About gsnidow

gsnidow · ‎07-26-2013

Greetings. In a dataset I have a date variable that is (or should be) a date. It is in format 'YYYY-MM-DD', and when I look at the dataset properties for that column, the format is YYMMDD10. and the Informat is YYMMDD10. So, in my code, I want to put some conditions on the date... If Date_variable >= '2013-07-26'd Then... But I keep getting error: ERROR: Invalid date/time/datetime constant '2013-07-26'd. I have tried '2013-07-26'd, '20130726'd, '26JUL2013'd, '07/26/2013'd, and everything else I could think of, and I am still getting errors. I'm sure it something simple (and very frustrating), but I can't make it work. Can anyone help? Thank you. Greg

gsnidow · ‎05-15-2013

SASKiwi, thank you for the reply. I'm not concerned about the label, its just that is the only way I could get the column to be named as I want. My concern is that I need to use this dataset in some other stuff, and I would like to be able to reference the alias I named in the proc sql statement. It works not, so I'm not sure if it's a label or the column name is correct, but I can reference short_name in the dataset. Thanks.

gsnidow · ‎05-15-2013

This seemed to work somereallylongcolumnname as short_name labe='short_name'

gsnidow · ‎05-15-2013

Greetings all. I'm having some difficulty renaming a column in proc sql. Consider the following: proc sql; create table sometable as select '0' as group_num, somereallylongcolumnname as short_name from sourcetable; quit; When I run this code, the first column is correctly named, as it should have been based on some documentation I found on the web. However, the column 'somereallylongcolumnname' retains its name eventhough I used an alias. I read where the alias will be displayed on reports and such, but I need the column to be renamed in the dataset. I tried using (rename= at various places, but none seem to work. I'm sure it is something simple, but I just can't get it. Any ideas? Thank you. Greg

gsnidow · ‎02-27-2013

DOH! a thousand times. Yes, Reeza, you are correct. I got an error when I ran your code, telling me WORK.SAMPLE.DATA does not exist. So, what I was really looking at was the results from the last time I ran my faulty code. I just re-wrote the formula as you have it, and VOILA!. Thank you so, so much. I'm confused though, in why the intercept needs to be included in the formula when it does not matter. Again, thank you for the help, and I realize now I could have made it easier on you in the first place if I had just left Excel out of it, and done the entire thing in SAS. Greg

gsnidow · ‎02-27-2013

Reeza, I only typed in the estimates because I did not know how to get them directly. The problem I have is that I am unable to figure out the formula to calculate the probability given the estimates. I need to hand off a formula to be applied to data outside of SAS, on a mainframe. In the output, the 'myformula' in your code is still not matching the predicted probability ip_1. Did I miss something? Thank you so much for all of your good time and help. Greg

gsnidow · ‎02-27-2013

Reeza, I took your advice, and started over with a documented example I understood. I looked at the Neuralgia example 51.2 avalailable at http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_logistic_sect060.htm I started by altering the data a bit to match my scenario by changing 'M' and 'F' to 0 and 1, and also to change 'No' and 'Yes' to 0 and 1. Other than that, it pretty much seems the same as my situation, with the exeption that I have 3 binary class predictors, and 3 numeric predictors. I tried using the same forumula that got me close yesterday on my own data, and now I am not even coming close, as all of the results of my formula are .9999 when rounded. I have attached the sas code for the example, including my changegs, the proc logistic, and a final dataset with the current iteration of the formula I am trying to get to work. It just seems like I should be able to replicate the predicted probabilities given the estimates and intercept, but after trying everything I can think of, I just can't get it to work. I have referenced my stats book, and I don't see anything contrary to what you have suggested in the other post, and what I am trying. Thank you. Greg

gsnidow · ‎02-26-2013

I'm right clicking the dataset, then chosing export. It seems to be keeping the precision as is in the SAS dataset.

