Hi all, My goal is to produce several parameter-by-age series for each ID and have several reference percentiles on the same plot. Reference percentiles' data are provided for the age range of 24-240: Age (in months) 3rd Percentile BMI Value 5th Percentile BMI Value 10th Percentile BMI Value 25th Percentile BMI Value 50th Percentile BMI Value 75th Percentile BMI Value 85th Percentile BMI Value 90th Percentile BMI Value 95th Percentile BMI Value 97th Percentile BMI Value 24 14.52095 14.73732 15.09033 15.74164 16.57503 17.55719 18.16219 18.60948 19.33801 19.85986 24.5 14.50348 14.71929 15.07117 15.71963 16.54777 17.52129 18.11955 18.56111 19.2789 19.79194 25.5 14.46882 14.68361 15.03336 15.67634 16.49443 17.45135 18.03668 18.4673 19.16466 19.66102 ... ... ... ... ... ... ... ... ... The actual data is much narrower by the age and also very scarce in datapoints for each ID. They have to be on the same dataset, so both setting and merging them together were tested, but desired output can't be achieved either way using SGPLOT. What is the key to have these series be plotted correctly? Current code: PROC SGPLOT DATA = plotdata;
styleattrs datacontrastcolors=('red' 'green' 'blue' 'black' 'maroon');
SCATTER Y=chaaval X=ageass / MARKERATTRS = (SYMBOL=circlefilled size=5px color="GREEN") legendlabel='Group 1' name='cohorta' ;
SERIES Y=chaaval X=ageass / group=USUBJID datalabel=subjnum LINEATTRS = (THICKNESS = 2.25 COLOR="GREEN" PATTERN = Solid) legendlabel='Group 1' name="linea";
SCATTER Y=chbaval X=ageass / MARKERATTRS = (SYMBOL=circlefilled size=5px color="RED") legendlabel='Group 2' name='cohortb' ;
SERIES Y=chbaval X=ageass / group=USUBJID datalabel=subjnum LINEATTRS = (THICKNESS = 2.25 COLOR="RED" PATTERN = Solid) legendlabel='Group 2' name="lineb";
SERIES Y = _3RD_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='3rd percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _5TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='5th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _10TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='10th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _25TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='25th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _50TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=orange PATTERN = Solid) curvelabel='50th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _75TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='75th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _90TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='90th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _95TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='95th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
SERIES Y = _97TH_PERCENTILE_VALUE X = agem_htab / LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) curvelabel='97th percentile' CURVELABELATTRS=(SIZE = 5 COLOR=gray weight=NORMAL) CURVELABELLOC=OUTSIDE;
REFLINE 15 20 25 30 35 49/LINEATTRS = (THICKNESS = 1 COLOR=gray PATTERN = Solid) transparency=.3;
XAXIS label="BMI at Visit (months)" values=(24 to 132 by 12);
YAXIS label= "BMI" values=(15 to 40 by 5);
KEYLEGEND 'cohorta' 'cohortb' /title="Group:" location=OUTSIDE position=bottom autoitemsize;
run; Data example: subjnum AGEASS chaaval chbaval AVAL agem_htab 1 99.876796715 17.6 . 17.6 99.5 2 68.73100616 16.4 . 16.4 68.5 2 73.659137577 16.9 . 16.9 73.5 3 87.260780287 . 19.3 19.3 86.5 3 91.86036961 . 19 19 91.5 4 72.542094456 . 19.2 19.2 72.5 5 56.509240246 . 16.7 16.7 56.5 Expected kind of plot:
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