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
Registration is open! SAS is returning to Vegas for an AI and analytics experience like no other! Whether you're an executive, manager, end user or SAS partner, SAS Innovate is designed for everyone on your team. Register for just $495 by 12/31/2023.
If you are interested in speaking, there is still time to submit a session idea. More details are posted on the website.
Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.
Find more tutorials on the SAS Users YouTube channel.