bout statistics is about as
sensible as taking a statistician's advice about chemistry - the mileage can
vary.
S Ellison
> -Original Message-
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Austin Paul
> Sent: 27 September 2011 0
if this is making sense to you.
Regards,
Indrajit
From: Austin Paul
Cc: "r-help@r-project.org"
Sent: Tuesday, September 27, 2011 12:30 PM
Subject: Re: [R] two-way anova help
Hi Indrajit and Bert,
Â
I really appeciate your help. I have coded as
lication. Your model form will remain the same.
>
> Regards,
> Indrajit
>
> --
> *From:* Austin Paul
> *To:* Indrajit Sengupta
> *Cc:* "r-help@r-project.org"
> *Sent:* Tuesday, September 27, 2011 10:57 AM
> *Subject:* Re: [R] t
Tanks are the experimental unit, fishes within tanks are repeated measures
of the treatment, there is no "nesting" of replicates. You can analyze the
45 x 50 individual data values by repeated measures (mixed effects models)
or by summarizing the 50 measurements per tank=treatment into a single val
___
To: Austin Paul
Cc: "r-help@r-project.org"
Sent: Tuesday, September 27, 2011 12:10 PM
Subject: Re: [R] two-way anova help
Hi Paul,
There should not be any problem. Here is how I visualize the data table looks
like:
Obs Male_type Â
Female_typeÂ
Response
1 1 1 34
2 1 1 44
model form will remain the same.
Regards,
Indrajit
From: Austin Paul
Cc: "r-help@r-project.org"
Sent: Tuesday, September 27, 2011 10:57 AM
Subject: Re: [R] two-way anova help
Hi,
Yes. As I explained, the three male and three female types were
Hi,
Yes. As I explained, the three male and three female types were crossed in
all combinations (9 ways). For each of the 9 types, I have *5 replicate
tanks* (45 total tanks). And from each of the 45 tanks I have 50
observations for size. So the 5 replicates are somehow nested within the
two-w
Can you explain what do you mean by "5 replicate tanks"?
Doing a two way anova is very simple in R. You would need to fit a linear model
(lm function).
Eg.:
> model <- lm(y ~ male + female + male:female, data =)
Regards,
Indrajit
From: Austin Paul
To: r-
the following works. i don't exactly what happens here. I guess "lm"
might treat S1 and S2 as quantitative variables, not qualitative
variables.
cheers,
Zhiliang
S1 <- as.character(Data[,1])
S1 <- as.factor(S1)
S2 <- as.character(Data[,2])
S2 <- as.factor(S2)
data <- data.frame(S1=S1, S2=S2, ExM=
On Jul 8, 2009, at 12:11 PM, Greg Snow wrote:
Well, since we don't have Data.txt it is kind of hard for us to
replicate what you have done.
Here goes a guess as to what the problem may be.
Have you told R anywhere that S1 and S2 are factors with 6 levels
rather than numeric vectors? Or are
Well, since we don't have Data.txt it is kind of hard for us to replicate what
you have done.
Here goes a guess as to what the problem may be.
Have you told R anywhere that S1 and S2 are factors with 6 levels rather than
numeric vectors? Or are you just hoping that the computer can read your mi
Using traditional ANOVA, you'd have to drop either cases or time
points with missing data. Using linear mixed effects analysis, you'd
be able to use all the data. LME also has the benefit of *not*
assuming sphericity, which is good for data like yours (many measures
across few cases) where the trad
Hi,
your post is hardly readable because it is so spread out over several pages.
Can you repost it?
Besides that I do not understand your question fully. The y is your
dependent variable and as it looks the A*B would be Sulfur*Nitrogen if these
are your variable names.
You can also take a look
Keith
Try names(sumpcf) or str(sumpcf). That should help you find what you
want.
Incidentally, simply summary(pcf.aov) will do - R uses the appropriate
method based on the type of object.
HTH
Peter Alspach
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