About hellohere

hellohere · ‎03-06-2022

Thanks, Pal. The original dataset has 19397784 row. The row counts from both treatments show up identical(19397784, right click). BUT run subset, timesec=93100000, one has zero and the other shows up thousands(as expected). Double click the dataset, timesec=93100000 does not show up in dataset-merge treatment(remember the row count is 19397784). That is the myth.

Reeza · ‎03-03-2022

Read the documentation. What does it say under DATA for PROC DELETE? https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/proc/n1469bsvzgd33jn0z4xecxmx2bgo.htm

FreelanceReinh · ‎03-02-2022

Hi @hellohere, One way is to use the CODE statement in the original PROC REG step and to use the stored code later in a DATA step: proc reg data=sashelp.class; model height=age; code file='C:\Temp\regscore.sas' residual; quit; data want; set newclass; %include 'C:\Temp\regscore.sas'; run; Dataset WANT will contain variables P_Height and R_Height with the predicted values and residuals, respectively. See also two of Rick Wicklin's blog posts about scoring of regression models: The missing value trick for scoring a regression model Techniques for scoring a regression model in SAS

AllanBowe · ‎03-01-2022

You can unzip without the use of X command using this macro: https://core.sasjs.io/mp__unzip_8sas.html

hellohere · ‎03-01-2022

Yeah， you guys are right. it is . not :

Kurt_Bremser · ‎02-28-2022

@hellohere wrote: my SASHELP.VTABLE only shows up info on dataset in sashelp, not in work ... SASHELP.VTABLE has metadata for ALL datasets in currently assigned libraries, which includes WORK: data class; set sashelp.class; run; data _null_; set sashelp.vtable; where libname ="WORK" and memname = "CLASS"; put nobs=; run; Log: 69 data class; 70 set sashelp.class; 71 run; NOTE: There were 19 observations read from the data set SASHELP.CLASS. NOTE: The data set WORK.CLASS has 19 observations and 5 variables. NOTE: Verwendet wurde: DATA statement - (Gesamtverarbeitungszeit): real time 0.00 seconds user cpu time 0.00 seconds system cpu time 0.00 seconds memory 827.78k OS Memory 24228.00k Timestamp 28.02.2022 01:56:05 nachm. Step Count 24 Switch Count 2 Page Faults 0 Page Reclaims 183 Page Swaps 0 Voluntary Context Switches 11 Involuntary Context Switches 0 Block Input Operations 0 Block Output Operations 264 72 73 data _null_; 74 set sashelp.vtable; 75 where libname ="WORK" and memname = "CLASS"; 76 put nobs=; 77 run; nobs=19 NOTE: There were 1 observations read from the data set SASHELP.VTABLE. WHERE (libname='WORK') and (memname='CLASS');

Kurt_Bremser · ‎02-20-2022

Which means that either the path is not available in your SAS session, or is empty. Expand the code to see which of that is true: data names; length fref $8 name $200; rc = filename(fref,"&path."); did = dopen(fref); if did then do; do i = 1 to dnum(did); name = dread(did,i); output; end; rc = dclose(did); end; else put "Directory not found!"; rc = filename(fref); keep name; run;

sbxkoenk · ‎02-14-2022

Hello, I have moved your post to this board --> [ Mathematical Optimization, Discrete-Event Simulation, and OR Operations Research topics: SAS/OR, SAS Optimization, and SAS Simulation Studio ] as it is more about optimization than about Forecasting and Econometrics (the board where you originally posted). Start here : SAS/OR User's Guide: Mathematical Programming The Quadratic Programming Solver Example 11.2 Portfolio Optimization https://go.documentation.sas.com/doc/en/orcdc/14.3/ormpug/ormpug_qpsolver_examples02.htm?homeOnFail Cheers, Koen

hellohere · ‎02-13-2022

dataset will like 40 columns(for 20 for winners and 20 for losers), and 100 rows(daily returns). Winner Daily Return= 0.25*ranuni()+0.05*ranuni() [Winner has daily 0.25% expected return, with 0.05% additional variance in expectation] Winner Daily Return= -0.25*ranuni()+0.05*ranuni() [Lower has daily -0.25% expected return, with 0.05% additional variance in expectation] Goal is to find the Optimal Pair(Winner-Loser) to Maximize the Portfolio Gap Return with consideration with daily variance(Ret/Stdev) Any help?! Thanks

SASKiwi · ‎02-13-2022

The SAS Windows Companion explains all this. Here is the link to batch processing.

Kurt_Bremser · ‎02-08-2022

Let us see code examples for the steps that take so much time, there is (almost) always opportunity for performance tuning. Which unzip program do you use in the X statement?

hellohere · ‎02-07-2022

Thanks a lot!

hellohere · ‎01-27-2022

Thanks a lot still. To CSum by var in a dataset, here are many ways to do so. The easiest one is the one with IML, which I do not use. I bet your way surely works also. I am sticky with converting string-token line into rows in a dataset, because that is a way I know how to systematically generate code line for variables. The variable names are fluid, not just var1/var2/var3/... and not just CSum

s_lassen · ‎01-25-2022

The first solution suggested by @Reeza can be made a bit simpler (and slightly faster) like this: data want_option1; do until(last.orgID); set have; by orgID; length combined $100.; call catx(', ', combined, states); end; run; And it has to be done in a data step, because SAS SQL does not have concatenation as a "summary" function, unlike most other SQL dialects. If performance matters, you should always use CALL CATX rather than the function.

Kurt_Bremser · ‎01-19-2022

A "Sack Overflow" is something I would expect to happen in the NFL, especially now that it's playoff time 😉 SCNR

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