{smcl}
{* 14dec2006}{...}
{hline}
help for {hi:invgaussfit}
{hline}
{title:Fitting a two-parameter inverse Gaussian distribution by maximum likelihood}
{p 8 17 2}
{cmd:invgaussfit}
{it:varname}
[{it:weight}]
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[{cmd:,}
{cmdab:mu:var(}{it:varlist1}{cmd:)}
{cmdab:lambda:var(}{it:varlist2}{cmd:)}
{cmdab:r:obust}
{cmdab:cl:uster(}{it:clustervar}{cmd:)}
{cmdab:l:evel(}{it:#}{cmd:)}
{it:maximize_options} ]
{p 4 4 2}{cmd:by} {it:...} {cmd::} may be used with
{cmd:invgaussfit}; see help {help by}.
{p 4 4 2}{cmd:fweight}s and {cmd:aweight}s are allowed;
see help {help weights}.
{title:Description}
{p 4 4 2} {cmd:invgaussfit} fits by maximum likelihood a two-parameter
inverse Gaussian distribution to a distribution of a variable
{it:varname}. The distribution has probability density function for
variable x > 0, location parameter m > 0 and scale parameter
l > 0 of {bind:(l / 2 pi x^3)^(1/2) exp((-l (x - m)^2 / 2 m^2 x))}.
{title:Options}
{p 4 8 2}{cmd:muvar(}{it:varlist1}{cmd:)} and
{cmd:lambdavar(}{it:varlist2}{cmd:)} allow the user to specify each parameter as
a function of the covariates specified in the respective variable list. A
constant term is always included in each equation.
{p 4 8 2}{cmd:robust} specifies that the Huber/White/sandwich estimator of
variance is to be used in place of the traditional calculation; see
{hi:[U] 20.14 Obtaining robust variance estimates}. {cmd:robust} combined with
{cmd:cluster()} allows observations which are not independent within cluster
(although they must be independent between clusters).
{p 4 8 2}{cmd:cluster(}{it:clustervar}{cmd:)} specifies that the observations
are independent across groups (clusters) but not necessarily within groups.
{it:clustervar} specifies to which group each observation belongs; e.g.,
{cmd:cluster(personid)} in data with repeated observations on individuals. See
{hi:[U] 20.14 Obtaining robust variance estimates}. Specifying {cmd:cluster()}
implies {cmd:robust}.
{p 4 8 2}{cmd:level(}{it:#}{cmd:)} specifies the confidence level, in percent,
for the confidence intervals of the coefficients; see help {help level}.
{p 4 8 2}{cmd:nolog} suppresses the iteration log.
{p 4 8 2}{it:maximize_options} control the maximization process; see
help {help maximize}. If you are seeing many "(not concave)" messages in the
log, using the {cmd:difficult} option may help convergence.
{title:Remarks}
{p 4 4 2}The inverse Gaussian appears in various guises in other
Stata model fit commands, but none is identical to that here. {help glm}
with identity link and inverse Gaussian family is similar except that
the parameterisation is different; the scale parameter is there treated as
ancillary, and thus the definition of likelihood is quite different; and
{cmd:glm} does not allow the scale parameter to depend on covariates.
Various programs by Joseph Hilbe (Hilbe 2000 and later work on SSC
accessible using {help findit} and {help ssc}) wire in log link
functions. Finally, the use of inverse Gaussian as one way of modelling
frailty in {help streg} differs yet again. Note, however, that
the program {help geninvgauss} by Roberto Gutierrez in his {cmd:gendist}
package (accessible using {cmd:findit}) that produces random deviates from an inverse Gaussian uses
the same parameterisation, and the same names mu and lambda, as that
here.
{title:Saved results}
{p 4 4 2}In addition to the usual results saved after {cmd:ml}, {cmd:invgaussfit}
also saves the following, if no covariates have been specified:
{p 4 4 2}{cmd:e(mu)} and {cmd:e(lambda)} are the estimated inverse
Gaussian parameters.
{p 4 4 2}The following results are saved regardless of whether covariates have
been specified:
{p 4 4 2}{cmd:e(b_mu)} and {cmd:e(b_lambda)} are row vectors containing the
parameter estimates from each equation.
{p 4 4 2}{cmd:e(length_b_mu)} and {cmd:e(length_b_lambda)} contain the lengths
of these vectors. If no covariates are specified in an equation, the
corresponding vector has length equal to 1 (the constant term); otherwise, the
length is one plus the number of covariates.
{title:Examples}
{p 4 8 2}{cmd:. invgaussfit mpg}
{title:Authors}
{p 4 4 2}Nicholas J. Cox, Durham University{break}n.j.cox@durham.ac.uk
{p 4 4 2}Stephen P. Jenkins, University of Essex{break}stephenj@essex.ac.uk
{title:References}
{p 4 8 2}
Evans, M., Hastings, N. and Peacock, B. 2000. {it:Statistical distributions.}
New York: John Wiley.
{p 4 8 2}
Hilbe, J. 2000.
Two-parameter log-gamma and log-inverse Gaussian models.
{it:Stata Technical Bulletin} 53: 31{c -}32
({it:STB Reprints} 9: 273{c -}275).
{p 4 8 2}
Johnson, N.L., Kotz, S. and Balakrishnan, N. 1994.
{it:Continuous univariate distributions: Volume 1.} New York: John Wiley.
{title:Also see}
{p 4 13 2}
Online: help for
{help pinvgauss} (if installed),
{help qinvgauss} (if installed)
{help invgausscf} (if installed)