Dear Greg,
thanks so much. Now I understand!!!!!!

Thanks!!




________________________________
From: Greg Snow <Greg.Snow@imail.org>

<r-help@r-project.org>
Sent: Wed, January 5, 2011 11:27:53 PM
Subject: RE: [R] Comparing fitting models


The output with all three fits gives you 2 comparisons, fit1 vs. fit2 and fit2 
vs. fit3.

So using an alpha of 0.05, the 0.99 p-value is comparing model 2 (fit2) and
model 3 (fit1) and testing the null that they fit equally well with the 
differences being due to random chance.  The p-value is large so we cannot
reject that fit2 fits as well as fit1, so the interaction is not significantly 
different.  


Then the 0.23 p-value compares the models with and without stimulus (the 
interaction not being considered), again the p-value is larger than alpha so we 
don’t have the evidence to conclude that stimulus helps.

If you want to test stimulus and the interaction in a single step, then do
anova(fit3, fit1), giving the anova does the sequential tests.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow@imail.org
801.408.8111


Sent: Wednesday, January 05, 2011 2:46 PM
To: Greg Snow; r-help@r-project.org
Subject: Re: [R] Comparing fitting models

Dear Greg,
thanks for your answer, but that is not the point.

My question is another: from the data in the R prompt how
can I compare the models?
I don´t understand the output of anova(fit1, fit2, fit3)

What I have to look to understand something about the comparison of the models?

Look the output in R:
> anova(fit3, fit2, fit1) #compare models 
Analysis of Variance Table

Model 1: response ~ condition
Model 2: response ~ stimulus + condition
Model 3: response ~ stimulus + condition + stimulus:condition
  Res.Df    RSS Df Sum of Sq      F Pr(>F)
1    171 382.78                           
2    165 364.13  6    18.650 1.3628 0.2328
3    159 362.67  6     1.465 0.1071 0.9955

I don´t understand these data...what they say actually about the comparison 
between the three models?

Please enlighten me.

Thanks a lot


________________________________

From:Greg Snow <Greg.Snow@imail.org>

<r-help@r-project.org>
Sent: Wed, January 5, 2011 10:34:15 PM
Subject: RE: [R] Comparing fitting models

Just do anova(fit3, fit1)

This compares those 2 models directly.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow@imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Frodo Jedi
> Sent: Wednesday, January 05, 2011 10:10 AM
> To: r-help@r-project.org
> Subject: [R] Comparing fitting models
> 
> 
> Dear all,
> I have 3 models (from simple to complex) and I want to compare them in
> order to
> see if they fit equally well or not.
> From the R prompt I am not able to see where I can get this
> information.
> Let´s do an example:
> 
> fit1<- lm(response ~ stimulus + condition + stimulus:condition,
> data=scrd)
> #EQUIVALE A lm(response ~ stimulus*condition, data=scrd)
> 
> 
> fit2<- lm(response ~ stimulus + condition, data=scrd)
> 
> fit3<- lm(response ~ condition, data=scrd)
> 
> 
> > anova(fit2, fit1) #compare models
> Analysis of Variance Table
> 
> Model 1: response ~ stimulus + condition
> Model 2: response ~ stimulus + condition + stimulus:condition
>  Res.Df    RSS Df Sum of Sq      F Pr(>F)
> 1    165 364.13
> 2    159 362.67  6    1.4650 0.1071 0.9955
> 
> 
> > anova(fit3, fit2, fit1) #compare models
> Analysis of Variance Table
> 
> Model 1: response ~ condition
> Model 2: response ~ stimulus + condition
> Model 3: response ~ stimulus + condition + stimulus:condition
>  Res.Df    RSS Df Sum of Sq      F Pr(>F)
> 1    171 382.78
> 2    165 364.13  6    18.650 1.3628 0.2328
> 3    159 362.67  6    1.465 0.1071 0.9955
> 
> 
> 
> How can I understand that the simple model fits as good as the complex
> model
> (the one with the interaction)?
> 
> Thanks in advance
> 
> All the best
> 
> 
> 
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