This is perfect. Thank you ... View more

Thank you for the direction that was quite helpful. I updated my dataset to accommodate the example code, see attached. I am still having a few issues based on my code. 1. On the x axis the numbers are negative, is there a way to make both sides of the axis positive? 2. The labels on the bars show at the extreme ends so that not all of the number is showing, is there a way to center the labels on the bar? 3. Is there a way to adjust the tick values? I would prefer the value to be in increments of 20. I tried using values=() and listing the values but that has not worked.    Thank you  data have; input group $ yaxis $ xaxis x2 x3; datalines; AHS High 346 346 . AHS Middle-High 220 220 . AHS Middle-Low 268 268 . AHS Low 312 312 . ASP High -281 . -281 ASP Middle-High -276 . -276 ASP Middle-Low -278 . -278 ASP Low -248 . -248 ; run; proc sgplot data=have; styleattrs datacolors=(teal black); hbarparm category=yaxis response=xaxis / group=group nooutline name='x' dataskin=matte; scatter x=x2 y=yaxis /markerchar=x2 labelstrip markercharattrs=(color=white size=12); scatter x=x3 y=yaxis /markerchar=x3 labelstrip markercharattrs=(color=white size=12); xaxis values=(-360 -340 -320 -300 -380 -260 -240 -220 -200 -180 -160 -140 -120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360) label= 'Quartile Ranking'; yaxis label= 'Frequency'; label group= 'Source'; title 'Figure 1. Total Correct Per Quartile of Ranking'; run; ... View more

Steve,   Thank you for the comment, it was helpful. I attempted the first method and unfortunately performing a 1:1 and then a 2:1 led to 1 of 3 groups being overrepresented in the 2:1. However, this thought did lead me to perform a 1:1 as suggested and then to take 1 of the groups from the 1:1 and match it to the remaining group in a 1:1 fasion. I then combined the matched from the second 1:1 with the previous 1:1 matches. This ended up leading to equal groups across the 3 procedure types. This method requires 2 different match keys and to create a new match key to combine the previous two but it appears to have worked for my purposes. Thank you again for the comment.  ... View more

Attached is my code for the proc summary. When I do this it returns the number of observations per quarter and year, I would like it to return the times var1=1 and the number of times var2=1 and plot both of those lines on a graph to see how they move in relationship to one another. Is there a way to modify this code to accomplish that. Thank you  proc summary data= check8; class qrt_yr; var var1 var2; output out=check9; run; ... View more

Rick,   This is a method to review already published data. Below are the datalines for this table. The p-value for this dichotomous outcome is >0.05. The goal is to see the minimum number of nonevents that need to change to an event for each outcome measure in order for the nonsignificant p-value, as measured by Fisher's exact test, to become significant. Alpha level 0.05. Based on previous analysis, I know the minimum number of events needed is 5 for this study and want to determine a way to replicate the analysis in SAS.  data want; input study $ group $ outcome $; datalines; A Exp Yes A Exp Yes A Exp Yes A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Exp No A Con Yes A Con Yes A Con Yes A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No A Con No ; run; proc freq data= want; table group*outcome /chisq fisher; run; ... View more