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:dwinsem...@comcast.net]
Sent: Saturday, August 20, 2011 6:02 PM
To: Andrew Campomizzi
Cc: r-help@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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