About AliRKM

AliRKM · ‎04-29-2020

Version 14.1 😞

AliRKM · ‎04-29-2020

Thanks for your answer and comment on how best to submit. Adding dfadj did not work. What does it do? 26 proc surveymeans data=meps.analysis; 27 var adj_RXEXPY1 adj_RXEXPY2 adj_TOTEXPY1 adj_TOTEXPY2; 28 stratum varstr; 29 cluster varpsu; 30 weight poolwt; 31 domain subpop/ dfadj diff; ____ 22 202 ERROR 22-322: Expecting ;. ERROR 202-322: The option or parameter is not recognized and will be ignored. 32 run;

AliRKM · ‎04-29-2020

Hi there, I would like to perform a t-test on survey data and found that the domain statement has a DIFF option. This is the data: data WORK.TTEST; infile datalines dsd truncover; input DUPERSID:$8. VARPSU:32. VARSTR:32. poolwt:32. subpop:32. adj_ERTEXPY1:32. adj_RXEXPY1:32. adj_RXEXPY2:32. adj_TOTEXPY1:32. adj_TOTEXPY2:32.; label DUPERSID="PERSON ID (DUID + PID)" VARPSU="VARIANCE ESTIMATION PSU" VARSTR="VARIANCE ESTIMATION STRATUM"; datalines; 10001101 1 1021 6820.3638388 1 . 7.1330798479 2695 2735.026616 4948 10001102 1 1021 6011.4727588 1 . . 9 824.38022814 362 10001103 1 1021 6080.6644898 1 880.42585551 . 165 7433.6882129 1535 10001104 1 1021 6139.2404668 1 1529.5361217 2560.7756654 10 4996.2129278 995 10002101 1 1077 3125.6143298 0 . 2720.7604563 1676 25133.91635 208014 10002102 1 1077 3681.7596113 0 . . . . . 10004101 3 1089 2492.3878598 0 . . 75 213.99239544 1009 10005101 2 1012 2844.490106 0 . 252.7148289 128 1427.634981 277 10005102 2 1012 3681.681901 0 . 110.05323194 167 2888.8973384 3673 10005103 2 1012 3480.0634415 0 326.08365019 . 12 467.72623574 706 10006101 1 1003 15081.075204 0 . 23.437262357 342 23.437262357 31340 10008101 1 1142 5955.1487343 0 . 7.1330798479 . 7.1330798479 . 10008102 1 1142 7483.37747 0 . 2793.1102662 1593 3482.9809886 2128 10008104 1 1142 4737.750246 0 . 2186.7984791 1470 3677.6121673 3033 10008105 1 1142 5663.2273393 0 . 1514.2509506 1537 3877.338403 3226 10008106 1 1142 7438.6524568 0 . 1091.3612167 1471 1545.8403042 2843 10008107 1 1142 4986.8989575 0 . 8.1520912548 6 13350.068441 1739 10008108 1 1142 4864.6059188 0 . . . . . 10009101 1 1004 6625.7804373 1 . . 10 228.25855513 1630 10009102 1 1004 7024.5235785 1 . 13.247148289 40 2221.4448669 33548 10009103 1 1004 6407.0544698 1 . 