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stellapersis7

Hi all, Please help me to code this. This is the dataset below- I have 2 drugs being used for each ID i.e., SGA and DM. I have multiple start date and end dates for each ID and drug. I want to see if the DM_start falls into any of the SGA_start and SGA_end under each id. That is DM_start of period 3 can fall in the SGA_start and SGA_end of period1. And also calculate the number of overlapping days for each time period. I want to know the no. of days for each orange period. Thank you all in advance, Any help would be appreciated.

rick_b

When a gambler decides how much to bet, their decision is influenced by their prior wager outcomes (perhaps they start to double down if they have sustained losses, for example), and I am trying to examine how NFL gamblers' prior betting profits/losses influence their next-round-of-betting behavior. I need to sum just the profits/losses from last round of completed NFL events. NFL events occur in distinct blocks (e.g., Sunday at 1:00, 4:00, and 8:00, Monday at 8:00, Thursday at 9:00) and last three hours, but bets can be placed any time from a week before the block up until game time. The dataset I am attaching contains only one gambler but there are many gamblers (each uniquely identified by product_account_id), so the sum needs to begin anew when a new product_account_id is reached. As you can see, things get especially messy with rows 88-103. I would also like to set up a structure where I can sum the profits from the last 2, 3, 4, and 5 rounds of betting. Any help would be greatly appreciated. data WORK.bets; infile datalines; input product_account_id:$10. bet_id:$15. eleven_digit_account_id:$11. net_stake:3. bet_date:mmddyy10. bet_time:time8. bet_day_of_week:$10. event_date:mmddyy10. event_time:time8. event_day_of_week:$10. bet_profit:5. desired_output:5.; format bet_date mmddyy10. bet_time time8. event_date mmddyy10. event_time time8.; datalines; 12101170 81758848.516 00012101170 2 10/9/2023 20:29 Monday 10/9/2023 20:16 Monday 2 . 12101170 81758939.516 00012101170 3 10/9/2023 20:30 Monday 10/9/2023 20:16 Monday 2.4 . 12101170 81759255.516 00012101170 2 10/9/2023 20:34 Monday 10/9/2023 20:16 Monday 1.68 . 12101170 81760925.516 00012101170 3 10/9/2023 20:48 Monday 10/9/2023 20:16 Monday 2.61 . 12101170 81764937.516 00012101170 3 10/9/2023 21:26 Monday 10/9/2023 20:16 Monday -3 . 12101170 81768542.516 00012101170 3 10/9/2023 22:03 Monday 10/9/2023 20:16 Monday 2.64 . 12101170 82231252.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 13:01 Sunday 2.73 8.33 12101170 82231251.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82231250.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 13:05 Sunday 2.46 8.33 12101170 82231253.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 16:26 Sunday 2.85 8.33 12101170 82231254.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 16:27 Sunday -3 8.33 12101170 82231345.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 13:05 Sunday 2.46 8.33 12101170 82231346.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82231347.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 16:26 Sunday 2.85 8.33 12101170 82231348.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 16:27 Sunday -3 8.33 12101170 82231466.516 00012101170 3 10/14/2023 14:40 Saturday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82231465.516 00012101170 3 10/14/2023 14:40 Saturday 10/15/2023 13:05 Sunday 2.46 8.33 12101170 82231467.516 00012101170 3 10/14/2023 14:40 Saturday 10/15/2023 16:26 Sunday 2.85 8.33 12101170 82348229.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 9:35 Sunday -3 8.33 12101170 82348234.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:01 Sunday -3 8.33 12101170 82348231.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:01 Sunday 2.61 8.33 12101170 82348230.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:03 Sunday -3 8.33 12101170 82348233.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:04 Sunday 2.55 8.33 12101170 82348235.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:05 Sunday 2.94 8.33 12101170 82348232.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82348267.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:01 Sunday 2.61 8.33 12101170 82348270.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:01 Sunday -3 8.33 12101170 82348266.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:03 Sunday -3 8.33 12101170 82348269.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:04 Sunday 2.55 8.33 12101170 82348271.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:05 Sunday 2.94 8.33 12101170 82348268.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82348305.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:01 Sunday -3 8.33 12101170 82348307.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:01 Sunday 2.85 8.33 12101170 82348308.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:03 Sunday -3 8.33 12101170 82348306.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:05 Sunday 2.94 8.33 12101170 82348304.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82439386.516 00012101170 3 10/15/2023 13:14 Sunday 10/15/2023 13:04 Sunday -3 8.33 12101170 82439455.516 00012101170 3 10/15/2023 13:14 Sunday 10/15/2023 13:01 Sunday 2.73 8.33 12101170 82439531.516 00012101170 3 10/15/2023 13:14 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82478787.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:06 Sunday 2.46 32.49 12101170 82478786.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82478788.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82478789.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:27 Sunday -3 32.49 12101170 82478974.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:06 Sunday 2.46 32.49 12101170 82478973.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82478975.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:27 Sunday -3 32.49 12101170 82479827.516 00012101170 6 10/15/2023 16:03 Sunday 10/15/2023 16:26 Sunday 5.46 32.49 12101170 82479826.516 00012101170 6 10/15/2023 16:03 Sunday 10/15/2023 16:27 Sunday -6 32.49 12101170 82482465.516 00012101170 3 10/15/2023 16:07 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82493073.516 00012101170 2 10/15/2023 16:34 Sunday 10/15/2023 16:26 Sunday 1.92 32.49 12101170 82550551.516 00012101170 2 10/15/2023 20:57 Sunday 10/15/2023 20:23 Sunday -2 11.22 12101170 82553228.516 00012101170 2 10/15/2023 21:23 Sunday 10/15/2023 20:23 Sunday -2 11.22 12101170 82555259.516 00012101170 2 10/15/2023 21:44 Sunday 10/15/2023 20:23 Sunday -2 11.22 12101170 82646184.516 00012101170 3 10/16/2023 20:25 Monday 10/16/2023 20:16 Monday -3 -6 12101170 82647533.516 00012101170 3 10/16/2023 20:34 Monday 10/16/2023 20:16 Monday -3 -6 12101170 82647545.516 00012101170 6 10/16/2023 20:34 Monday 10/16/2023 20:16 Monday -6 -6 12101170 82899789.516 00012101170 2 10/19/2023 20:23 Thursday 10/19/2023 20:16 Thursday 1.96 -12 12101170 82904693.516 00012101170 3 10/19/2023 20:59 Thursday 10/19/2023 20:16 Thursday 2.64 -12 12101170 82906147.516 00012101170 3 10/19/2023 21:08 Thursday 10/19/2023 20:16 Thursday -3 -12 12101170 83235976.516 00012101170 3 10/22/2023 9:23 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83235974.516 00012101170 3 10/22/2023 9:23 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83236035.516 00012101170 3 10/22/2023 9:24 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83236033.516 00012101170 3 10/22/2023 9:24 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83236190.516 00012101170 3 10/22/2023 9:25 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237824.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83237827.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237823.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83237825.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:05 Sunday 2.64 1.6 12101170 83237829.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83237922.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83237924.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237921.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83237926.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238000.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83238002.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237999.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238004.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238165.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83238164.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238170.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238167.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238244.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83238249.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238246.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238318.516 00012101170 3 10/22/2023 9:39 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238315.516 00012101170 3 10/22/2023 9:39 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83263216.516 00012101170 6 10/22/2023 11:14 Sunday 10/22/2023 13:02 Sunday -6 1.6 12101170 83395974.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83395975.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83395972.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:26 Sunday 2.55 -63.36 12101170 83395973.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:26 Sunday 2.55 -63.36 12101170 83398125.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83398126.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83398127.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:26 Sunday 2.64 -63.36 12101170 83398128.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:26 Sunday 2.82 -63.36 12101170 83403470.516 00012101170 3 10/22/2023 17:13 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83403472.516 00012101170 3 10/22/2023 17:13 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83408418.516 00012101170 3 10/22/2023 17:44 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83417137.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83417136.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:06 Sunday 1.71 -63.36 12101170 83417135.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83417134.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:26 Sunday 1.89 -63.36 12101170 83417192.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83540873.516 00012101170 3 10/23/2023 19:09 Monday 10/23/2023 20:16 Monday -3 -24 12101170 83575341.516 00012101170 3 10/23/2023 21:21 Monday 10/23/2023 20:16 Monday -3 -24 12101170 83576079.516 00012101170 3 10/23/2023 21:28 Monday 10/23/2023 20:16 Monday -3 -24 proc print; run;

