Here's what I get:
> summary(lm(yield ~ prevyield + trt + block))
Call:
lm(formula = yield ~ prevyield + trt + block)
Residuals:
Min 1Q Median 3Q Max
-22.616 -9.254 2.051 10.687 19.421
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 38.5797 26.5447 1.453 0.16816
prevyield 28.4010 3.3840 8.393 7.8e-07 ***
trtB -13.9099 11.7844 -1.180 0.25752
trtC -6.4099 11.7844 -0.544 0.59505
trtD 0.6605 11.9128 0.055 0.95657
trtE 20.4409 12.2329 1.671 0.11692
trtO -29.1408 12.1256 -2.403 0.03068 *
block2 1.6396 9.7127 0.169 0.86836
block3 -22.6886 11.6961 -1.940 0.07282 .
block4 44.7776 12.7351 3.516 0.00342 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 16.66 on 14 degrees of freedom
Multiple R-Squared: 0.9461, Adjusted R-squared: 0.9114
F-statistic: 27.29 on 9 and 14 DF, p-value: 2.276e-07
What does R consider balanced anyway? I've had data with the same obs
per trt and R complains about it being unbalanced...
Yeah, the covariate is in bushels and the yield is in pounds... but I
don't get why the means of the models with and without covariate would
change. The SE's are another story, but the means?
Thanks
Peter Dalgaard wrote:
>Damián Cirelli <[EMAIL PROTECTED]> writes:
>
>
>>Dear R gurus,
>>I have the following model:
>>
>>appcov.aov <- aov(yield ~ prevyield + trt + block)
>>
>>where prevyield is a continuous numeric covariate and trt and block
>>are factors (yes, I did factor()!)
>>Now, when I do a TukeyHSD, my diff's are all screwed up!
>>For instance:
>>treatment mean for treatmen "E" is 277.25 and for treatment "O" is
>>279.5, so I figure the diff O-E should be 2.25, but TukeyHSD says:
>>
>> diff lwr upr
>>O-E -50.817101 -84.8112057 -16.822996
>>
>>So I wonder where is that -50.8 coming from???
>>
>>Anybody have a clue?
>>
>>Thanks a lot!
>>
>>PS: it works if I take prevyield (the covariate) out of the model, but
>>the point is I need to analyse it with the covariate.
>>Thanks again
>>
>
>If the covariate level differs between the treatment groups, then the
>difference in the covariate-adjusted means could well differ quite a
>bit from the unadjusted difference. What happens if you do
>
>summary(lm(yield ~ prevyield + trt + block))
>
>(Not sure I'm happy about using the HSD procedure with an unbalanced
>design, btw.)
>
>
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