Date:         Wed, 11 Mar 2009 18:12:39 -0700
Reply-To:     Bminer <b_miner@LIVE.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Bminer <b_miner@LIVE.COM>
Organization: http://groups.google.com
Subject:      Re: NLMixed Convergence
Comments: To: sas-l@uga.edu
Content-Type: text/plain; charset=ISO-8859-1

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!