Hello SAS Community, I'm currently working on a project where I'm investigating the modifying effects of city parameters on the relationship between heat and the number of EMS services across 25 cities. Here's a breakdown of my approach and where I need some guidance: Initial Stage: I've estimated the heat effects adjusted for the year and weekday using a negative binomial model for each city separately. This step provided city-specific estimates of the heat effects. Second Stage: Now, I want to use meta-regression to explain the variance of the heat effects across the cities. My challenge lies in incorporating two different types of covariates: daily parameters and yearly parameters. Here are my specific questions: Is there a way to assess the daily covariates in the meta-regression without aggregating them into a single mean? How can I properly incorporate both daily and yearly parameters into the meta-regression model while accounting for the city-specific estimates obtained in the initial stage? How should the data structure look like to utilize the PROC MIXED function for this purpose? Any advice or suggestions on how to approach this would be greatly appreciated. Thank you!
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USING THE CODE ALREADY POSTED IN SUNFLOWER EXAMPLE. I HAVE A PROBLEM THE ESTIMATE STATEMENT OR LINE IS NOT WORKING AS IT GIVES AN ERROR "ERROR 180-322: Statement is not valid or it is used out of proper order." WHAT COULD BE THE PROBLEM? I HAVE TRIED SHIFTING THE ESTIMATE LINE TO BE BEFORE THE OUTPUT STATEMENT, STILL IT FAILS. /* Mean heights of 58 sunflowers: Reed, H. S. and Holland, R. H. (1919), "Growth of sunflower seeds" Proceedings of the National Academy of Sciences, volume 5, p. 140. http://www.pnas.org/content/pnas/5/4/135.full.pdf */ data Sunflower; input Time Height; label Time = "Time (days)" Height="Sunflower Height (cm)"; datalines; 7 17.93 14 36.36 21 67.76 28 98.1 35 131 42 169.5 49 205.5 56 228.3 63 247.1 70 250.5 77 253.8 84 254.5 ; proc nlin data=Sunflower list noitprint; parms K 250 r 1 b 40; /* initial guess */ model Height = K / (1 + exp(-r*(Time - b))); /* model to fit; Height and Time are variables in data */ output out=ModelOut predicted=Pred lclm=Lower95 uclm=Upper95; estimate 'Dt' log(81) / r; /* optional: estimate function of parameters */ run;
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Hi all,
At https://developer.sas.com/apis/rest/v3.5/#filters there is supposed to be a link that explains filter expressions:
For a complete description of filter expressions, see the Filtering reference.
However, when clicking this link (on the word "Filtering") I do not see any such page. It looks like this "got lost" during the switch to the new developers.sas.com web site.
Anyone knows where can I find the doc about API filter expressions?
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Hi, I have a SAS dataset wherein date format is incorrect and the same is to be corrected.
Please help in the matter.
data A; input Employee_Id $ Date Date9. ; cards;
70202028 - 04JAN2023;
70204018 - 04SEP2023;
70172038 - 27MAR2023;
run;
data want;
set a;
70202028 - 01APR2023; /*to be changed*/
70204018 - 09APR2023; /*to be changed*/
70172038 - 27MAR2023; /*this is correct and it should not be changed*/
run;
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