I'm a new SAS user and I finally managed to impute a 110 mixed variable dataset using proc mi using the code below:   proc mi data=F0_Data NIMPUTE=50 out=F0_Imputed_Data seed=54321; class Q4_diseaseF0 Q5_MSD_SymptomsF0 Q6a1F0 Q6b1F0 Q6c1F0 Q6d1F0 Q6e1F0 Q6f1F0 Q7a1F0 Q7b1F0 Q7c1F0 Q7d1F0 Q7e1F0 Q7f1F0 Q7g1F0 ... ; var Q3_BMI_CategoryF0 Q4_diseaseF0 Q5_MSD_SymptomsF0 Q6a1F0 Q6b1F0 Q6c1F0 Q6d1F0 Q6e1F0 Q6f1F0 Q7a1F0 Q7b1F0 Q7c1F0 Q7d1F0 Q7e1F0 Q7f1F0 Q7g1F0 ... ; /* up to 110 variables */ /* Use FCS for mixed types */ /* Use linear regression for continuous variables */ fcs reg(Q3_BMI_CategoryF0 Q10_raw_sleep_scoreF0 Q11_13_sleep_deficitF0 Q16bF0 Q16cF0 Q19_CESDF0 Q20_WRFQF0 ...) /* Use logistic regression for binary variables*/ logistic(Q4_diseaseF0 Q7a1F0 Q7b1F0 Q7c1F0 Q7d1F0 Q7e1F0 Q7f1F0 Q7g1F0 ... /details LIKELIHOOD=AUGMENT link=logit descending) /* Use logistic regression for ordinal variables*/ logistic(Q5_MSD_SymptomsF0 Q6a1F0 Q6b1F0 Q6c1F0 Q6d1F0 Q6e1F0 ... /details LIKELIHOOD=AUGMENT link=logit descending) /* Use discriminat analysis for nominal variables*/ discrim(Q16aF0 Q16dF0 Q24_Job_title_codeF0 Q26_SHIFTCAT_F0 ... /CLASSEFFECTS=INCLUDE); run; 1) I need to pick the best /most optimum imputed dataset from the 50 generated using Proc MiAnalyze (and other procedures?), but I have no clue how to properly use it in my mixed variables case. BTW, as explained earlier, I have 110 mixed variables from over 400 survey respondents with no missing values at this point in time. Proc MiAnalyze SAS examples are not helpful.   2) Secondly, many of the imputed values for non-continuous variables are floating point, which doesn't make sense. How do I round up or down  while leaving the continuous values as is. It seems the "round=0.1" option for example, applies to all variables, which is not desirable.   Thanks, David   ... View more

Hi all ... I am performing an ordinal factor regression (Ordinal outcome with Factor predictors/independent variables). In my field, we consider salient thresholds between variable and factor to be |0.4| and above. So, Factor1 = a*Var1 + b*Var2 + ... + z*VarN, where a, b ... z are coefficients [ranging from -1.0 to +1.0] relating that particular variable to Factor1.   I want to perform a regression for coefficients of |0.4| and above for that particular factor. I'm hoping to set those coefficients under |0.4| to zero and then perform the regression with these modified factors.    1) Is there a way to do this? Can I have access to the Factor1 coefficient array of coefficient values that could be modified in SAS or manually?  2) Will these modified factors produce better regression parameter estimates, tighter confidence intervals and reliable p-values?   Thx! David ... View more

I am performing  simple Pearson, Spearman and Kendall correlations between many variables using PROC CORR.   1.  I need to suppress non-significant correlations (p > 0.05) in my matrix, leaving only significant correlations (p <0.05).  2. I need to show confidence intervals for these correlations.   How can I do this? Thanks! ... View more