Re: [R] MANOVA and Extra Sums-of-Squares Tests

2012-03-14 Thread chris33
Hi John,

Thanks again.  That looks like an easy and convenient approach.  Regards,

Chris

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[R] MANOVA and Extra Sums-of-Squares Tests

2012-03-13 Thread chris33
I would like to conduct an extra sum-of -squares test that compares a full
MANOVA model (with all 1st order interactions) to a reduced model (no
interactions) to determine if I can drop all interactions at the same time. 
This is analagous to an extra sum-of-squares F-test in ANOVA, but instead
using MANOVA.  Is there a command in R that does this?  If not, is there a
command that calculates residual sum-of-squares and cross-product matrices? 
Thanks.

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Re: [R] MANOVA and Extra Sums-of-Squares Tests

2012-03-13 Thread John Fox
Dear chris33,

You can use the anova() function to compare the two multivariate linear models. 
Alternatively, the Anova() function in the car package will compute type II 
or type III MANOVA tests, which aren't quite what you're asking about.

I hope this helps,
 John


John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/

On Tue, 13 Mar 2012 12:31:25 -0700 (PDT)
 chris33 malachow...@gmail.com wrote:
 I would like to conduct an extra sum-of -squares test that compares a full
 MANOVA model (with all 1st order interactions) to a reduced model (no
 interactions) to determine if I can drop all interactions at the same time. 
 This is analagous to an extra sum-of-squares F-test in ANOVA, but instead
 using MANOVA.  Is there a command in R that does this?  If not, is there a
 command that calculates residual sum-of-squares and cross-product matrices? 
 Thanks.
 
 --
 View this message in context: 
 http://r.789695.n4.nabble.com/MANOVA-and-Extra-Sums-of-Squares-Tests-tp4470077p4470077.html
 Sent from the R help mailing list archive at Nabble.com.
 
 __
 R-help@r-project.org mailing list
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Re: [R] MANOVA and Extra Sums-of-Squares Tests

2012-03-13 Thread chris33
Hi John,

Thanks for your response.  The anova funtion will not work in my case,
because I have multiple response variables.  In other words, I would like to
conduct an extra sums-of-squares and cross-products test between the
following models:

FULL.MODEL:   (Y1, Y2, Y3, Y4, Y5) as a function of  X1 + X2 + X3 + X4 +
X1*X2 +X1*X3 + X1*X4
REDUCED.MODEL:   (Y1, Y2, Y3, Y4, Y5) as a function of X1 + X2 + X3 + X4 

So, I suppose that I would need to calculate the residual sum-of-squares and
cross-product matrices for each of these models as a start.  Any ideas how I
would go about this in R?  Thanks again,

Chris   

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Re: [R] MANOVA and Extra Sums-of-Squares Tests

2012-03-13 Thread John Fox
Dear chris33,

Well, actually as I said, the anova() function *will* do what you want. You can 
fit multivariate linear models with lm(),

mod.1 - lm(cbind(Y1, Y2, Y3, Y4, Y5) ~ X1*X2 +X1*X3 + X1*X4)
mod.2 - lm(cbind(Y1, Y2, Y3, Y4, Y5) ~ X1 + X2 + X3 + X4)

and then use anova() to get multivariate tests,

anova(mod.1, mod.2)

See ?anova.mlm for more information.

Best,
 John

On Tue, 13 Mar 2012 14:49:01 -0700 (PDT)
 chris33 malachow...@gmail.com wrote:
 Hi John,
 
 Thanks for your response.  The anova funtion will not work in my case,
 because I have multiple response variables.  In other words, I would like to
 conduct an extra sums-of-squares and cross-products test between the
 following models:
 
 FULL.MODEL:   (Y1, Y2, Y3, Y4, Y5) as a function of  X1 + X2 + X3 + X4 +
 X1*X2 +X1*X3 + X1*X4
 REDUCED.MODEL:   (Y1, Y2, Y3, Y4, Y5) as a function of X1 + X2 + X3 + X4 
 
 So, I suppose that I would need to calculate the residual sum-of-squares and
 cross-product matrices for each of these models as a start.  Any ideas how I
 would go about this in R?  Thanks again,
 
 Chris   
 
 --
 View this message in context: 
 http://r.789695.n4.nabble.com/MANOVA-and-Extra-Sums-of-Squares-Tests-tp4470077p4470459.html
 Sent from the R help mailing list archive at Nabble.com.
 
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 and provide commented, minimal, self-contained, reproducible code.

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