Dear R-Helpers
(1)
After a night's sleep, I realized why the other helpers think differently
from me.
I agree with others that it may be better to use multi-stratum model but I
was a bit surprised since they seem to think 'block' variable should *not*
be a fixed effect.
A. Others seemed to think,
I agree that it is better to use your way.
However, in my defense, I thought simply how to recover the specific numbers
that Kevin wants to get.
> I don't understand why R doesn't output a value for F and Pr for the
> Error (Block) dimension, as my textbook shows 12.807 and 0.0015
> respectively.
Bert Gunter wrote:
>
> Let's be careful here. aov() treats block as a **random** error component
> of
> variance. lm() treats block as a **fixed effect**. That's a different
> kettle of fish. Perhaps both Kevin and the authors of his textbook need to
> read up on fixed versus random effects an
I found that you can do the same thing with 'aov' as well.
Sorry for any confusion. :)
> model.aov <- aov(Score.changes ~ Therapy + Block, data=table1)
> summary(model.aov)
Df Sum Sq Mean Sq F value Pr(>F)
Therapy 2 260.93 130.47 15.259 0.001861 **
Block4 438.00 109.5
what sorts
of tests make sense for each.
Bert Gunter
Genentech Nonclinical Statistics
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of T.K.
Sent: Tuesday, December 04, 2007 2:46 PM
To: Zembower, Kevin
Cc: r-help@r-project.org
Subject: Re: [R] Lear
This seems to work.
The trick is to use 'lm' instead of 'aov'.
> model.aov <- lm(Score.changes ~ factor(Therapy) + factor(Block),
data=table)
> anova(model.aov)
Analysis of Variance Table
Response: Score.changes
Df Sum Sq Mean Sq F value Pr(>F)
factor(Therapy) 2 260.93 130.47
We just studied randomized block design analysis in my statistics class,
and I'm trying to learn how to do them in R. I'm trying to duplicate a
case study example from my textbook [1]:
> # Case Study 13.2.1, page 778
> cd <- c(8, 11, 9, 16, 24)
> dp <- c(2, 1, 12, 11, 19)
> lm <- c(-2, 0, 6, 2, 11
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