[R] how to constrast with factorial experiment

[szhan at uoguelph.ca]

Hello, R users,
I have two factors (treat, section) anova design experiment where  
there are 3 replicates. The objective of the experiment is to test if  
there is significant difference of yield between top (section 9 to 11)  
and bottom (section 9 to 11) of the fruit tree under treatment. I  
found that there are interaction between two factors. I wonder if I  
can contrast means from levels of one factor (section) under another  
factor (treat)? if so, how to do it in R and how to interpret the  
output?
Here is the data and commands I used to test the differece between  
section 1 to 8 and 9 to 11 under treatment. But I don't know if I was  
right, how to interpret the out and whether there are significant  
difference between section 1 to 8 and section 9 to 11 under treatment.

yield	replicate	treat	section
35.55	1	Ctl	1
53.70	1	Ctl	2
42.79	1	Ctl	3
434.81	1	Ctl	4
705.96	1	Ctl	5
25.91	1	Ctl	6
57.53	1	Ctl	7
41.45	1	Ctl	8
85.54	1	Ctl	9
51.23	1	Ctl	10
188.24	1	Ctl	11
35.71	2	Ctl	1
45.15	2	Ctl	2
40.10	2	Ctl	3
312.76	2	Ctl	4
804.05	2	Ctl	5
28.22	2	Ctl	6
68.51	2	Ctl	7
46.15	2	Ctl	8
123.14	2	Ctl	9
33.78	2	Ctl	10
121.28	2	Ctl	11
30.96	3	Ctl	1
36.10	3	Ctl	2
47.19	3	Ctl	3
345.80	3	Ctl	4
644.61	3	Ctl	5
27.73	3	Ctl	6
56.63	3	Ctl	7
42.63	3	Ctl	8
61.25	3	Ctl	9
59.43	3	Ctl	10
109.87	3	Ctl	11
143.50	1	Trt	1
82.76	1	Trt	2
125.03	1	Trt	3
493.76	1	Trt	4
868.48	1	Trt	5
45.09	1	Trt	6
249.43	1	Trt	7
167.28	1	Trt	8
274.72	1	Trt	9
176.40	1	Trt	10
393.10	1	Trt	11
93.75	2	Trt	1
63.83	2	Trt	2
117.50	2	Trt	3
362.68	2	Trt	4
659.40	2	Trt	5
62.10	2	Trt	6
218.24	2	Trt	7
210.98	2	Trt	8
291.48	2	Trt	9
209.36	2	Trt	10
454.68	2	Trt	11
119.62	3	Trt	1
66.50	3	Trt	2
87.37	3	Trt	3
414.01	3	Trt	4
707.70	3	Trt	5
44.40	3	Trt	6
142.59	3	Trt	7
137.37	3	Trt	8
181.03	3	Trt	9
131.65	3	Trt	10
310.18	3	Trt	11

> dat1<-read.delim("c:/testcontr.txt", header=T)
> dat1$treat<-as.factor(dat1$treat)
> dat1$replicate<-as.factor(dat1$replicate)
> dat1$section<-as.factor(dat1$section)
> attach(dat1)
> obj<-lm(log2(yield)~treat*section)
> anova(obj)
Analysis of Variance Table

Response: log2(yield)
               Df Sum Sq Mean Sq  F value    Pr(>F)
treat          1 24.608  24.608 297.8649 < 2.2e-16 ***
section       10 99.761   9.976 120.7565 < 2.2e-16 ***
treat:section 10  6.708   0.671   8.1197 2.972e-07 ***
Residuals     44  3.635   0.083
---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

> contrasts(section)<-c(3,3,3,3,3,3,3,3,-8,-8,-8)
> objnew<-lm(log2(yield)~treat*section)
> summary(objnew)

Call:
lm(formula = log2(yield) ~ treat * section)

Residuals:
      Min       1Q   Median       3Q      Max
-0.49647 -0.14913 -0.01521  0.17471  0.51105

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)         6.288403   0.050034 125.682  < 2e-16 ***
treatTrt            1.221219   0.070759  17.259  < 2e-16 ***
section1           -0.008502   0.010213  -0.832 0.409675
section2           -0.491175   0.165945  -2.960 0.004942 **
section3            2.569427   0.165945  15.484  < 2e-16 ***
section4            3.556067   0.165945  21.429  < 2e-16 ***
section5           -1.157069   0.165945  -6.973 1.25e-08 ***
section6           -0.003562   0.165945  -0.021 0.982971
section7           -0.487770   0.165945  -2.939 0.005223 **
section8            0.106181   0.165945   0.640 0.525585
section9           -0.776882   0.165945  -4.682 2.74e-05 ***
section10           0.759168   0.165945   4.575 3.87e-05 ***
treatTrt:section1  -0.049000   0.014444  -3.392 0.001474 **
treatTrt:section2   0.160825   0.234682   0.685 0.496757
treatTrt:section3  -0.949101   0.234682  -4.044 0.000208 ***
treatTrt:section4  -1.118870   0.234682  -4.768 2.07e-05 ***
treatTrt:section5  -0.295937   0.234682  -1.261 0.213950
treatTrt:section6   0.538638   0.234682   2.295 0.026549 *
treatTrt:section7   0.796518   0.234682   3.394 0.001468 **
treatTrt:section8  -0.548744   0.234682  -2.338 0.023984 *
treatTrt:section9  -0.191029   0.234682  -0.814 0.420033
treatTrt:section10 -0.556642   0.234682  -2.372 0.022137 *
---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Residual standard error: 0.2874 on 44 degrees of freedom
Multiple R-Squared: 0.973,      Adjusted R-squared: 0.9601
F-statistic: 75.55 on 21 and 44 DF,  p-value: < 2.2e-16

Thanks,
Joshua