Re: [R] pairwise.t.test vs. t. test

From: Chuck Cleland <ccleland_at_optonline.net>
Date: Thu 07 Sep 2006 - 22:44:00 GMT

MARK LEEDS wrote:

> no, because the formula for the test statistics ( even assuming that 
> variances are equal ) of the two different tests are different. in the 
> pairwise t test, the pairwise differences are
> viewed as one sample so it turns into a one sample test. any intro stat book 
> will have the formulas.
>                                                                              
>      mark

  Actually, I think the difference is due to the SD being pooled across all 3 groups in the pairwise.t.test, but just 2 groups in t.test.

> ----- Original Message ----- 
> From: "Li,Qinghong,ST.LOUIS,Molecular Biology" <Qinghong.Li@rdmo.nestle.com>
> To: <r-help@stat.math.ethz.ch>
> Sent: Thursday, September 07, 2006 5:07 PM
> Subject: [R] pairwise.t.test vs. t. test
> 
> 

>> Hi,
>>
>> If I set the p.adjust="none", does it meant that the output p values from
>> the pairwise.t.test will be the same as those from individual t.tests (set
>> var.equal=T, alternative="t")?
>>
>> I actually got different p values from the two tests. See below. Is it
>> supposed to be this way?
>>
>> Thanks
>> Johnny
>>
>>> x
>> [1] 61.6 52.7 61.3 65.2 62.8 63.7 64.8 58.7 44.9 57.0 64.3 55.1 50.0 41.0
>> [15] 43.0 45.9 52.2 45.5 46.9 31.6 40.6 44.8 39.4 31.0 37.5 32.6 23.2 34.6
>> [29] 38.3 38.1 19.5 21.2 15.8 33.3 28.6 25.8
>>> Grp
>> [1] Yng Yng Yng Yng Yng Yng Yng Yng Yng Yng Yng Yng Med Med Med Med Med
>> Med
>> [19] Med Med Med Med Med Med Old Old Old Old Old Old Old Old Old Old Old
>> Old
>> Levels: Yng Med Old
>>> pairwise.t.test(x=x,g=Grp,p.adjust.method="none")
>> Pairwise comparisons using t tests with pooled SD
>>
>> data: x and Grp
>>
>> Yng Med
>> Med 1.0e-06 -
>> Old 2.0e-12 2.6e-05
>>
>> P value adjustment method: none
>>
>>
>>> t.test(x=x[1:12],y=x[25:36],var.equal=T, alternative="t")
>> Two Sample t-test
>>
>> data: x[1:12] and x[25:36]
>> t = 10.5986, df = 22, p-value = 4.149e-10
>> alternative hypothesis: true difference in means is not equal to 0
>> 95 percent confidence interval:
>> 24.37106 36.22894
>> sample estimates:
>> mean of x mean of y
>> 59.34167 29.04167
>>
>> [[alternative HTML version deleted]]
>>
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>>
> 
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-- 
Chuck Cleland, Ph.D.
NDRI, Inc.
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Received on Fri Sep 08 08:49:40 2006

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