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JC411911

Hello, I have the below code which is part of larger code that will stop running the rest of the program if there is a difference in the proc compare. It is working okay but I am curious if there is a way to rename the _type_ variable that is created for the comparison. Right now it list BASE and COMPARE which is great but for a user that isn't aware I would like to rename to the actual file names. Is this possible? %let diff_count = 0; /* Compare the TO_TAX and FROM_TAX BUWfiles */ PROC COMPARE BASE=WORK.TO_TAX_MODIFIED brief transpose COMPARE=WORK.FROM_TAX_MODIFIED OUT=COMPARISON OUTBASE OUTCOMP OUTDIF LISTOBS OUTNOEQUAL BRIEFSUMMARY; VAR member_number account_number payer_id ; title 'COMPARING FILE SENT AND RECEIVED FROM'; RUN; DATA _NULL_; SET COMPARISON END=eof; IF eof THEN CALL SYMPUT('diff_count', _N_); RUN;

mitrakos

Hello everyone, I have survey data of approximately 5000 participants and I am currently looking to model the outcome of a dichotomized response variable to a predictor. Our question is if there is a trend of participants being more likely to be in the 1 level of the response variable as we move from 4 to 1 in the ordinal response predictor. The dichotomized response has levels 1 and 0 while the predictor is an ordinal survey response with levels 1 - "Yes - completely", 2 - "Yes - mostly", 3 - "Yes - somewhat", and 4 - "No". I am currently wondering how to structure my proc logistic code and also weighing the use of proc logistic vs. proc surveylogistic. I do not have survey weights, so it seems that proc surveylogistic would not be useful in comparison to proc logisitic. Here is my code so far: proc logistic data = import plots=all; class outcome q1 / param=ordinal; model outcome = q1; run; I chose ordinal for the param= statement however I am not sure about that, though changing the param= option does not change the model fit statistics. I will continue to read documentation about proc logistic to see what other options might suit, but if there are any suggestions please let me know. Thank you!

GeorgeTr

I am currently working on the analysis of animal behaviour (frequency and duration) in a 2 x 2 factorial design, on two days (d2 and d16). Behavioural frequencies were analysed with proc glimmix with a Poisson distribution and Log link function, and a multiplicative overdispersion parameter: proc glimmix data=behav; by Obs_Day; class Batch Sanitary Diet; model Fighting_Total_number = Batch Sanitary|Diet / dist=Poisson link=log; random _residual_; lsmeans Batch Sanitary|Diet / pdiff; run; Durations were analysed with proc glimmix, with a binomial distribution and logit link function, and a multiplicative overdispersion parameter: proc glimmix data = behav; NLoptions Maxiter = 2000; by Obs_Day; class Batch Sanitary Diet; model Fighting_Total_duration = Batch Sanitary|Diet / dist = binomial link = logit; random _residual_ ; lsmeans Batch Sanitary|Diet / pdiff; run; These are standard methods used by my department for such analyses. However, I was later told that because one treatment group had 0 incidences of a certain behaviour on d2, I cannot use proc glimmix. Is that so? If yes, what is the alternative? I would appreciate any help from the community.

