Inverse Gaussian distribution-log link MLE model -------------------------------------------------
^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.
^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.)
^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.
. ^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