gsnidow · ‎02-26-2013

Reeza, thank you for the link. That is *exactly* what I am trying to do. I've got close, but can't seem to get it exactly right. I have my spread sheet set up just like the poster in the other post has done, with the MLE estimates populated above the variable names. the closest I could get to calculating the individual probability of any one observation, and this is attempting to use the forumula you suggested to the other poster... 1/(1 + EXP(-1*(var_1*estimate_1)+(var_2*estimate_2)+(varN*estimate_N))) The value calculated with this formula is slightly different (with an absolute difference less than .01) than the 'Individual Probability of event=1' value calculated by SAS. I'm also not sure where the intercept fits in. I found this document... http://support.sas.com/resources/papers/proceedings12/317-2012.pdf and it seems to imply the intercept need not be taking into consideration in some cases. Do you see anything wrong with my forumula? Again, thank you so much for your help. Greg

gsnidow · ‎02-26-2013

Reeza, thank you so much for continuing to answer my newbie questions. I ran my model with the output as you have shown above, and I am googling madly to find help in deciphering the results. In the mean time, perhaps an explanation of what I am needing to do would help. Using historical account data, with a binary response, two binary predictors, and several interval predictors, I need to come up with a formula to predict which accounts will ultimately not pay, and end up being sent to collections. The system housing the data is a DB2 mainframe, and the DB2 programmers need to have a formula to sort the bad accounts having the highest probability of ending up in collections. They have, in a DB2 table, all of the variables in my SAS model, but they need a formula to calculate the probability any account will end up in collections. So, while it helps me to see the predicted probability of each observation in my dataset, I am needing to be able to figure out how to calculate that probability by hand, so I can give the mainframers a formula. I hope this helps explain my dilema. Thank you again.

gsnidow · ‎02-26-2013

Ok, I am still missing something. I used / rsq lackfit, to see if my model even made sense, and indeed it is a good fit. I guess what I am missing is that SAS must be using some kind of formula to determine the probability of each observation having a response of either 0 or 1. I am still unsure how to do this. Do I need to look at the probability of each variable independently, the put them all together? Thank you. Greg

gsnidow · ‎02-25-2013

Reeza, thank you so, so much for that link. That is *exactly* what I need. Greg

gsnidow · ‎02-25-2013

Thank you Reeza. Unfortuneately, I do not have access to an in-house statustician, so I have been trying to figure it out on my own for several days now. Below is what I am trying. proc logistic data = credit.crnp_logreg; class bad_debt_at_conn dep_unpaid; model chg_off (event='1') = bad_debt_at_conn dep_unpaid cr_scor arrears qy_fc_c qy_fc_b qy_st_c qy_st_b / selection=none expb; quit; Would you be able to show me how to run it so it performs as you suggets, or to point me to a good reference? I have tried googling everything I can think of. Thank you. Greg

gsnidow · ‎02-25-2013

Greetings all. I am trying to create my regression forumula from the estimates output from proc logistic. Thinking back to multiple regression (and it was several years ago), I could simply take the intercept + (estimate1*variable1) + (extimateN*variableN). However, if I use this methodology, I seem to get some results that are counter intuitive. My question is, how does the 'Exp(Est)' affect the parameter estimate with respect to putting it in the regression forumula? I have attached a copy of my log, and sure would appreciate it if anyone would be willing to put some experienced eyes on it. For example, variable 'bad_debt_at_connection' indicates a customer opened a new account when they had an account that at some point in the past went to a collection agency for non-payment. If I am trying to predict accounts that will go to collections, it seems to me if this condition were true for any customer, they would be more likely to go to collections if they have already done so in the past. However, the parameter estimate of -0.1582 seems to me to indicate this is not the case. The two class variables are binary, having either a 1 or 0. The rest of the variables are numeric, and should be treated as such. Thank you. Greg.

gsnidow · ‎02-21-2013

Thank you both so much for the help, I was struggling to understand what was going on. Greg

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