + (1|B:subject), data=d)    

These threads from R-help may be helpful (and to give credit, that's where I got the nlme code from).

http://www.biostat.wustl.edu/archives/html/s-news/2005-01/msg00091.html

http://permalink.gmane.org/gmane.comp.lang.r.lme4.devel/3328

http://www.mail-archive.com/[email protected]/msg10843.html

This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.

EDIT: Also note that in the data set you have, some of variance components are negative, which is not allowed using random effects with lme, so the results differ. A data set with all positive variance components can be created using a seed of 8. The results then agree. See this answer for details.

Also note that lme from nlme does not compute the denominator degrees of freedom correctly, so the F-statistics agree but not the p-values, and lmer from lme4 doesn't try too because it's very tricky in the presence of unbalanced crossed random effects, and may not even be a sensible thing to do. But that's more than I want to get into here.