I did a growth performance study in grower pigs using 4 different diets ( peas and enzymes were the two factors)and the data turned out to be non-normal. The experiment was arranged as an RCBD on factorial design.
What distribution should I use...gamma or poisson?
How would I identify the fit statistic? Can I get AIC, BIC values using glimmix or should I use proc mixed?
How can I get the estimate values if I am using any of these distributions? Or should I take the estimate values from proc mixed?
This is the code I have used:
ods html;
ods graphics on;
proc glimmix data=Body_weight plot =(residualpanel studentpanel);
class block pen peas enzymes Day;
model weight = peas|enzymes|Day/dist=gamma link=log s;
random Day/subject=Pen(peas enzymes) type=cs residual;
lsmeans peas|enzymes|Day/slice=Day;
run;
ods graphics off;
ods html close;
I have attached the entire code with my data for further information.
Please guide me.
Thanks.
When fitting
proc glimmix data=Body_weight plot =(residualpanel studentpanel);
class block pen peas enzymes Day;
model weight = peas|enzymes|Day/ /*dist=gamma link=log s*/;
random Day/subject=Pen(peas enzymes) type=un residual;
lsmeans peas|enzymes|Day/slice=Day;
run;
the studentized residuals look like
I don't know about the model. But the residuals look fine to me.
When fitting
proc glimmix data=Body_weight plot =(residualpanel studentpanel);
class block pen peas enzymes Day;
model weight = peas|enzymes|Day/ /*dist=gamma link=log s*/;
random Day/subject=Pen(peas enzymes) type=un residual;
lsmeans peas|enzymes|Day/slice=Day;
run;
the studentized residuals look like
I don't know about the model. But the residuals look fine to me.
Thank you so much.
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