On Jun 17, 2010, at 12:51 AM, Worik R wrote:
If it were not for the fact that I get inconsistent results I would
be sure
that I need...
I have not yet seen a proper challenge to your original assumption
that the t-statistic should be the same as the p-value. They go in
_opposite_ directions, so the fact that in one comparison they happen
to be equal is either an unlikely random occurrence (or perhaps the
intended consequence of a devious statistics instructor testing your
knowledge of the basics.) P-values are bounded on the interval [0-1]
while t-statistics have possible range [-Inf,Inf]
2*pt(stat, df)
Section 8.1 of R-intro.pdf is explicit.
Problem is it gives inconsistent results
It? "Inconsistent"? The impersonal pronoun should be strongly
disparaged in technical discourse in my opinion.
--
David.
Worik
On Thu, Jun 17, 2010 at 10:30 AM, Worik R <wor...@gmail.com> wrote:
I have two pairs of related vectors
x1,y1
and
x2,y2
I wish to do a test for differences in means of x1 and y1, ditto x2
and y2.
I am getting odd results
I am not sure I am using 'pt' properly...
I have not included the raw vectors as they are long. I am
interested if I
am using R properly...
c(length(x1), length(y1), length(x2), length(y2))
[1] 3436 1619 2677 2378
First where the T-stat and the DF do not give the same result as
't.test'
when passed into 'pt'
t.1 <- t.test(x1, y1)
2 * pt(t.1$statistic, t.1$parameter)
t
1.353946
t.1$p.value
[1] 0.646054
I would have thought these would have been the same. Like below....
t.2 <- t.test(x2, y2)
2 * pt(t.2$statistic, t.2$parameter)
t
0.8679732
t.2$p.value
[1] 0.8679732
This is what I expect.
clearly I misunderstand some thing. What is it?
cheers
Worik
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