[R] Calculating p-value for 1-tailed test in a linear model

David Winsemius dwinsemius at comcast.net
Mon Aug 22 16:12:10 CEST 2011


On Aug 22, 2011, at 9:44 AM, Andrew Campomizzi wrote:

> David,
> It's fair to question my intentions.  I'm running power analyses using
> simulations (based on Bolker's Ecological Models and Data in R) and  
> need to
> provide decision-makers with options.  So, I'm attempting to make it  
> clear
> that if the research hypothesis (e.g., response variable declines  
> with an
> increase in predictor variable) can be clearly answered with a 1- 
> tailed
> test, then one might need a sample size of n to get a particular  
> power,
> given variance and alpha.

So the possibility that the response variable will be increased by the  
predictor variable is known to be false? It would be unusual to have  
such prior knowledge but I suppose it is possible if the starting  
point is at the ceiling, but then typical regression methods may not  
be appropriate.

> I think Mark's response answers my question.

Mark's response was not copied to the list.

-- 
David.
> Thanks,
> Andy
>
> -----Original Message-----
> From: David Winsemius [mailto:dwinsemius at comcast.net]
> Sent: Saturday, August 20, 2011 6:02 PM
> To: Andrew Campomizzi
> Cc: r-help at r-project.org
> Subject: Re: [R] Calculating p-value for 1-tailed test in a linear  
> model
>
>
> On Aug 19, 2011, at 6:20 PM, Andrew Campomizzi wrote:
>
>> Hello,
>>
>> I'm having trouble figuring out how to calculate a p-value for a 1-
>> tailed
>> test of beta_1 in a linear model fit using command lm.  My model has
>> only 1
>> continuous, predictor variable.  I want to test the null hypothesis
>> beta_1
>> is >= 0.  I can calculate the p-value for a 2-tailed test using the
>> code
>> "2*pt(-abs(t-value), df=degrees.freedom)", where t-value and
>> degrees.freedom
>> are values provided in the summary of the lm.  The resulting p-value
>> is the
>> same as provided by the summary of the lm for beta_1.  I'm unsure
>> how to
>> change my calculation of the p-value for a 1-tailed test.
>
> You need to clearly state your hypothesis. Then using the output from
> the regression function should be straightforward.
>
> (Yes. this is a intentionally vague answer designed to elicit further
> information about your understanding of the statistical issues and how
> they relate to your domain knowledge. Many time peole already have the
> data and because they didn't get the answer they wanted, they search
> for other ways to "game the system" by ad-hoc changes in the
> statistical "rules of the road".)
>
> --
>
> David Winsemius, MD
> West Hartford, CT
>
>

David Winsemius, MD
West Hartford, CT



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