Feel free to direct my to an article or post if this has been discussed previously.   I'd like to create a table that replicates an observation based on a frequency count. While I'm able to use the frequency (or weight) command for many functions, it would be nice to have a full cross-tabulation of the data as well. A sample of my data would be:   Have: Group Event Study Count 1 1 1 3 1 0 1 2 0 1 1 4   Want:   Group Event Study 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 1 1 0 1 1   I used low frequencies here but my frequencies are actually in the hundreds or thousands. My inkling is that I should be using an array within a data step but I haven't figured it out yet.   I appreciate any answers! I'm keen to learn different potential approaches as well.  ... View more

Background: I am using GENMOD to run a GEE on survey weighted data. My code below includes the correlated aspect of my data which is hospital #. I am using a national dataset, and the total observations are ~54 million and ~50 variables (I cut out many already but I realize I should've cut out all but the ones absolutely needed) ods graphics off; ods exclude all; ods results off; sasfile work.finalscabies open; proc genmod data=finalscabies; class hosp_nis Nis_stratum race homeless female agecat pay1 zipinc_qrtl / descending; model scabies(event='1')= Race female AgeCat Pay1 homeless ZIPINC_QRTL / dist=bin link=logit maxiter=10; weight discwt; repeated subject=Hosp_Nis(nis_stratum) / TYPE=EXCH; estimate 'Black' race 0 0 0 0 1 -1 /exp; estimate 'Hispanic' race 0 0 0 1 0 -1 /exp; estimate 'API' race 0 0 1 0 0 -1 /exp; estimate 'NA' race 0 1 0 0 0 -1 /exp; lsmeans homeless / OR cl; lsmeans female / OR cl; lsmeans agecat / OR cl; lsmeans pay1 / OR diff=all cl; lsmeans ZIPINC_QRTL / OR diff=all cl; ods output Estimates=estimateESTscabies GEEEmpPEst=GEEest GEEFitCritera=GEEFit LSMeans=OR1 Diffs=ORDiffs; run; ods exclude none; sasfile work.finalscabies close; Dilemma: I ran the GEE without any of the estimate statements and without the SASFILE line to load it to memory. This took 1 hour to run and output. I added 4 estimate statements and ran the code and it took 33 hours. Unfortunately I still had tweaks to make. I added the lines lsmeans lines, changed the ods settings to hopefully increase performance and ran the code again. This took 48 hours, at which point my computer did an automatic update without me realizing and all was lost.  Finally, I added the SASFILE line to load this massive file (24 GB) to memory of which I have 40GB of RAM.    I ran the code for the 3rd time and it's currently running at 36 hours elapsed. Here's where the real question comes in. The log only shows: NOTE: Writing HTML5(EGHTML) Body file: EGHTML 27 28 ods graphics off; 29 ods exclude all 30 ods results off; In prior runs of the code, the log would show: "NOTE: Algorithm Converged" after approximately 15-20 minutes. What is my log reflecting? Surely it must be running the GENMOD after 36 hours?    Additional Information: Looking at my RAM usage I saw an initial increase to 28GB used early on in the run. It's now down to 18GB use.    Thank you for anyone who can provide advice, suggestions, or an answer!!  (Yes I will remove the extra variables if for some reason I have to run this again) ... View more

I've look at several guides across the internet and can't figure this out so I'm posting here.   I'm creating a basic scatter plot, however the ticks are discrete rather than continuous, and I can find no way to change this.   The first set of code produces the graph below:  proc sgplot data=accidents noautolegend; scatter x=year y=age_adjusted_rate /markerattrs=(color=red symbol=circlefilled); label age_adjusted_rate="Car Accident Death Rate"; run; As you can see, SAS is automatically using the discrete values from the data for both the X and Y axes rather than continuous integers. Also why would SAS put the Y axis in descending order?!?   I changed the code to the following: proc sgplot data=accidents noautolegend; scatter x=year y=age_adjusted_rate /markerattrs=(color=red symbol=circlefilled); label age_adjusted_rate="Car Accident Death Rate"; xaxis grid values=(1999 to 2016 by 5); yaxis grid values=(2 to 8 by 1); run; Unfortunately while this changed my axes to the values I want, SAS is still using discrete values. This code produces the graph below.  How do I fix this? I tried playing around in ODS Graphic Designer forever and couldn't find any options.  ... View more

I'll preface by saying I am a complete novice to SAS so may need more in depth explanations than other users.   I'm wanting to generate a cluster of data sets (say 50) comprised of 75 rolls of two dice. Then with these 50 data sets, I want to have SAS find the data set that least resembles a normal distribution. I want to view that seeds set of 75 rolls as a PROC Freq table. I have the code to randomize 75 dice rolls (I'll paste below in case you want to make changes) but have no clue where to start with coding for a cluster of data 50 data sets, and what function may exist to have SAS identify the data set that is furthest from a normal distribution.   Thanks for any help!     DATA DICE(KEEP=SUM) OUTCOMES(KEEP=OUTCOME); DO ROLL=1 TO 75; OUTCOME1=1+INT(6*RANUNI(123)); OUTCOME2=1+INT(6*RANUNI(123)); SUM=OUTCOME+OUTCOME2; OUTPUT DICE; OUTCOME=OUTCOME1; OUTPUT OUTCOMES; OUTCOME=OUTCOME2; OUTPUT OUTCOMES; END; RUN; PROC FREQ DATA=DICE; TABLE SUM; RUN; PROC FREQ DATA=OUTCOMES; TABLE OUTCOME; RUN; ... View more