On Sep 20, 2010, at 11:53 AM, Jabez Wilson wrote:
Hi, I am trying to reconcile anova table in R (summary(lm)) with
individual t.test.
datafilename="http://personality-project.org/R/datasets/R.appendix1.data
"
data.ex1=read.table(datafilename,header=T) #read the data into a
table
summary(lm(Alertness~Dosage,data=data.ex1))
The quick answer is that in the ANOVA situation where you are
interpreting individual level parameters, you are testing for the
difference of a particular group from a shared mean (the intercept)
across all three groups, whereas with the t-test you are only
considering two groups at a time. (The default treatment contrasts do
not actually result in the intercept estimate being a global mean,
however.)
--
David.
gives:
Call:
lm(formula = Alertness ~ Dosage, data = data.ex1)
Residuals:
Min 1Q Median 3Q Max
-8.500 -2.437 0.250 2.687 8.500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.500 2.010 16.166 6.72e-11 ***
Dosageb -4.250 2.659 -1.598 0.130880
Dosagec -13.250 3.179 -4.168 0.000824 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.924 on 15 degrees of freedom
Multiple R-squared: 0.5396, Adjusted R-squared: 0.4782
F-statistic: 8.789 on 2 and 15 DF, p-value: 0.002977
As far as I understand it the lines "Dosageb" and "DosageC"
represent the difference between DosageA and the other two dosages.
My question is this: are these differences and the p-values
associated with them the same as a t.test or pairwise.t.test on
these groups? If I do t.tests, I get different values for t and p-
value from those in the anova table above.
Can someone please explain what the discrepancy is?
Thanks
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