Hi,
I should create a map with smoothing mortality rate for italian areas with the neighbor Matrix (for each area (row) the Matrix considers more areas (colomns)) .
I haven't PROC IML and I think I could't use PROC GLIMMIX.
http://www.si-folkesundhed.dk/scient.pub/sr/Icar.htm
I'm trying to study PROC MCMC form an example by SAS that considers the neighbor Matrix, but It doesn't work .
Could you help me?
Thanks,
Alessandra Rossi
Doesn't work is awful vague.
Are there errors in the log?: Post the code and log in a code box opened with the {i} to maintain formatting of error messages.
No output? Post any log in a code box.
Unexpected output? Provide input data in the form of a dataset, the actual results and the expected results. Data should be in the form of a data step. Instructions here: https://communities.sas.com/t5/SAS-Communities-Library/How-to-create-a-data-step-version-of-your-dat... will show how to turn an existing SAS data set into data step code that can be pasted into a forum code box using the {i} icon or attached as text to show exactly what you have and that we can test code against.
Try using radial smoothing functions (TYPT=RSMOOTH) as in this GLIMMIX example:
Hi,
I solved using SAS Studio that has neighbour option in PROC MCMC.
I'd need to weight the single area (cod_pro) and the neighbors (cod_pro_adj) with their Observedevents .
How could I do?
follow my sintax without the weight
ods select none;
proc mcmc data=step1 seed=615926 nbi=10000 nmc=50000 thin =10
plots=none outpost=/*OUT.*/step1post;
parms tau_b 0.5 tau_h 0.2 provar 0.5;
parms alpha 0;
prior tau: ~ gamma(0.5, is=0.0005);
prior alpha ~ general(0);
prior provar ~ gamma(0.001, is=0.001);
random h ~ n(0, prec=tau_h/**poptime*/) s=cod_pro;
random b ~ normalcar(neighbors=cod_pro_ad, num=num, prec=tau_b*num/*/poptime ObservedEvents*/) s=cod_pro;
mu=alpha + b + h;
model stdrate ~ normal(mu, sd=provar);
run;
I've attached the db with few rows.
Many thanks
Alessandra
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