*! Version 2.0.0 * INVERSE GAUSSIAN REGRESSION - 2 PARAMETER WITH LOG PARAMETERIZATION: Joseph Hilbe : 8Oct2005 program ivgauss2, eclass properties(svyb svyj svyr) version 9.1 syntax [varlist] [if] [in] [fweight pweight aweight iweight] [, /// CENsor(string) Level(cilevel) /// OFFset(passthru) EXposure(passthru) /// CLuster(passthru) EForm Robust noLOG FROM(string asis) * ] gettoken lhs rhs : varlist mlopts mlopts, `options' if ("`weight'" != "") local weight "[`weight' `exp']" if (`"`from'"' != `""') local initopt `"init(`from')"' ml model lf ivgln_ll (xb: `lhs' = `rhs', `offset' `exposure') /// /ln_phi /// `if' `in' `weight', /// `mlopts' `robust' `cluster' /// title("2-parameter Log-Inverse Gaussian Regression") /// maximize `log' `initopt' /// diparm(ln_phi, exp label(phi)) ereturn scalar k_aux = 1 ml display, level(`level') `eform' qui { * AIC tempvar aic local nobs e(N) local npred e(df_m) local df = e(N) - e(df_m) -1 local llike e(ll) gen `aic' = ((-2*`llike') + 2*(`npred'+1))/`nobs' } * DISPLAY di in gr _col(1) "AIC Statistic = " in ye %11.3f `aic' end