I am running it in your sample data and it give me the desired output. Without giving C across all observation. Even your data sample in the post is not starting with C and there is no way the code will give C for all the observation except for case when your data starting with C and not having V in any observation! the code is simple and you can trace it easily? If you could share your entire code or upload the file you are reading from the data. in order to help you further.
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Solution ---- (Repost): data want (drop=newV ); retain newV ' '; set have; if _n_=1 and STATUS = ' ' then Do ; STATUS = 'V'; newV=STATUS; End; Else if STATUS in ('C','V') then newV=STATUS; Else if STATUS=' ' then STATUS=newV; run; Could you run this code on the data you have ? And let us know how it is difference from what you want. keeping in mind that the 'O' values for the STATUS is typo as you clarified in your previous post.
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i think his last post contain typo it should be TABLE C: id source _a _b number 1 a re 123 . 2 b ba 53 14 3 a ag 679 . 3 b 45 78 2 as based on the original post.
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You should be aware of : That different problems may arises when MERGE, IF-THEN statement used within one data step. Kindly check: Be Careful When You Merge SAS Datasets! So use two data step instead of one, as one work around for your case. mohamed zaki
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the previous one to see the change but the final code should be like the following" data newC (drop=newV ); retain newV ' '; set new; if _n_=1 and STATUS = ' ' then Do ; STATUS = 'V'; newV=STATUS; End; Else if STATUS in ('C','V') then newV=STATUS; Else if STATUS=' ' then STATUS=newV; run;
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Check this *** Not tested Data new ; infile datalines dlm=','; input STATUS $; datalines; C, C, , V, V, , , V, , C, , , ; run; data newC; retain newV ' '; set new; if _n_=1 and STATUS = ' ' then Do ; *STATUS = 'V'; newV='V'; End; Else if STATUS in ('C','V') then newV=STATUS; *Else if STATUS=' ' then STATUS=newV; run;
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When i checked his paper i did not find any reference to the code " A Rake-Trim SAS® Macro and Its Uses at Westat ", you may want to ask him. You can find his mail at the end of the paper. Although i think it is in the property of Westat
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What is your version of EM ? I have found other people to refer to the lack of the cross validation in EM with neural network, so they repeat the test several time with different data partition seeds and taking the average. Check this: http://www.kms.pwr.wroc.pl/staff/trawinski/wp-content/uploads/2013/09/2009-Comparative-Analysis-of-Neural-Network-Models1.pdf Hope other to tell us if that possible.
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Although i think your code is typo but this mean that you specify that your data is nested check this for more clarification http://www.thejuliagroup.com/documents/procmixed1.pdf
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use nodupkey with sort while you keep only data that matter that could help you. data test ; input specie $ ID Gender $ Biopsy_letter $ Step; cards; G 122 M A 1 G 122 M A 2 G 122 M A 3 G 122 M A 4 G 132 F A 1 G 132 F A 2 G 132 F A 3 G 132 F A 4 S 546 F A 1 S 546 F A 2 S 546 F A 3 S 546 F A 4 S 546 F B 1 S 546 F B 2 S 546 F B 3 S 546 F B 4 ; run; proc sort data=test(keep=specie ID Gender) out=new nodupkey ; by specie ID Gender ; run ; proc freq ; tables specie*gender ; run ;
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If your week data set is only incremented integer you can use this also without SQL: data test1; input x y$; datalines; 23 A 23 B 24 A 24 B ; data all; set test1; do week = 1 to 5 by 1; output; end; run; proc sort data=all; by week; run;
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