On 25/06/2012 09:32, Rune Haubo wrote:
According to standard likelihood theory these are actually not
t-values, but z-values, i.e., they asymptotically follow a standard
normal distribution under the null hypothesis. This means that you
Whose 'standard'?
It is conventional to call a value of t-like statistic (i.e. a ratio of
the form value/standard error) a 't-value'. And that is nothing to do
with 'likelihood theory' (t statistics predate the term 'likelihood'!).
The separate issue is whether a t statistic is even approximately
t-distributed (and if so, on what df?), and another is if it is
asymptotically normal. For the latter you have to say what you mean by
'asymptotic': we have lost a lot of the context, but as this does not
appear to be IID univariate observations:
- 'standard likelihood theory' is unlikely to apply.
- standard asymptotics may well not be a good approximation (in
regression modelling, people tend to fit more complex models to large
datasets, which is often why a large dataset was collected).
- even for IID observations the derivation of the t distribution assumes
normality.
The difference between a t distribution and a normal distribution is
practically insignificant unless the df is small. And if the df is
small, one can rarely rely on the CLT for approximate normality ....
could use pnorm instead of pt to get the p-values, but an easier
solution is probably to use the clm-function (for Cumulative Link
Models) from the ordinal package - here you get the p-values
automatically.
Cheers,
Rune
On 23 June 2012 07:02, Bert Gunter <gunter.ber...@gene.com> wrote:
This advice is almost certainly false!
A "t-statistic" can be calculated, but the distribution will not
necessarily be student's t nor will the "df" be those of the rse. See, for
example, rlm() in MASS, where values of the t-statistic are given without p
values. If Brian Ripley says that p values cannot be straightforwardly
calculated by pt(), then believe it!
-- Bert
On Fri, Jun 22, 2012 at 9:30 PM, Özgür Asar <oa...@metu.edu.tr> wrote:
Michael,
Try
?pt
Best
Ozgur
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