[BioC] question: fit2$F.p.value

Fri Aug 11 15:29:53 CEST 2006

Hi Gordon,

Thanks for the confirmation and the documentation changes. After 
responding to the original email, I realized that it should have been 
obvious anyway - how could something like decideTests(fit, method = 
"nestedF") ever work if the F test were MSR/MSE?

Best,

Jim

Gordon Smyth wrote:
> Hi James,
> 
> Thanks for handingly this query. In the limma documentation, the term 
> "coefficient" is consistently used to refer to the coefficients 
> multiplying the columns of the design matrix, whereas "contrast" is 
> consistently used to refer to the columns specified by contrasts.fit(). 
> In other words, the coefficients are the columns in the lmFit() fit 
> while the contrasts are the columns given to eBayes(). When the 
> documentation says that the F-test corresponds to "all contrasts", it 
> means all the contrasts in the above sense. In other words, the F-test 
> always corresponds to the columns in the same fitted model object. It is 
> an overall test statistic computed from the set of t-statistics in the 
> same object. This has to be so, because there is no other way in limma 
> to extract F-tests for particular subjects of contrasts.
> 
> If someone as familiar with limma as you found this unclear, then it 
> must be unclear, so I've revised the help for ?eBayes to try to make it 
> more transparent. The description of the output component F now reads
> 
> "numeric vector of moderated F-statistics for testing all contrasts 
> defined by the columns of \code{fit} simultaneously equal to zero"
> 
> The details section says
> 
> "The empirical Bayes moderated t-statistics test each individual 
> contrast equal to zero. The moderated F-statistics test whether all the 
> contrasts are zero for each probe (row). For each probe, the F-statistic 
> is an overall test computed from the set of t-statistics for that probe. 
> This is exactly analogous the relationship between t-tests and 
> F-statistics in conventional anova, except that the residual mean 
> squares and residual degrees of freedom have been moderated between 
> probes."
> 
> Best wishes
> Gordon
> 
>> Date: Wed, 09 Aug 2006 15:49:37 -0400
>> From: "James W. MacDonald" <jmacdon at med.umich.edu>
>> Subject: Re: [BioC] question: fit2$F.p.value
>> To: "Mistretta, Toni-Ann" <toniannm at bcm.tmc.edu>
>> Cc: bioconductor at stat.math.ethz.ch
>> Message-ID: <44DA3C51.7030407 at med.umich.edu>
>> Content-Type: text/plain;  charset="utf-8";  format=flowed
>>
>> Hi Scott,
>>
>> Mistretta, Toni-Ann wrote:
>> >
>> >
>> > Hello,
>> >
>> >
>> > I have fit a group means parameterization to my three samples: A, B,
>> > C (fit1).  I used contrasts.fit to fit my contrasts of interest: B-A,
>> > and C-A (fit2).  My question: does fit2$F.p.value apply to the
>> > differences between the three samples A, B, and C (similar to a
>> > one-way ANOVA) or does it apply to the differences between the two
>> > contrasts B-A and C-A?  I really need someone to clarify this point
>> > for me before I go on and select differentially expressed genes.  In
>> > my case treatments B and C are very similar so this point is almost
>> > mute.  However, I will be analyzing data sets in the future where
>> > treatments B and C are very different making my question very
>> > important.
>>
>> Well, I was working under the assumption that the F-statistic was the
>> 'usual' F-stat (MSR/MSE), but apparently I was mistaken. The help page
>> for the MArrayLM class states:
>>
>> 'F.stat': 'numeric' vector giving moderated F-statistics for
>>            testing all contrasts equal to zero
>>
>> 'F.p.value': 'numeric' vector giving p-value corresponding to
>>            'F.stat'
>>
>> That's not perfectly clear (all contrasts could be 'all possible
>> contrasts', yes?). So a test:
>>
>>  > library(fibroEset)
>>  > data(fibroEset)
>>  > library(limma)
>>  > design <- model.matrix(~0+pData(fibroEset)[,2])
>>  > contrast <- matrix(c(-1,1,0))
>>  > fit <- lmFit(log2(exprs(fibroEset)), design)
>>  > fit2 <- contrasts.fit(fit, contrast)
>>  > fit2 <- eBayes(fit2)
>>  > contrast <- matrix(c(-1,1,0,-1,0,1,0,-1,1), nc=3)
>>  > fit3 <- contrasts.fit(fit, contrast)
>>  > fit3 <- eBayes(fit3)
>>  > cbind(fit2$F,fit3$F)[1:10,]
>>                [,1]       [,2]
>>   [1,] 15.118961041  7.7401961
>>   [2,] 11.105009661 19.8022284
>>   [3,]  0.007904608  1.4477756
>>   [4,]  1.748568559  1.3576871
>>   [5,]  0.187640831  4.1238891
>>   [6,]  1.633728790  0.8174006
>>   [7,]  2.116625209  1.7426943
>>   [8,]  0.208801377  0.5223336
>>   [9,]  9.813763553 10.9888647
>> [10,]  0.010995645  0.8344381
>>
>> Looks like 'all contrasts' means 'all specified contrasts'.
>>
>> HTH,
>>
>> Jim
>>
>>
>>
>> >
>> > Scott Ochsner Baylor College of Medicine One Baylor Plaza Houston,
>> > TX. 77030
>> >
>> >
>> > thanks,
>> >
>> > S
>> >
>> >
>> > Sent for S. Ochsner by TAM
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > _______________________________________________ Bioconductor mailing
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>>
>>
>> -- 
>> James W. MacDonald, M.S.
>> Biostatistician
>> Affymetrix and cDNA Microarray Core
>> University of Michigan Cancer Center
>> 1500 E. Medical Center Drive
>> 7410 CCGC
>> Ann Arbor MI 48109
>> 734-647-5623
> 
> 

-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623

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