631.78707224 604 3222.1140684 2247 10009104 1 1004 13623.937655 0 . . 480 . 2046 10010101 1 1008 27326.603299 0 . 15.285171103 24 886.53992395 562 10010102 1 1008 26289.643731 0 . . . 619.55893536 140 10014101 2 1030 6102.345339 0 . 443.26996198 109 4156.5475285 2000 10016101 1 1144 3909.2115073 0 . . 12 . 12 10017101 2 1003 5244.852484 0 1183.0722433 15.285171103 . 1486.7376426 . 10017102 2 1003 7466.6795125 0 . . . 270.03802281 . 10019102 3 1124 5606.137352 0 . 5962.2357414 4502 16094.26616 31962 10021101 1 1130 4419.1832208 0 . . . . . 10022101 1 1018 2635.7591798 0 . 36.684410646 118 358.69201521 1349 10022102 1 1018 2953.8852153 0 . . . . 110 10022103 1 1018 2090.6765608 0 . 10.190114068 9 249.65779468 9 10023101 3 1023 2279.8954978 0 . 648.09125475 1130 1380.7604563 2432 10023102 3 1023 3654.2519563 1 . 262.90494297 67 965.00380228 1972 10024101 2 1059 2280.0660103 1 . 2067.5741445 3116 2159.2851711 3201 10024102 2 1059 2278.1422513 1 . . 3 . 7199 10024103 2 1059 1918.5318633 1 . . . . . 10024104 2 1059 1486.43269 01. . 29 445.30798479 7841 10024105 2 1059 1396.701735 0 . . . . 123 10024106 2 1059 1608.6954495 0 . . . 418.81368821 1367 10025101 1 1122 10648.338945 0 . 502.37262357 417 1501.0038023 1537 10025102 1 1122 9504.141299 0 . 394.35741445 2784 2268.3193916 2834 10025103 1 1122 12284.554372 0 . 1752.6996198 6975 2208.1977186 7508 3 The SAS System 10:27 Friday, April 24, 2020 10027101 1 1084 9151.1762425 0 . 4292.0760456 1332 10701.657795 11221 10027102 1 1084 9895.8191088 0 645.03422053 7972.7452471 7244 20407.741445 24101 10028101 1 1118 2733.7887845 0 . 6.1140684411 . 4805.6577947 8189 10029101 3 1124 5772.2379498 0 1505.0798479 2660.6387833 704 7164.6692015 11427 10031101 1 1013 5267.2207123 0 . 1769.0038023 4424 1769.0038023 12264 10031102 1 1013 3722.491092 0 . 325.06463878 667 9702.0076046 9481 ;;;; Is it possible that this option is not available on SAS Enterprise Guide (V7.15 HF8) as I keep getting an error message? ERROR 79-322: Expecting a DFADJ. ERROR 202-322: The option or parameter is not recognized and will be ignored. proc surveymeans data=meps.analysis; var adj_RXEXPY1 adj_RXEXPY2 adj_TOTEXPY1 adj_TOTEXPY2; stratum varstr; cluster varpsu; weight poolwt; domain subpop/diff; run; Many thanks

