hello @samp945    I cannot say doing the following will help you find a 'better ' model, but here are a couple of things you may consider when trying to explore model diagnostics:   1. you may find it helpful to plot the time series and see if you can visually identify some patterns. I used the following to produce a time series plot and the first thing that I noticed from the plot, is that there may be some level shift around 2016~2017, so having a CHECKBREAK option in the LEVEL statement may help.   proc timeseries data = a(where = (CauseOfDeath='Traffic')) plot = series; id DeathMonth interval = month; var count ; run;   2. you may consider starting from a baseline model similar to the following example in the doc(or some other baseline model you find appropriate for your data), and check for breaks in the baseline model using the CHECKBREAK option in the LEVEL statement,   SAS Help Center: A Seasonal Series with Linear Trend   I tried the above baseline model and the results seem to indicate level shift in Aug. 2016 in the baseline model.    3.  If level shift is confirmed in the baseline model (as is the case using the above baseline model),  you may want to include the level shift dummy variable as a regressor in the model, following shows an example of level shift detection and how to account for it:   https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/etsug/etsug_ucm_examples07.htm     You may want to do some further data explorations that may help you find alternative candidate models that suite your data, but I hope the above several notes are helpful to you.         ... View more

Give it an initial value 7 by RETAIN statement, as the URL posted by Tom. data temp; set temp; by age; retain n 7; n+first.age; run; ... View more

Thank you! Your advice solved the problem for me.   I used the instructions in this blog post to semi-automatically create an attribute map with the ID variable values set to GraphData1-GraphDataN for MarkerStyle, LineStyle, and TextStyle.   As you pointed out, the problem was that the HTML output uses a different default style (htmlblue) than the LISTING output (listing). This caused the on-screen HTML output to show solid lines but the PNG files created with the default LISTING style to show dashed lines.   I specified the ODS LISTING STYLE as htmlblue and the problem is fixed!   ods listing style=htmlblue gpath='\\Figures' image_DPI=300; Thanks again! ... View more

@samp945 wrote: One question though: I am not familiar with the _ALL_ variable that you used on the by statement. If I understand correctly, using _ALL_ after the first two variables (time stratumnum) has the effect of including all possible combinations of every other variable without actually having to write those all out. Is that correct? The variable list _ALL_ stands for all variables in dataset KMSurvivalPlotOffType, ordered by variable number (see PROC CONTENTS output). So, adding more variable names in the BY statement actually creates a list with duplicate names, but the duplicates (here: time and stratumnum contained in _ALL_) are ignored, as is mentioned in the second note in the log.   Putting time stratumnum first in the BY statement ensures that dataset WANT will be sorted by time stratumnum, regardless of their position (variable number) in the dataset, which I didn't want to make assumptions about. Sorting by time is crucial for the SERIES statement of the PROC SGPLOT step. The secondary sort key stratumnum determines the order of the treatment groups in the x-axis table, in the legend and also regarding color assignment. At least this is true for the simulated sample data. You could insert descending before stratumnum to switch treatment order.   I think the sort order of the remaining variables in KMSurvivalPlotOffType has no impact on the graph, so covering them by the abbreviation _ALL_ was a convenient way to have NODUPKEY remove duplicate observations without losing any combination of values. ... View more

You could put these datalabel into a SCATTER statement. data have; call streaminit(123); do Time=1 to 100; Group=1; x=rand('uniform'); y=pdf('normal',x); _x=.;_y=.;year=.; if Time in (2 3 5 20 40 80 95 99) then do;_x=x;_y=y;year=2.45;end; output; Group=2; x=x+0.005; y=y+0.001; _x=.;_y=.;year=.; if Time in (2 3 5 20 40 80 95 99) then do;_x=x;_y=y;year=2.45;end; output; end; run; proc sort data=have ;by group x;run; proc sgplot data=have noborder; series x=x y=y /lineattrs=(pattern=solid) Group=Group; scatter x=_x y=_y/datalabel=year group=group ; run;     ... View more

Since the segments starting position don't always align, this graph doesn't really do that.    I'd consider some variation on a slope graph with small multiples possibly.   Slope Graph https://www.storytellingwithdata.com/blog/2020/7/27/what-is-a-slopegraph   Small Multiples https://kieranhealy.org/blog/archives/2020/03/27/a-covid-small-multiple/       ... View more

Yes. variable WANT is retained due to SUM Statement "want+1". As mkeintz pointed out , "want+1" already included statement "retain want 0" . ... View more

Thank you so much, yes that makes total sense. ... View more

The other suggestions/observations are valid points, but I agree this is the key mystery: "why changing the DPI in SGPLOT seems to slightly change the font size".   Perhaps the developers could check the underlying calculations(?)  @DanH_sas ?   ... View more

Please provide some example data and the graphics code you run, so we can recreate the issue. It might very well be that you have stumbled across a SAS bug. ... View more

Whether for the four groups (predictor level combinations) in a two-way interaction of binary predictors, or the eight groups in a three-way interaction, each group has an odds or log odds. It does not have an odds ratio. An odds ratio or log odds ratio compares the odds (or log odds) of exactly two of the groups. If you are saying that you want to compare pairs of the groups, then this is simply done using the LSMEANS statement like the following which will provide the odds ratio and log odds ratio for each pairwise comparison of the groups defined by the a*b*c interaction.   lsmeans a*b*c / ilink diff oddsratio cl; Since there will be several odds ratio estimates (28 pairwise comparisons for 8 groups) it is obviously not possible for these to all match the three-way interaction parameter estimate. In the case of the difference in difference, we used the LSMESTIMATE statement to estimate a single function of the four groups which is equivalent to the definition of the interaction, so the estimate and p-value of that single estimate is the same as the parameter estimate and p-value of the two-way interaction parameter.   ... View more

Do you have any thoughts as to why it seems that researchers (to my knowledge) do not take the approach that I am using   No, this is not my field of endeavor and so I really can't comment on this. ... View more