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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;
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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!
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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.
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