Hi Bert,

I am very sorry to bother you again.

For the following question, as you suggested, I posted it in both Biostars 
website and stackexchange website, so far no reply.

I really hope that you can do me a great favor to share your points about how 
to explain the coefficients for drug A and drug B if run anova model (response 
variable = drug A + drug B). is it different from running three separate T 
tests?

Thank you so much!!

Ding

I need to analyze data generated from a partial two-by-two factorial design: 
two levels for drug A (yes, no), two levels for drug B (yes, no);  however, 
data points are available only for three groups, no drugA/no drugB, yes 
drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes 
drugB.  I think we can not investigate interaction between drug A and drug B, 
can I still run  model using R as usual:  response variable = drug A + drug B?  
any suggestion is appreciated.


From: Bert Gunter [mailto:bgunter.4...@gmail.com]
Sent: Friday, March 02, 2018 12:32 PM
To: Ding, Yuan Chun
Cc: r-help@r-project.org
Subject: Re: [R] data analysis for partial two-by-two factorial design

________________________________
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This list provides help on R programming (see the posting guide linked below 
for details on what is/is not considered on topic), and generally avoids 
discussion of purely statistical issues, which is what your query appears to 
be. The simple answer is yes, you can fit the model as described,  but you 
clearly need the off topic discussion as to what it does or does not mean. For 
that, you might try the stats.stackexchange.com<http://stats.stackexchange.com> 
statistical site.

Cheers,
Bert


Bert Gunter

"The trouble with having an open mind is that people keep coming along and 
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Fri, Mar 2, 2018 at 10:34 AM, Ding, Yuan Chun 
<ycd...@coh.org<mailto:ycd...@coh.org>> wrote:
Dear R users,

I need to analyze data generated from a partial two-by-two factorial design: 
two levels for drug A (yes, no), two levels for drug B (yes, no);  however, 
data points are available only for three groups, no drugA/no drugB, yes 
drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes 
drugB.  I think we can not investigate interaction between drug A and drug B, 
can I still run  model using R as usual:  response variable = drug A + drug B?  
any suggestion is appreciated.

Thank you very much!

Yuan Chun Ding


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