AliRKM · ‎04-22-2020

Still no luck: NOTE: A stored DATA STEP view cannot run under a different operating system. the output is blank. I tried to read up on the note but did not understand the comments. Nor do I understand what a data view is - again, despite reading up on it. I have no technical background, my statistics is shaky so this is all beyond me 😞 Thanks for your help.

AliRKM · ‎04-21-2020

No luck: Got this in the log: 26 proc print data=out; 27 where subpop=1 and nmiss(of F_a65_69 -- pmdinsy1) = 0; ________ 22 76 ERROR: Syntax error while parsing WHERE clause. ERROR 22-322: Syntax error, expecting one of the following: !, !!, &, (, *, **, +, ',', -, /, <, <=, <>, =, >, >=, ?, AND, BETWEEN, CONTAINS, EQ, GE, GT, LE, LIKE, LT, NE, OR, ^=, |, ||, ~=. ERROR 76-322: Syntax error, statement will be ignored. Tried parenthesis: where subpop=1 and nmiss(of (F_a65_69 -- pmdinsy1)) = 0; and got this: ERROR: WHERE clause operator requires numeric variables.

AliRKM · ‎04-21-2020

Hi all, I have run a procsurvey freq on many variables and would like to print the CrossTabs where all variables have (or do not have) a specific value Is there a way to stipulate something like: proc print...; where (var2 var2 var3) ne .; ? This is a sample of the data - sorry so long but most people have subpop=0 data WORK.EXAMPLE; infile datalines dsd truncover; input DUPERSID:$8. REGIONY1:32. SEX:32. HISPANX:32. RTHLTH1:32. MNHLTH1:32. ADSMOK2:32. INSCOVY1:32. PMDINSY1:32. subpop:32. other_race:32. white:32. afr_am:32. a65_69:32. a70_74:32. a75_79:32. over80:32. married:32. div_sep:32. widowed:32. nev_marry:32. n o_hs:32. hs_ed:32. more_hs:32. priv_ins:32. pub_only:32. no_ins:32. good_mhealth:32. norm_undwt:32. overwt:32. obese:32. vobese:32. polypharm:32.; label DUPERSID="PERSON ID (DUID + PID)" REGIONY1="CENSUS REGION AS OF 12/31/13" SEX="SEX" HISPANX="HISPANIC ETHNICITY (EDITED/IMPUTED)" RTHLTH1="PERCEIVED HEALTH STATUS - RD 1" MNHLTH1="PERCEIVED MENTAL HEALTH STATUS - RD 1" ADSMOK2="SAQ: CURRENTLY SMOKE,R 2" INSCOVY1="HEALTH INSURANCE COVERAGE INDICATOR 13" PMDINSY1="PMED INSURANCE ANY TIME IN R3"; datalines; 10002101 1 1 2 3 3 1 2 2 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0 1 10005101 4 2 2 3 3 2 2 2 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 1 10014101 1 2 2 4 4 2 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 1 10019102 2 2 2 3 3 2 1 2 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 1 10027101 1 1 2 3 1 2 2 2 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 10027102 1 2 2 5 3 2 2 2 1 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1 10034101 3 1 2 2 2 -1 1 2 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 10034102 3 2 2 2 2 -1 1 2 0 0 0 1 . 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 10040101 4 2 2 3 2 2 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 0 10047101 2 1 2 1 2 2 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 10049101 1 1 2 2 2 2 2 2 0 0 0 1 . 0 0 0 0 0 0 1 0 1 0 0 1 0 1 . . . . 0 10056101 3 1 2 2 3 2 1 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 10058101 3 1 2 2 2 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 10058102 3 2 2 1 1 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10061101 1 1 2 2 2 2 2 2 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 10061102 1 2 2 4 4 2 2 2 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 10061102 1 2 2 4 4 2 2 2 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 10064101 4 2 2 3 2 2 2 2 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 10067101 3 2 2 4 4 2 2 2 1 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 10067102 3 2 2 5 4 2 2 2 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 10068101 2 2 2 3 3 2 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 1 10076101 1 2 2 1 1 2 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 0 1 10079101 4 2 2 4 3 2 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 10083101 2 1 2 2 2 2 2 2 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 10083102 2 2 2 2 2 2 2 2 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 10086101 4 1 2 2 2 2 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10111101 2 1 2 5 2 2 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 10111102 2 2 2 1 2 2 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10112101 3 1 2 3 3 -9 2 2 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 10115101 4 1 2 3 1 2 2 2 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 10115102 4 2 2 2 1 2 2 2 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 3 The SAS System 12:01 Tuesday, April 21, 2020 10117101 3 1 2 3 3 2 1 2 0 0 1 0 0 0 0 1 0 0 1 0 . . . 1 0 0 1 0 1 0 0 1 10118101 3 2 2 3 2 1 1 1 0 0 1 0 . 