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- Re: intial values for parms in proc nlmixed

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Posted 06-24-2021 03:54 AM
(659 views)

hello everybody can you help me in the problem in how we can chose the intial values for parms in the proc nlmixed? is there specified method can I chose from it the intial values for the parameters

please help me, my distribution and the program is:

proc nlmixed data=WORK.y;

parms b0=1 b1=2 b2=2=1 a=1 b=1 s=2;

m =b0+(b1*age)+(b2*creatin)+(b3*gender2);

y=urea;

if status_u=1 then f=((a*b)*((1-exp(-exp((y-m)/s)))**(a-1))*((1+((exp(exp((y-m)/s))-1)**(a)))**(-(b+1)))) /(s*exp((-a*exp((y-m)/s))-((y-m)/s))) ;

else f= (1+(((1-exp(-exp((y-m)/s)))/(exp(-exp((y-m)/s))))**a))**-b ;

ll=log(f);

model y ~ general(ll);

run;

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It looks like you have a typo on the PARMS statement?

parms b0=1 b1=2 **b2=2=1** a=1 b=1 s=2;

I know of three ways to choose initial parameters:

- The brute force method: Use a grid search to find initial parameter values
- Fit a reduced model. Sometimes your model is a generalization of a simpler model that has fewer parameters. You can estimate the simpler model and use those estimates as initial guesses for the full model.
- Use the method of moments for parameters that are connected to means, standard deviations, etc.

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It looks like you have a typo on the PARMS statement?

parms b0=1 b1=2 **b2=2=1** a=1 b=1 s=2;

I know of three ways to choose initial parameters:

- The brute force method: Use a grid search to find initial parameter values
- Fit a reduced model. Sometimes your model is a generalization of a simpler model that has fewer parameters. You can estimate the simpler model and use those estimates as initial guesses for the full model.
- Use the method of moments for parameters that are connected to means, standard deviations, etc.

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Thank u very very much for your help, really it is a very important methods to chose initial values.

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