JoseRomero

This t-Test is taken from Statistical Business Analysis Using SAS libname sasba 'c:\sasba\ames'; data ames; set sasba.ames300; run; proc format; value yesno 0=No 1=Yes; run; proc ttest data =ames plots (only)=qq alpha=.05 h0=0; class bonus; var gr_liv_area; format bonus yesno.; run; Let's imagine we want to repeat the test using Bayes, in BUGS we could approach/reproduce! that result with this model. model { for (i in 1:2) { mu[i] ~ dnorm(0.0,1.0E-6) tau[i] ~ dgamma(0.001,0.001) } for (i in 1:n) { area[i] ~ dnorm(mu[bonus[i]+1],tau[bonus[i]+1]) } x1 ~ dnorm(mu[1],tau[1]) x2 ~ dnorm(mu[2],tau[2]) dx<- x1-x2 dmu<-mu[1]-mu[2] } list( area = c(864, 1829, 1328, 1063, 2207, 972, 912, 1978, 1801, 2018, 882, 1370, 1350, 2402, 1600, 2020, 1629, 2452, 2490, 1114, 864, 1740, 1728, 1313, 1154, 1113, 1567, 1392, 1144, 1478, 1297, 1062, 1121, 1092, 1513, 1368, 1560, 1680, 2036, 1641, 874, 1782, 2464, 1574, 1460, 1175, 1740, 925, 1136, 1092, 1100, 1196, 1044, 825, 1117, 882, 1211, 1524, 1094, 1418, 924, 1445, 1091, 1279, 923, 816, 914, 872, 2633, 1636, 988, 1093, 1214, 1150, 912, 1442, 1721, 922, 948, 952, 1242, 897, 955, 1299, 998, 1342, 1442, 1500, 907, 1214, 768, 1839, 1680, 1696, 1478, 2279, 1240, 1040, 1293, 1868, 1780, 2156, 1334, 1251, 1216, 1124, 884, 1045, 1073, 1159, 1458, 1124, 1654, 1339, 720, 1068, 1296, 1022, 952, 875, 520, 838, 672, 816, 778, 968, 960, 1047, 694, 1103, 693, 641, 865, 884, 1108, 1616, 1536, 1962, 1154, 1668, 1374, 1461, 1328, 1324, 1412, 1112, 1716, 1529, 1672, 1406, 1079, 1376, 924, 1846, 1174, 1635, 1274, 1409, 1316, 1382, 1362, 1426, 1123, 1717, 1312, 1466, 1440, 1176, 1324, 1635, 1221, 1595, 1629, 3279, 1960, 1733, 2599, 1845, 1352, 3222, 1392, 1274, 1797, 2673, 1868, 2224, 1432, 1594, 2365, 2450, 2122, 1730, 2090, 2142, 1352, 1683, 2020, 1764, 1779, 1660, 2034, 1961, 2237, 1638, 2332, 1456, 1720, 1792, 2322, 2125, 1933, 1737, 1978, 1796, 1611, 2022, 2200, 1852, 2031, 1764, 1710, 1582, 1656, 1600, 1642, 2030, 1877, 2643, 2758, 2551, 2385, 2398, 2531, 2538, 2154, 2080, 1812, 2654, 2127, 1958, 1873, 1308, 1796, 1668, 2090, 1797, 1408, 1756, 1501, 2263, 1574, 1914, 1509, 1414, 1976, 1911, 1499, 1995, 1724, 2315, 2519, 1560, 1482, 1768, 1427, 1369, 1344, 2168, 2358, 1086, 2601, 1660, 1824, 1355, 1098, 1428, 2009, 1436, 1784, 2104, 1374, 1152, 1034, 1382, 1472, 1374, 1430, 2071, 1166, 1165, 1350, 1656, 954, 996, 965, 768, 1034, 898, 999, 1291), bonus = c(0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), n=300 ) My problem is that when I try to reproduce this in Proc MCMC I get crazy results, without converge and in the autocorrelation I have zebra patterns. I think* the problem is in model area ~ dnorm(mu[bonus+1],tau[bonus+1]) I say I think because the log is clean and all I have is my suspicion that mu[1] and tau[1] fail when bonus=1 and mu[2] and tau[2] fail when bonus=0. A failure is understood as not taking the record as null and ignoring it. If someone has any idea, I would appreciate it.

Daily1

How to create a tabulated report based on data: Column CATEGORY in H means a horizontal report. TITLE_LEVELS indicate how many levels of title there are. TITLE_LEVEL1 is the main header, TITLE_LEVEL2 is the sub-header of TITLE_LEVEL1, and TITLE_LEVEL3 is the sub-header of TITLE_LEVEL2. All columns are Character data type proc tabulate data=datset; class TITLE_LEVEL1 TITLE_LEVEL2 TITLE_LEVEL3 Column1 ; table TITLE_LEVEL1* TITLE_LEVEL2*TITLE_LEVEL3; run; i have data i want PFA