Estimated Effects Not Balanced

Something does not make sense in R. It has to do with the question of
balance and unbalance.

*A<-factor(c(1,1,1,1,1,1,2,2,2,2,2,2))*
*B<-factor(c(1,1,2,2,3,3,1,1,2,2,3,3))*
*y<-rnorm(12)*
*mod<-aov(y~A+B)*

I was under the impression that the design is balanced ie order does not
effect the sums of squares. However, when I compute the anova R reports
that the Estimated Effects are Unbalanced. I thought that when all
combinations of levels of A and B have equal replications then the design
is called balanced. But, R tends to think that when not all levels of A and
levels of B have equal replication, then the "Estimated Effects are
unbalanced".... Is this the same as the design being unbalanced? Because
for the example below, where the error occured, the order does not matter
(which make me think that the design is balanced).


*Call:*
*   aov(formula = y ~ A + B)*

*Terms:*
*                        A         B Residuals*
*Sum of Squares   0.872572  0.025604 16.805706*
*Deg. of Freedom         1         2        10*

*Residual standard error: 1.296368*
*Estimated effects may be unbalanced*
--
Yours sincerely,
Justin

*I check my email at 9AM and 4PM everyday*
*If you have an EMERGENCY, contact me at +447938674419(UK) or
+60125056192(Malaysia)*

        [[alternative HTML version deleted]]

tmp <- data.frame(A=factor(c(1,1,1,1,1,1,2,2,2,2,2,2)),

mod <- aov(y ~ A+B, data=tmp)
summary(mod)

Something does not make sense in R. It has to do with the question of
balance and unbalance.

*A<-factor(c(1,1,1,1,1,1,2,2,2,2,2,2))*
*B<-factor(c(1,1,2,2,3,3,1,1,2,2,3,3))*
*y<-rnorm(12)*
*mod<-aov(y~A+B)*

I was under the impression that the design is balanced ie order does not
effect the sums of squares. However, when I compute the anova R reports
that the Estimated Effects are Unbalanced. I thought that when all
combinations of levels of A and B have equal replications then the design
is called balanced. But, R tends to think that when not all levels of A and
levels of B have equal replication, then the "Estimated Effects are
unbalanced".... Is this the same as the design being unbalanced? Because
for the example below, where the error occured, the order does not matter
(which make me think that the design is balanced).


*Call:*
*   aov(formula = y ~ A + B)*

*Terms:*
*                        A         B Residuals*
*Sum of Squares   0.872572  0.025604 16.805706*
*Deg. of Freedom         1         2        10*

*Residual standard error: 1.296368*
*Estimated effects may be unbalanced*
--
Yours sincerely,
Justin

*I check my email at 9AM and 4PM everyday*
*If you have an EMERGENCY, contact me at +447938674419(UK) or
+60125056192(Malaysia)*

        [[alternative HTML version deleted]]

The problem is that you have 12 observations and 1+2+10=13 degrees of freedom.
There should be 1 + 2 + 8 = 11 degrees of freedom.
Probably one of your variables is masked by something else in you workspace.
Protect yourself by using a data.frame

tmp <- data.frame(A=factor(c(1,1,1,1,1,1,2,2,2,2,2,2)),

+ B=factor(c(1,1,2,2,3,3,1,1,2,2,3,3)),
+ y=rnorm(12))

mod <- aov(y ~ A+B, data=tmp)
summary(mod)

            Df Sum Sq Mean Sq F value Pr(>F)
A            1  1.553   1.553   1.334  0.281
B            2  3.158   1.579   1.357  0.311
Residuals    8  9.311   1.164

On Mon, Aug 22, 2016 at 11:15 AM, Justin Thong <justinthong93 at gmail.com> wrote:

Something does not make sense in R. It has to do with the question of
balance and unbalance.

