Hello, I want to run a multinomial logistic regression for sample survey data. 1. should I use proc surveylogistic statement for the multinomial logistic regression? what is the difference between binary logistic regression and multinomial logistic regression when it comes to the code. 2. How to do the diagnostics? Should I using Pearson residuals?   Thank you! ... View more

Hello, I want to run a multinomial logistic regression for sample survey data. The code is below.   I have two questions: 1. is this the right code for the multinomial logistic regression? 2. How to do the diagnostics? Below is my code using Pearson residuals, but I  am not sure it is right or not. (In addition, Does proc surveylogistic statement have goodness of fit test option? I did not find it, but please correct me if i am wrong ) Do I need to do any other diagnostic tests?   Thank you!     proc surveylogistic data=nh.diseases nomcar; class cluster age gender PIR SDDSRVYR race/ param=glm;     strata SDMVSTRA;     cluster SDMVPSU;     weight glucwt4yr; domain eligible; model BMI_outcome= age gender PIR SDDSRVYR race  EIEER totalcounts cluster/vadjust=none link=glogit RSQUARE; output out=res_o predicted=prob_o; /*output the predicted probability*/ run;   proc sql; create table res2_o as select prob_o, BMI_outcome, glucwt4yr from res_o where prob_o is not missing; run;   data res3_o; set res2_o; if BMI_outcome=1 then y_o = 1;  *** y=1 means that participants are obesity ; else if BMI_outcome=2 then y_o = 2;  *** y=2 means that participants are overweight ; else y_o=0;  *** y=2 means that participants are normal weight ; pearson_o = sqrt(glucwt4yr)*(y_o-prob_o)/sqrt(prob_o*(1-prob_o)); /*by formula*/ run;   /* QQ plot */ proc univariate data=res3_o normal; var pearson_o; qqplot pearson_o/normal(mu=est sigma=est); run; ... View more

Hello, When we try to use linear regression, we need to do the diagnostics first. So I wonder when I use "proc surveyreg" to investigate the linear regression, should I use "proc reg" statement to do the diagnostics test (residual plot ...) or should I use "proc surveyreg" statement to do the diagnostics? If I should use "proc surveyreg" statement, how can I get the residual plot?   Thank you   ... View more

Hello.   I want to get a table as follows: (this is the proc means results)   however, when I used proc surveymeans, code are below proc surveymeans data=nh.outcomes; STRATA sdmvstra; CLUSTER sdmvpsu; var BMXBMI; CLASS alpha16; WEIGHT glucwt4yr; domain eligible; run; I only get the table like this:   could you please tell what went wrong?   Thank you!   ... View more

Hello,   I run a logistic regression, the code is below.  proc surveylogistic data=nh.ob_diet1836 nomcar; class t240 age RIAGENDR PIR SDDSRVYR RIDRETH1/ param=glm; strata SDMVSTRA; cluster SDMVPSU; weight glucwt4yr; domain eligible; model BMI_outcome= age RIAGENDR PIR SDDSRVYR EIEER RIDRETH1 t240/ vadjust=none df=none link=glogit; lsmeans t240/ cl oddsratio adjust =tukey; run;   after running this code, the results automatically have information of AIC:  but I think it is too large, so I used PROC mixed to test.   code is below: proc mixed data=nh.ob_diet1836 covtest noclprint; CLASS t240 age RIAGENDR PIR SDDSRVYR RIDRETH1; WEIGHT glucwt4yr; model BMI_outcome= age RIAGENDR PIR SDDSRVYR EIEER RIDRETH1 t240; random intercept; run;   and AIC results is:   The difference between two AIC is huge. I wonder which one is right or how can I get the accurate results.   One thing I want to mention is that when I run PROC MIXED, the log showed that " 18634 observations are not included because of missing values. NOTE: Convergence criteria met but final Hessian is not positive definite. NOTE: Estimated G matrix is not positive definite." and those did not show when I run PROC SURVEYLOGISTIC statement.   Thank you for helping me! ... View more

Hello, I want to know how to get r-square in the logistic regression. My code is below.   Thank you!   proc surveylogistic data=nh.diseases_pa1836_1 nomcar; class t180 age RIAGENDR PIR SDDSRVYR RIDRETH1/ param=glm;     strata SDMVSTRA;     cluster SDMVPSU;     weight glucwt4yr; domain eligible; model BMI_outcome= age RIAGENDR PIR SDDSRVYR   RIDRETH1 totalcounts t180/ vadjust=none df=none link=glogit; lsmeans t180/ cl oddsratio adjust =tukey; run;   ... View more