.-
help for ^ivglog^
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Inverse Gaussian distribution-log link MLE model
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^ivglog^ depvar [indepvars] [weight] [^if^ exp] [^in^ range] [^,^ ^ir^r
^ef^orm
^r^obust ^cl^uster^(^varname^)^ ^sc^ore^(^newvarnames^)^
^l^evel^(^#^)^ maximize_options ]
^fweight^s, ^iweight^s, ^aweight^s, and ^pweight^s are allowed; see help
@weights@.
^ivglog^ shares the features of all estimation commands; see help @est@.
The syntax of @predict@ following ^ivglog^ is
^predict^ [type] newvarname [^if^ exp] [^in^ range] [^,^ statistic]
where statistic is
^n^ predicted values of depvar
^xb^ linear prediction
^stdp^ standard error of the linear prediction
These statistics are available both in and out of sample; type "^predict^ ...
^if e(sample)^ ..." if wanted only for the estimation sample.
Description
------------
^ivglog^ estimates a full-information maximum-likelihood version of the
inverse Gaussian family-log link generalized linear model. That is, the
coefficient (i.e., point) estimates produced by ^ivglog^ are similar to the
coefficient estimates produced by ^glm^ ...^, family(ig) link(log)^; see
help @glm@. The standard errors, however, will be slightly different since
the log link is not the canonical link for the inverse Gaussian family.
^ivglog^ estimates distributions with a typically high initial peak with
a long tail. It can be used to estimate otherwise log-gamma of negative
binomial models with extremely long right-hand tails; see help @gammalog@
and help @nbreg@.
The outcome variable assumed for ^ivglog^ is continuous and is strictly
greater than zero. (^ivglog^ does not allow depvar to take on the value zero
or any negative value.)
Options
--------
^irr^ and ^eform^ both do the same thing. They report estimated coefficients
transformed to incidence-rate ratios.
^robust^ specifies the Huber/White/sandwich estimator of variance is to be
used
in place of the traditional calculation; see ^[U] 23.11 Obtaining robust^
^variance estimates^. ^robust^ combined with ^cluster()^ allows
observations
which are not independent within cluster (although they be be independent
between clusters). If you specify ^pweight^s, ^robust^ is implied.
^cluster(^varname^)^ specifies that the observations are independent across
groups
(clusters) but not necessarily within groups. varname specifies to which
group each observation belongs; e.g., ^cluster(personid)^ in data with
repeated observations on individuals. See ^[U] 23.11 Obtaining robust^
^variance estimates^. ^cluster()^ can be used with @pweight@s to produce
esti-
mates for unstratified cluster-sampled data. Specifying ^cluster()^
implies
^robust^.
^score(^newvars^)^ creates newvar containing each observation's contribution
to the
score; see ^[U] 23.12 Obtaining scores^. If two new varnames are
specified,
then the score from the ancillary parameter equation is also saved.
^level(^#^)^ specifies the confidence level, in percent, for confidence
intervals;
see help @level@.
maximize_options control the maximization process; see help @maximize@. You
should never have to specify them.
Options for ^predict^
--------------------
^n^, calculates the predicted value of depvar, exp(x_j*b)
^xb^ calculates the linear prediction.
^stdp^ calculates the standard error of the linear prediction.
Examples
---------
. ^ivglog los age sex, eform^
. ^ivglog los age sex, cluster(hospital) nolog sc(scr1 scr2)^
. ^predict mu, n^
. ^predict linear, xb^
Also see
---------
Manual: ^[U] 23 Estimation and post-estimation commands^,
^[U] 29 Overview of model estimation in Stata^,
On-line: help for @est@, @postest@; @glm@, @nbreg@, @poisson@, @zinb@
See also: help for @gammalog@
Help
------
Joseph Hilbe
Arizona State University
email: hilbe@@asu.edu;
jhilbe@@aol.com