Richard Williams gave a good answer. A detail remains over output shown
as
a stop or period (.).
Just as with data, a numeric result shown as . is Stata's way of saying
missing, or don't know.
Just from missing, however, it is difficult to reverse engineer what
lies behind it. There are two main possibilities.
1. Stata did the calculation, but can't show a number because the
result is indeterminate.
. di 7/0
.
is a simple example. 7/0 is indeterminate.
2. Stata doesn't know what you are referring to.
. summarize foobar
. di r(SUM)
.
You ask for a display of r(SUM). But so such result is known to Stata.
It shrugs its shoulders and puts missing. (You might have meant r(sum).
Such typos are quite a common reason for a display of missing.)
Nick
[email protected]
Sara Mottram
I have fitted a partial proportional odds model in Stata 9.2 using
-gologit2-. In their book, Regression Models for Categorical Dependent
Data using Stata, Long and Freese suggest that McKelvey & Zavoina's R2
most closely approximates the R2 from linear regression models (which I
assume makes this the most suitable R2 to use). Following the advice on
Richard William's website to use the v1 option, I have been able to use
-fitstat- with -gologit2-, however, it does not produce the M&Z R2.
Please could someone tell me if this is because it does not make sense
with a partial proportional odds model, in which case, is there a more
appropriate measure of fit that I can use? Or do I need to specifically
ask for M&Z R2 via an option?
There was a similar question posed last year, but the solution suggested
to that post (see below) gives me an estimate of "." I'm not sure what
that means.
sysuse auto
logit foreign price
fitstat
estadd scalar r2_mz = r(r2_mz)
and then tabulate using:
estout ., eform cells(n se(par)) stats(r2_mz) style(fixed)
or:
esttab ., eform se scalars(r2_mz)
*
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