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 10120101 1 1 2 2 2 2 1 2 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10120102 1 2 2 1 1 2 1 2 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 10126101 2 1 2 2 1 2 2 2 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 1 10130101 4 1 2 4 4 2 1 2 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 10130102 4 2 2 3 2 2 1 2 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 10135101 4 1 2 4 1 2 2 2 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 10135102 4 2 2 2 1 2 2 2 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 10136102 3 1 2 3 3 2 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1 10138101 4 1 2 2 2 2 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10138102 4 2 2 2 2 2 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10140101 2 1 2 2 3 -1 1 2 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 10140102 2 2 2 3 3 -1 1 2 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 10144101 3 2 2 3 3 2 2 2 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 10146101 3 2 2 2 2 2 2 2 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 10154101 4 1 1 1 1 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 10154104 4 2 1 4 3 2 3 2 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 1 10156103 3 2 1 4 1 2 2 2 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 10157101 1 1 2 3 2 2 2 2 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 1 10159101 4 2 2 2 2 2 1 2 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10162101 2 2 2 3 2 2 2 2 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 1 10166101 3 1 2 3 3 2 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 10166102 3 2 2 3 3 2 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 10168102 4 1 1 3 3 2 2 2 0 0 1 0 . 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 10169102 4 1 2 2 2 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 10175101 4 1 2 2 3 1 2 2 0 1 0 0 . 0 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 10180101 1 2 2 4 4 1 2 2 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 10181101 3 1 2 2 2 2 1 2 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 10181102 3 2 2 4 3 2 1 2 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 10183101 1 2 2 1 1 2 1 1 0 0 1 0 . 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 10184101 1 1 1 2 2 2 2 2 1 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 10200101 2 1 2 5 5 1 2 2 0 0 1 0 . 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 10206101 1 1 2 5 2 2 1 2 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 10206102 1 2 2 3 3 2 1 2 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 10209101 4 2 1 3 3 -1 1 1 0 0 1 0 . 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 10214101 3 1 2 4 4 2 1 1 0 0 0 1 . 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 10220101 2 2 2 2 2 2 2 2 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 10220102 2 1 2 2 3 2 2 2 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 10221101 3 1 1 3 1 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 10224101 4 1 2 2 1 2 1 1 1 0 1 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 10224102 4 2 2 1 1 2 1 2 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 10243101 4 2 1 3 1 2 2 2 0 0 1 0 . 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 1 10245101 3 1 2 1 1 2 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 10246101 4 2 2 3 3 1 2 2 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 10246102 4 1 2 4 3 1 2 2 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 10247101 2 1 2 4 2 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 10247102 2 2 2 4 4 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 10248101 2 1 2 3 3 2 2 2 0 1 0 0 . 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 10250101 1 2 2 2 3 2 2 2 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 10251101 2 1 2 4 1 2 1 2 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 10251102 2 2 2 3 1 2 1 2 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 10253101 3 2 2 2 1 1 1 1 0 0 0 1 . 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 10254101 3 1 2 3 2 2 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 1 10257101 3 1 1 4 1 -1 2 2 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 10260101 4 1 2 1 1 -1 1 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10260102 4 2 2 1 1 -1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 10261101 3 1 2 1 1 2 1 1 0 0 1 0 . 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 4 The SAS System 12:01 Tuesday, April 21, 2020 10269101 4 1 1 2 3 2 1 2 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 10274103 3 1 2 2 1 -1 2 2 0 0 1 0 0 1 0 0 0 1 0 0 . . . 0 1 0 1 1 0 0 0 0 10277101 3 1 2 2 2 2 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 10286101 4 1 2 4 3 2 2 2 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 1 10292201 2 1 2 1 1 1 2 2 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 10302101 3 2 2 2 2 2 3 2 0 0 1 0 . 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 10302102 3 1 2 2 2 2 1 2 0 0 1 0 . 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 10305101 2 2 2 3 2 2 2 2 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 1 10311101 3 2 2 3 3 2 2 2 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 1 10316101 2 2 2 4 4 1 2 2 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1 10327101 2 2 2 3 2 2 1 2 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1 ;;;; I tried this with no luck where subpop=1 and (F_a65_69 -- pmdinsy1 ne .); Maybe an array but isn't that only in data steps? As you can see form below, my variable names have no pattern to them. ods output Crosstabs=out; proc surveyfreq data=meps.analysis; strata varstr; cluster varpsu; weight poolwt; tables subpop*(a65_69 a70_74 a75_79 over80 sex white afr_am other_race hispanx married div_sep widowed nev_marry no_hs hs_ed more_hs inscovy1 rthlth1 mnhlth1 norm_undwt overwt obese vobese adsmok2 curr_smoke polypharm pmdinsy1 ); run; proc print data=out; where subpop=1; var Frequency WgtFreq StdDeva65_69 a70_74 a75_79 over80 sex white afr_am other_race hispanx married div_sep widowed nev_marry no_hs hs_ed more_hs inscovy1 rthlth1 mnhlth1 norm_undwt overwt obese vobese adsmok2 curr_smoke polypharm pmdinsy1; run;

