Hi,
I need do a plot for my model in proc Mixed.
It is my final model:
proc mixed data=DatosExpIny2010 covtest;
class nest sex;
where F_Chick=1;
model Time= treatment date/ddfm=satterth;
random nest;
*lsmeans sex;
*estimate 'volumen huevo' huevovolok 1;
estimate 'orden_p' opuesta 1;
estimate 'trt' trt 1;
*estimate 'Fechatot' Fechatot 1;
*estimate 'Fase Pollo' F_Pollo 1;
run;
And I need a plot with 95% error.
Could you tell me the correct script?
Thank you very much.
All the best
And I need a plot with 95% error.
What statistic do you need a plot of with 95% confidence limits?
Yes, I need to make a graph that represents the model. It should be a plot for Treatment and Time, becaue they are continuous variables. Also I need it with confidence limits. Thanks!
As far as I can see, there is no built in plotting with PROC MIXED that makes a plot of the model.
You might want to do this through PROC GLIMMIX followed by PROC PLM with the EFFECTPLOT statement.
Update: Looks like PROC MIXED followed by PROC PLM with the EFFECTPLOT statement might work as well, but I haven't tried it.
It sounds great! but... how i can do it? Thanks!
SpanishPhD wrote:
It sounds great! but... how i can do it? Thanks!
Here's an example using PROC ORTHOREG, maybe this can get you started
but this option does not allow me to work with random, do it?
As I said, it was an example.
I think you will need to use PROC MIXED or PROC GLIMMIX followed by PROC PLM, if you want to include a RANDOM statement in the model.
If you want 95% CL for the mean predicted values, I think you can get that in an output data set. On the MODEL statement, use the OUTP= option to get an output data set. The doc says
"The L95 and U95 variables provide a t-type confidence interval for the predicted values, and they correspond to the L95M and U95M variables from the GLM and REG procedures for fixed-effects models. The residuals are the observed minus the predicted values."
You can then use PROC SGPLOT to plot the predicted and 95% CLM.
If you want to see examples of usnig PROC PLM, see the paper by Tobias and Cai (2010) : http://support.sas.com/resources/papers/proceedings10/258-2010.pdf
Thakns a lot!!! I will try it!!
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