telligent

The course references this library: libname locallib 'home/student/DMML35'; but I am working on my own machine so I don't have access to that library. Where should I save the course materials? I tried saving them my users folder libname locallib '/Users/Training Materials'; But I get ERROR: Library locallib does not exist. I do see it on my list of libraries but I can't open it.

LunaLoomer · ‎01-03-2024

Hello Community, I'm at a crossroads and would appreciate your insights. I aspire to work as a biostatistician or data scientist in the pharmaceutical sector, where proficiency in SAS is often a prerequisite. While I haven't used SAS in my previous roles, it seems crucial for roles in the pharma industry. Considering this, do you think it's worth investing 1-2 years to learn SAS for my career goals, or do you foresee a shift towards R/Python in these industries in the near future? Your advice and perspectives are highly valued. Thanks in advance!

jmbmpm

I am trying to analyze a dataset that has two dichotomous variables (placstat and hiv) predicting a continuous variable (lplacwbc). The data are unbalanced. I am interested in seeing a matrix for pairwise comparisons, with p values, for all possible comparisons. The following code yields the output provided (not showing the model output, only lsmeans output). I may be missing something simple here, but I cannot figure it out! proc glm; class placstat hiv; model lplacwbc=placstat hiv placstat*hiv/ ss1 ss2 ss3 ss4; lsmeans placstat hiv/pdiff=all adjust=tukey; run;