AliRKM · ‎04-13-2020

Perfect - thank you. Read some more - the t-test is a test of means so asking for a test of medians was a mistake on my part.

AliRKM · ‎04-13-2020

Thank you. I did not realise how complicated this is. I am really new to SAS so it will probably take me longer to understand the code you kindly provided than to create the table manually 😞 So much to learn.

AliRKM · ‎04-13-2020

Thanks @PaigeMiller. I am really new to this, do you mind providing example code? I'm looking at about 20 variables which is why I would love an automated process.

AliRKM · ‎04-10-2020

Thanks. I do indeed have lots of missing values. I did this originally using similar code to the what you have showed here but I have about 40 variables so manually creating the output I want from the freqtables and chisqtables is very onerous. Was hoping for a shortcut. My best bet is probably to proc tabulate matched to proc freq. CALL Execute statements are above my paygrade 😞

AliRKM · ‎04-10-2020

Hi there, Is there a way to combine proc means with proc ttest across several variables to create a single table that gives mean, sd, median and iqr for different groups as well as a ttest. This creates an approximation of what I want: proc means data=sashelp.cars n mean stddev median q1 q3 prt; class origin; var msrp invoice MPG_Highway;run; The difference is that I want origin as columns and the ttest to compare means and medians across origins: Asia Europe USA Variable N Mean Std Dev Median Lower Quartile Upper Quartile N Mean Std Dev Median Lower Quartile Upper Quartile N Mean Std Dev Median Lower Quartile Upper Quartile Pr > |t| MEDIAN Pr > |t| MEAN MSRP 158 24741.32 11321.07 23032.5 17200 28800 123 48349.8 25318.6 40590 33780 56595 147 28377.44 11711.98 25520 20310 33995 p p Invoice 158 22602.18 9842.98 20949.5 16265 26660 123 44395.08 23080.37 37575 31187 51815 147 25949.34 10518.72 23217 18881 30846 p p MPG_Highway 158 28.26582 6.770503 27 25 31 123 26.00813 4.167588 26 24 29 147 26.01361 5.396582 26 22 29 p p Many thanks

AliRKM · ‎04-10-2020

Hi all, I'm wanting to create a single table comparing variables between 2 groups with results of chisq. This code from @ballardw solves part of the problem: proc tabulate data=sashelp.cars; class make ; class type; class origin; tables type origin, (all='All makes' make)*( rowpctn n) ; run; Is there a way to add a chisq test directly to the output - without having to run a proc freq and ods output OneWayChiSq=chisq?

AliRKM · ‎04-10-2020

Thanks for your reply. I needed a dataset. Thanks for the tips - will include a more diverse data set next time.

AliRKM · ‎04-10-2020

In the end I found an ugly work-around: data intermediate; set have; if round=1 then days=r1days; if round=2 then days=r2days; if round=3 then days=r3days; run; proc sort data=intermediate; by id drug round;run; data want; set intermediate; by id, drug; retain tot_drug_days; if first.rxdrgnam then tot_drug_days = drug_days; else tot_drug_days=tot_drug_days+drug-days; /*then the same for fills and pills*/ retain total_days; if first.rxdrgnam then tot_days = days; else tot_days=tot_days+days; percent=tot_drug_days/tot_days; run; Thanks for all your help - really need to learn more about arrays...

AliRKM · ‎04-10-2020

Thanks Jag, that did not differentiate between drugs only being purchased in different rounds.

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