*A<-factor(c(1,1,1,1,1,1,2,2,2,2,2,2))*
*B<-factor(c(1,1,2,2,3,3,1,1,2,2,3,3))*
*y<-rnorm(12)*
*mod<-aov(y~A+B)*

I was under the impression that the design is balanced ie order does not
effect the sums of squares. However, when I compute the anova R reports
that the Estimated Effects are Unbalanced. I thought that when all
combinations of levels of A and B have equal replications then the design
is called balanced. But, R tends to think that when not all levels of A and
levels of B have equal replication, then the "Estimated Effects are
unbalanced".... Is this the same as the design being unbalanced? Because
for the example below, where the error occured, the order does not matter
(which make me think that the design is balanced).


*Call:*
*   aov(formula = y ~ A + B)*

*Terms:*
*                        A         B Residuals*
*Sum of Squares   0.872572  0.025604 16.805706*
*Deg. of Freedom         1         2        10*

*Residual standard error: 1.296368*
*Estimated effects may be unbalanced*
--
Yours sincerely,
Justin

*I check my email at 9AM and 4PM everyday*
*If you have an EMERGENCY, contact me at +447938674419(UK) or
+60125056192(Malaysia)*

        [[alternative HTML version deleted]]

Thanks, Rich. I didn't notice that!

-- Bert
Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Mon, Aug 22, 2016 at 1:43 PM, Richard M. Heiberger <rmh at temple.edu>
wrote:

The problem is that you have 12 observations and 1+2+10=13 degrees of

freedom.

There should be 1 + 2 + 8 = 11 degrees of freedom.
Probably one of your variables is masked by something else in you

workspace.

Protect yourself by using a data.frame

tmp <- data.frame(A=factor(c(1,1,1,1,1,1,2,2,2,2,2,2)),

+ B=factor(c(1,1,2,2,3,3,1,1,2,2,3,3)),
+ y=rnorm(12))

mod <- aov(y ~ A+B, data=tmp)
summary(mod)

            Df Sum Sq Mean Sq F value Pr(>F)
A            1  1.553   1.553   1.334  0.281
B            2  3.158   1.579   1.357  0.311
Residuals    8  9.311   1.164

On Mon, Aug 22, 2016 at 11:15 AM, Justin Thong <justinthong93 at gmail.com>

wrote:

Something does not make sense in R. It has to do with the question of
balance and unbalance.

*A<-factor(c(1,1,1,1,1,1,2,2,2,2,2,2))*
*B<-factor(c(1,1,2,2,3,3,1,1,2,2,3,3))*
*y<-rnorm(12)*
*mod<-aov(y~A+B)*

I was under the impression that the design is balanced ie order does not
effect the sums of squares. However, when I compute the anova R reports
that the Estimated Effects are Unbalanced. I thought that when all
combinations of levels of A and B have equal replications then the

design

is called balanced. But, R tends to think that when not all levels of A

and

levels of B have equal replication, then the "Estimated Effects are
unbalanced".... Is this the same as the design being unbalanced? Because
for the example below, where the error occured, the order does not

matter

(which make me think that the design is balanced).


*Call:*
*   aov(formula = y ~ A + B)*

*Terms:*
*                        A         B Residuals*
*Sum of Squares   0.872572  0.025604 16.805706*
*Deg. of Freedom         1         2        10*

*Residual standard error: 1.296368*
*Estimated effects may be unbalanced*
--
Yours sincerely,
Justin

*I check my email at 9AM and 4PM everyday*
*If you have an EMERGENCY, contact me at +447938674419(UK) or
+60125056192(Malaysia)*

        [[alternative HTML version deleted]]

posting-guide.html

posting-guide.html

Something does not make sense in R. It has to do with the question of
balance and unbalance.

*A<-factor(c(1,1,1,1,1,1,2,2,2,2,2,2))*
*B<-factor(c(1,1,2,2,3,3,1,1,2,2,3,3))*
*y<-rnorm(12)*
*mod<-aov(y~A+B)*