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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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