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I need to add a horizontal reference line at 90% to represent my target in the p-chart. When I put href=0.9 in my code I got the following warning "WARNING: Numeric href= values are incompatible with a character subgroup variable; HREF= lines are not displayed." The warning indicates that the href value is not compatible because the subgroup variable, quarter, is treated as a character data type. How to include the target line? Here is my code: proc shewhart data=tmp2; pchart yes_answer1*quarter/ markers subgroupn = total_count1 nohlabel Href=0.9; run;
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Hi guys,
suppose to have the following:
data DB1;
input ID Discharge;
format Discharge date9.;
cards;
0001 19JUN2017
0001 07SEP2020
0002 17MAR2016
0003 05MAY2016
0003 08FEB2017
0004 22MAR2017
0004 03MAY2017
0004 28MAR2021
;
data DB2;
input ID Discharge Flag NewDate;
format Discharge NewDate date9.;
cards;
0001 19JUN2017 0 .
0001 07SEP2020 1 08SEP2020
0002 17MAR2016 1 18MAR2016
0003 05MAY2016 0 .
0003 08FEB2017 1 09FEB2017
0004 22MAR2017 0 .
0004 03MAY2017 0 .
0004 28MAR2021 1 29MAR2021
;
Is there a way to add a flag to DB1 where for each ID there's the latest date (if there is only one date as for ID 0002 the last date will be the one reported) and then add the new date corresponding to the last + 1 day? Desired output: DB2.
Thank you in advance
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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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