9.4   Probably original release. ... View more

Original Plot   ... View more

Freelance,   You provided a great reply. In particular,  I appreciated how the c-index gets calculated. I also, liked the reference of how  the beta coefficients equaled the values in the classification table (which I had not checked). Lastly, your post was also very aethetically pleasing.   I guess for my quandary related to reporting the SEN and SPEC, but feeling if if I report the c-index from logistic - readers will be like me and say "hey, that is not the same value I can calculate for accuracy". I am looking for some insight in moving forward. I was planning to plot the accuracy values for the different cutoffs for the predictor (original continuous variable that I had created a threshold for in this example). The graph would look much like the ones you can see in linear models where they plot MSE changes for different modeling techniques.   I was also going to incorporate CI bands on the graph, which I can get from the logistic model, and via bootstrapping in the hand calculations. I guess regardless of how I progress, is there a proper use of terminology that I am missing. So I should use accuracy  if I do it by hand and use concordance index or AUC if I use the logistic model?  The logistic model seems like the better approach for the future, in that is provides an opportunigy to control for covariates in the model, if desired.   Any suggestions on  how to word the presentation of these data would be appreciated. I guess I was confused by how the SEN/(1-SPEC) is call accuracy and people also call the c-statistic the model accuracy.   THANKS! ... View more

Yes, please add more. I appreciate all feedback. Yeah, I was trying to recall the concordance index, I remembered you grabbed one of each, but did not recall the membership component. The slight predicament I had found myself in was just the reporting of the results, since I report SEN and SPEC, which didn't jive with the model. I guess what I was thinking, was you always hear the AUC and C-statistic are the same. However the AUC is based on the SEN and SPEC, but as you elucidated the c-index is not, I guess it comes back to the scoring of variables from the ML model. I was also trying to remember, how you get at the final value of the c-index. Is it the accuracy after comparing every possible combination of the 0s and 1s probability values? So the 1s should have a probability greater than the Os 58% of the time, given the logistic model? Yes, I saw the basic simulation code I used botch the n-value, but I was looking past it since I calculated the hand-version and logistic, with just the 127 observations. So they were both corrumpted!! Thanks. ... View more

I don't believe it will be that easy. I provided an example, which if ran, you can see both have n=127. Also, I would imagine that it would take a sufficient amount of missing data to shift accuracy down by > 10%. ... View more

Alright, I just posted my own question - so I will pay it forward by tryig to tackle this one.   proc surveylogistic data=mydata varmethod=jk; by _imputation_; class X1 (ref='0') /param=ref;; model Y (event='1') = X1; ods output parameterestimates=ORs; run; proc mianalyze parms(classvar=classval)=ORs; class X1; modeleffects intercept X1; run;   I hope this helps, I just grabbed this old piece of code from when I ran a comparable question. ... View more

Thank you Freelance and Rick!   Yes, my first approach was to simulate these data when I did not see the chi^2 option. I then better educated myself, and realized the chi^2 was a specal case of the gamma distribution. This seemed familiar, but I was a little mistrusting of going forward without confirmation.   Funny enough Rick, I currently had the simulation book open to page 39 when writing this reply. I had seen the code you mentioned, but was falsely discourage when seeing how long it was. I know, I want flexibility of writing code, but also point and click options at times. To put this in perspective, I am actually using this as a comparative for generated Mahalanobis squared distances - in the pursuit of examining for outlers. I had seen your Do Loop piece on this and it helped  in my understanding and I was hoping to use it as a complement to the chi^2 Quantile plots.    Question, are the robust Mahalanobis distances more appropriate for data that may be questionably multivariate normal?     ... View more

Yes, I saw this in a couple of places - including Wicklin's simulation book, but needed more confirmation to feel totally confortable. So I used the below, since I had 2 degrees of freedom on the chi^2 distribution. Please let me know if this seems incorrect PROC UNIVARIATE DATA=plotdata; VAR x; HISTOGRAM x / gamma(alpha=1 sigma=2); RUN;     ... View more

I am now using the following, which works out better for my needs (you can see I am looking at 4 variable sums):   proc means data=summies; class replicate; var A B C D; output out=LR_test sum(A)= A_sum sum(B)= B_sum sum(C)= C_sum sum(D)= D_sum; run;   However, the generated dataset has an extra row for the totals. So it has a replicate  = "." with the totals. Plus the 100 other rows with the sums. Is there a way to get rid of this extra within the above data step? ... View more

I am using the following to sum two different variables (i.e., A and C):   proc sql; create table B as select *, sum(A)as Sigma_A, sum(C)as Sigma_C from meta; drop D; quit;   But now I would like to sum them based on a group variable in the dataset called replicate. There are 100 replicate groups (i.e., 1-100) all with 300 observations. I would like to execute the about code but have the sums be for the replicates, and inserted into the new dataset as before.   Any help would be appreciated - I am currently having difficulties getting the "group by" to work with the multiple sums in the step. ... View more