On Mar 11, 12:50 pm, stringplaye...@yahoo.com (Dale McLerran) wrote: > Because the gamma distribution requires a response variable with > positive values, the zero-inflated gamma distribution can be > fit as a two-stage model: > > 1) extract the observations with a positive value for the response > and model those observations with the gamma distribution using > the procedure GENMOD > > 2) construct a modified response variable which is zero when the > original response (NEXTREVENUE) is zero and one when the > original response is positive. Model the probability of the > modified response being zero. Again, you can use the GENMOD > procedure to fit this model. > > The models in both 1) and 2) will be exactly correct because the > gamma distribution does not support zero values. Therefore, > there is no mixture of gamma distribution zeros with the zero- > inflation process. In this, it may be incorrect to make statements > about a zero-inflated gamma distribution. Rather, it is probably > better to talk just of a mixture of a gamma response with a zero > response. > > If you want to report the "zero-inflated gamma" model likelihood, > you will need to use NLMIXED. You can initialize NLMIXED from the > parameter estimates returned by your two GENMOD stages. Those > parameters should maximize the likelihood - at least if you are > using the same gamma and logistic regression model parameterizations. > > Dale > > --------------------------------------- > Dale McLerran > Fred Hutchinson Cancer Research Center > mailto: dmclerra@NO_SPAMfhcrc.org > Ph: (206) 667-2926 > Fax: (206) 667-5977 > --------------------------------------- > > --- On Mon, 3/9/09, Bminer <b_mi...@LIVE.COM> wrote: > > > > > From: Bminer <b_mi...@LIVE.COM> > > Subject: Re: NLMixed Convergence > > To: SA...@LISTSERV.UGA.EDU > > Date: Monday, March 9, 2009, 8:25 AM > > On Mar 9, 10:22 am, Bminer <b_mi...@live.com> wrote: > > > On Mar 9, 9:55 am, Bminer <b_mi...@live.com> > > wrote: > > > > > Hi All- > > > > > I'm using nlmixed to run a simple zero > > inflated gamma with one > > > > continuous variable and one dummy coded variable. > > > > > proc nlmixed data=China; > > > > parms b0_f=0 b1_f=0 b2_f=0 > > > > b0_h=0 b1_h=0 b2_h=0 > > > > log_theta=0; > > > > > eta_f = b0_f + b0_f*hassku + > > b2_f*REVORDONE ; > > > > p_yEQ0 = 1 / (1 + exp(-eta_f)); > > > > > eta_h = b0_h + b0_h*hassku + b2_h*REVORDONE > > ; > > > > mu = exp(eta_h); > > > > theta = exp(log_theta); > > > > r = mu/theta; > > > > > if NEXTREVENUE=0 then > > > > ll = log(p_yEQ0); > > > > else > > > > ll = log(1 - p_yEQ0) > > > > - lgamma(theta) + > > (theta-1)*log(NEXTREVENUE) - theta*log > > > > (r) - NEXTREVENUE/r; > > > > > model NEXTREVENUE ~ general(ll); > > > > predict (1 - p_yEQ0)*mu out=expect_zig; > > > > estimate "scale" theta; > > > > > run; > > > > > I get the following log warnings: > > > > > Does anyone have a suggestion on what to try / > > look for to get > > > > convergence? > > > > > NOTE: GCONV convergence criterion satisfied. > > > > NOTE: At least one element of the (projected) > > gradient is greater than > > > > 1e-3. > > > > NOTE: Moore-Penrose inverse is used in covariance > > matrix. > > > > WARNING: The final Hessian matrix is not positive > > definite, and > > > > therefore the > > > > estimated covariance matrix is not full > > rank and may be > > > > unreliable. > > > > The variance of some parameter estimates > > is zero or some > > > > parameters > > > > are linearly related to other > > parameters. > > > > Typo on the code. s/b: > > > > proc nlmixed data=China; > > > > > parms b0_f=0 b1_f=0 b2_f=0 > > > > b0_h=0 b1_h=0 b2_h=0 > > > > log_theta=0; > > > > > eta_f = b0_f + b1_f*hassku + > > b2_f*REVORDONE ; > > > > p_yEQ0 = 1 / (1 + exp(-eta_f)); > > > > > eta_h = b0_h + b1_h*hassku + b2_h*REVORDONE > > ; > > > > mu = exp(eta_h); > > > > theta = exp(log_theta); > > > > r = mu/theta; > > > > > if NEXTREVENUE=0 then > > > > ll = log(p_yEQ0); > > > > else > > > > ll = log(1 - p_yEQ0) > > > > - lgamma(theta) + > > (theta-1)*log(NEXTREVENUE) - theta*log > > > > (r) - NEXTREVENUE/r; > > > > > model NEXTREVENUE ~ general(ll); > > > > predict (1 - p_yEQ0)*mu out=expect_zig; > > > > estimate "scale" theta; > > > > > run; > > > I changed the tech= option to NEWRAP and got > > convergence......should I > > be happy to have found the right algorithm or skpetical of > > the results > > if other tech options failed?- Hide quoted text - > > - Show quoted text -

Thanks Dale and WenSui. I took the parameter estimates from the seperate stages and was able to achieve convergence with the default tech= option of NLMIXED!

Thanks!