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Mushy

Hello, I am updating huge table using update statement in proc sql. Its taking very long time. I need to just blank/null few char/num fields in the tables based of the dataset that provides the fields to be blanked. Is there an efficient way to deal with this? Thanks, Mushy

CathyVI

Hello Everyone, I need help trying to find the count for the multi_event column. Here is a sample code and my output. I was able to get the count for event 1-4 only. I also want to count when event is in more than 1 column. data have; input event1 event2 event3 event4; datalines; . 1 . . 1 1 . . . . 1 . 1 . . 1 1 . . . . 1 . . . . 1 . . 1 1 1 . . . 1 1 . . . . . 1 1 ; run; SAS output Row 2 and 4 met my criteria for multi_event so they were output in multi_event. Row 8 and 11 met the criteria but did not output. I want row 8, 11 or any other row with multiple count to be in the multi_event column. Here is my attempted code (My coding is not classic I will appreciate learning a better way to code it) data want; set have; if event1 ne . and event2=. and event3=. and event4=. then event1only=1; if event2 ne . and event1=. and event3=. and event4=. then event2only=1; if event3 ne . and event1=. and event2=. and event4=. then event3only=1; if event4 ne . and event1=. and event2=. and event3=. then event4only=1; if event1 ne . and (event2 ne . or event3 ne . or event4 ne . ) then multi_event=1; run; This is my final desired output is to be able to count each column like this with proc tabulate or report. Event1only Event2Only Event3Only Event4Only Multi_event 2 2 2 1 3 Thanks

rhampton16

Before I realized PROC PLM was a thing for getting simple slopes, I was taught how to get them from the proc mixed output by combining the estimates available there. I'm now noticing that one estimate is consistently different. Let's say we have data A B and M, where A is categorical with 2 levels, B is categorical with 2 levels, and M is continuous. I want to get the slope for M at: A1B1 A2B1 A1B2 A2B2 (reference) The default reference for categorical is the last value so A2B2 is the reference. If you calculate by hand from the solution for fixed effects, the values are the same for A2B2, A2B1, and A1B2 but wildly different for A1B1 and I can't for the life of me figure out why. For example: A2B2 is -.2300 (same in PLM) A2B1 is -.2300 + 1.4324 = 1.2033 (1.2024 in PLM) A1B2 is -.2300 + .4836 = .2555 (.2535 in PLM) using the same method, A1B1 would be -.2300 + -1.3125 = -1.542 using PLM, A1B1 is .3735 Here's my syntax for the estimates: estimate 'medinc x Target1 x stim1' medinc 1 medinc*target 0 1 medinc*stim 0 1 medinc*target*stim 0 0 0 1, 'medinc x Target1 x stim2' medinc 1 medinc*target 0 1 medinc*stim 1 0 medinc*target*stim 0 0 1 0, 'medinc x Target2 x stim1' medinc 1 medinc*target 1 0 medinc*stim 0 1 medinc*target*stim 0 1 0 0, 'medinc x Target2 x stim2' medinc 1 medinc*target 1 0 medinc*stim 1 0 medinc*target*stim 1 0 0 0 /e; Does anyone understand the difference? What is the value in the solution for A1B1 if not the comparison to the joint reference?

Psueri

Hello Sas Community, I am currently working on a project where I need to utilize PROC COUNTREG in SAS, particularly focusing on utilizing ZICMP distribution with spatialeffects. I'm trying to manually score based on the parameters, and from the documentation, I've reconstructed the following steps. Let: n be the number of observations m be the number of model regressors s be the number of spatial effects regressors z be the number of zero model regressors d be the number of dispersion model regressors X1 (n x m) be the matrix containing the values of m model regressors X2 (n x s) be the matrix containing the values of s spatial effects regressors Z1 (n x z) be the matrix containing the values of z zero model regressors D1 (n x d) be the matrix containing the values of d dispersion model regressors Wmat (n x n) be the spatial weights matrix Beta1 (m x 1) be the vector containing the coefficients of m model regressors Beta2 (s x 1) be the vector containing the coefficients of s spatial effects regressors Gamma (z x 1) be the vector containing the coefficients of z zero model regressors Delta (d x 1) be the vector containing the coefficients of d dispersion model regressors Intercept be the value of the intercept of the model Inf_Intercept be the value of the intercept of the zero model Disp_Intercept be the value of the intercept of the dispersion model , or But the values of lambda vector obtained in the output of the countreg differs from the ones computed as exp(xbeta), that is instead the value mu in the countreg output. (in documentation there is a typo in the ZICMP, but it's correct in CMP and the results are the same of countreg output) Now there isn't a finite form for this Normalization Factor, following the paper: Approximating the Conway–Maxwell–Poisson distribution normalization constant Steven B. Gillispie ∗ and Christopher G. Green University of Washington, Seattle, USA — Department of Statistics Technical Report no. 615 June 6, 2013 I found this approximation formula And then Now, the predicted results obtained do not match those obtained from the output of the countreg. The differing values are: Lambda (as mentioned above), P(y=0), P(y!=0) and Predicted (those three latter are probably due to the Lambda error) Any examples, tips, or resources you could share would be greatly appreciated. Thank you in advance for your assistance! Best regards, Piero

vijnad

Hi, I am trying to disable procedure title 'The SG Render Procedure' by using ODS NO PROCTITLE ; while generating .svg graph, this is not working, not sure where this is going wrong. can you please help. Thanks! Here is the output image and code: ods _all_ close; ods NOPROCTITLE ; ods html gpath="&figout" style=groupstyle image_dpi=300 ; filename image1 "&figout/%sysfunc(tranwrd(&tlfname,-,_)).svg" ; ods graphics on/ reset = index imagename="%sysfunc(tranwrd(&tlfname,-,_))" imagefmt=svg ; proc sgrender data=final template=swimmer; dynamic bartype="&bartype"; run; ods html close;