About Unay13

Unay13 · ‎11-02-2018

Thank you.

Unay13 · ‎11-01-2018

Hi Rick, So these were the notes in the Log file NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: At least one element of the 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. I came across an article which says that when above warning appears the results are not reliable. What are alternatives or other approaches to run the model without such errors? Thank you for your response!

Unay13 · ‎10-29-2018

I have been trying a few different models with different functions but NB distribution. However, in one of the functions that I use, in my results, I get covariance matrix and correlation matrix of parameter estimates even though I did not use CORR or COV function. What could possibly be the reason for me to obtain such output in one function but not in others? Attached is the output. Is there any statistical problem in the function? Any form of help would be highly appreciated.

Unay13 · ‎06-20-2018

Thank you for the help. I did try guessing the initial parameters based on other similar models. The model does converge but I get null values or blank spaces in parameter estimates standard error, t Value, confidence limit and P value. What could possibly be the reason for that? These are the notes and warning in Log: NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: At least one element of the gradient is greater than 1e-3. NOTE: Moore-Penrose inverse is used in covariance matrix. WARNING: The final Hessian matrix is full rank but has at least one negative eigenvalue. Second-order optimality condition violated.

Unay13 · ‎06-18-2018

Did you come up with a solution yet for CURE plot in Proc NLMIXED? Please share if you did.

Unay13 · ‎06-16-2018

Thank you for the response and I apologize for not being clear. I have defined the PARMS statement below with the default values. proc NLMIXED data = work.spf; PARMS beta_0=1 beta_1=1 beta_2=1 beta_3=1 beta_4=1 beta_5=1 k=1; mu=beta_0 +(beta_1/((1+ beta_2*(maj)**(-beta_3))*(1+ beta_4*(min)**(-beta_5)))); NBLL = (lgamma(tot+(1/k)) - lgamma(tot+1) - lgamma(1/k)+ tot*log(k*mu) - (tot+(1/k))*log(1+k*mu)); model tot ~ general (NBLL); run; The Betas and K are the parameters. There are 198 observation. tot is the dependent variable and maj and min are the independent variables. Any help regarding the initial parameters would be highly appreciated.

Unay13 · ‎06-16-2018

This is the code: proc NLMIXED data = work.spf; mu=beta_0 +(beta_1/((1+ beta_2*(maj)**(-beta_3))*(1+ beta_4*(min)**(-beta_5)))); NBLL = (lgamma(tot+(1/k)) - lgamma(tot+1) - lgamma(1/k)+ tot*log(k*mu) - (tot+(1/k))*log(1+k*mu)); model tot ~ general (NBLL); run; When I assigned mu as a different function with three parameters to be estimated the model converged. The first three solutions you provided are not applicable as I have less knowledge regarding what the parameters should be and there are 7 parameters. Also, I am not much familiar with the method of moments or grid based methods.

Unay13 · ‎05-04-2018

I was using the Proc NLMIXED for estimating my model using poisson and negative binomial model. The results change drastically when using the parms statement and assigning initial values. On what basis do we define the initial parameters in the Parms statement? We run the model for the estimation of those coefficient so how do we initially guess the values? Any help would be greatly appreciated.

Unay13 · ‎04-02-2018

I am using NLMIXED for Negative Binomial modeling with user defined functions. One of the models worked, but when I replaced a function from : mu= 5*365*((MINAADT)**beta_1)* ((MAXAADT)**beta_2)*(EXP(beta_0)) with this: 5*365*(beta_0 + beta_1/((1+beta_2 *(MINAADT)**beta_3)*(1+beta_4*(MAXAADT)**beta_5))); Following error is shown in the Log. ERROR: QUANEW Optimization cannot be completed. NOTE: QUANEW needs more than 200 iterations or 2000 function calls. NOTE: PROCEDURE NLMIXED used (Total process time): real time 0.19 seconds cpu time 0.12 seconds

Unay13 · ‎04-02-2018

Does the Negbin in Proc NLMIX refer to Negative binomial -1 or Negative Binomial 2?

Unay13 · ‎04-02-2018

I am sorry but I do not have a statistics background and I am new to SAS. For me the NB modeling I want to do of the above function is for the dependent variable TOT that I have. So is the model statement correct: model TOT ~ negbin(1/k,p); Is K the overdispersion parameter? The default Negbin function, is it for NB-1 or NB-2? NB-2 I suppose? What can I do to model my TOT (dependent variable) and MINAADT and MAXAADT as independent variables with the equation: Y= 5*365*((MINAADT)**beta_1)* ((MAXAADT)**beta_2)*(EXP(beta_0));

Unay13 · ‎04-02-2018

In the above case, TOT is my dependent variable so I was assuming I would have to specify that.

Unay13 · ‎03-30-2018

for the following codes where I have not specified TOT as a data set as it is one of the variables, can you let me know why do I get the warning below: 892 proc NLMIXED data =SPFU3ST; 893 parms k=0.8; 894 Y= 5*365*((MINAADT)**beta_1)* ((MAXAADT)**beta_2)*(EXP(beta_0)); 895 model TOT ~ NEGBIN (1/k, Y); 896 predict TOT out = TOT; 897 run; NOTE: The parameters beta_1, beta_2, beta_0 are assigned the default starting value of 1.0, because they are not assigned initial values with the PARMS statement. ERROR: No valid parameter points were found. NOTE: The SAS System stopped processing this step because of errors. WARNING: The data set WORK.TOT may be incomplete. When this step was stopped there were 0 observations and 0 variables. WARNING: Data set WORK.TOT was not replaced because this step was stopped.

Unay13 · ‎03-30-2018

Your response was really helpful. However, I got the following codes : proc nlmixed; parms b0=3, b1=1, k=0.8; linp = b0 + b1*x; mu = exp(linp); p = 1/(1+mu*k); model y ~ negbin(1/k,p); run; How necessary is it to use the PARMS statement? I do not know what values or on what basis do I assign the values for parameters to be accepted? Also in the model statement, what does (1/k, p) stand for? any help would be greatly appreciated.

Unay13 · ‎03-30-2018

Thanks Ksharp. But I need to feed in the Hoerl and Sigmoidal function and do the NB modeling. By any chance do you have the codes for NB modeling with our own specified functions?

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Re: Proc NLMIXED Covariance and Correlation

Re: Proc NLMIXED Covariance and Correlation

Proc NLMIXED Covariance and Correlation

Re: Parms statement in nlmixed

Re: Cumulative Residual Plots (CURE plots)

Re: Parms statement in nlmixed

Re: Parms statement in nlmixed

Parms statement in nlmixed

Error: QUANEW needs more than 200 iterations

NegBin in NLMIX

Re: Proc NLMIXED Covariance and Correlation

Re: Proc NLMIXED Covariance and Correlation

Re: Parms statement in nlmixed

Re: Parms statement in nlmixed

Re: Fitting Nlmixed model for negative binomial model.

Re: Proc NLMIXED Covariance and Correlation

Re: Proc NLMIXED Covariance and Correlation

Proc NLMIXED Covariance and Correlation

Re: Parms statement in nlmixed

Re: Cumulative Residual Plots (CURE plots)

Re: Parms statement in nlmixed

Re: Parms statement in nlmixed

Parms statement in nlmixed

Error: QUANEW needs more than 200 iterations

NegBin in NLMIX

Re: Fitting Nlmixed model for negative binomial model.

Re: Fitting Nlmixed model for negative binomial model.

Re: Fitting Nlmixed model for negative binomial model.

Re: Fitting Nlmixed model for negative binomial model.

Re: Fitting Nlmixed model for negative binomial model.