Hi All,   I need to create a interactive decision tree in SAS eminer with raw dataset(no train and validation split). However I am only getting top 4-5 variables for first node split. Is there a way I can get atleast 10 variables for first split.   Thanks ... View more

Hi All,   I want to export a SAS dataset of sixe 30MB approx. The challenge is the client cannot access the location where the excel are saved. They need to open the excel as soon as the SAS dataset is exported and save to their local system. What SAS function or procedure can be used to export the large dataset and then open the excel directly.   Kindly help with the issue.     Thanks, Satyakshma Rawat   ... View more

Hi, I have Account_ID of length $36. I am trying to convert this to numeric variable but it truncates the last digits or converts the last digit to 0000. Kindly help with the issue. ... View more

Hello All I'm trying to replicate in SAS a procedure I use in Excel. This procedure is basically an iterative solver (through a macro) that puts the variable Delta (Sum square error of actual and predicted value) to minimum value by changing the values of the Index value(starting with null values) used to calculate predict value.   The input data start like this: bucket score actual predicted delta index 1 -2.38219 0.011488 . . . 2 -0.64283 0.323792 . . . 3 -0.19096 0.5008 . . . 4 0.379314 0.693121 . . .   - bucket, score and actual are inputs; - index values are generated by Excel; Used in calculating predicted which is further used to find the delta - predicted is given by NORM.S.DIST(score + index, TRUE) - Delta is minimum sum square error (actual, predicted)   The solver objective function is Delta, it operates by changing index values and the constraint is predicted value should be such that minimum delta value. It runs for each cell.   After solver macro, the desired output is:   bucket score actual predicted delta index 1 -2.38219 0.011488 0.013501 0.000359105 0.170699 2 -0.64283 0.323792 0.318416 0.000359105 0.170699 3 -0.19096 0.5008 0.491916 0.000359105 0.170699 4 0.379314 0.693121 0.708845 0.000359105 0.170699   Is there a way to do this in SAS except  for proc optmodel as I don't have license for SAS/OR?    I have been trying and searching for a few days, without success.   If anyone could help with the code for this, it would be very much helpful and appreciated.   Kind Regards, Satyakshma Rawat ... View more

Hello Everyone,   I have to calculate the predicted values for year 2015, 2016 and 2017 for Bucket1, Bucket2, Bucket3 and Bucket4 using a Z_score and an Index value. I already have Z_Score from other table, but I need to generate an Index value (which is a random value). The Index value to be generated should be such that that when we calculate predicted PD using it for each year. The sum square errors of Predicted PDs and Actual PDs should be minimum. I am able to do this in Excel using Run solver function, and I need to do this using PROC FCMP in BASE SAS in SAS EG as I don't have SAS/IML or SAS/OR COMPONENT. Please help me with this code as I am new to PROC FCMP. Attached is the Input table and desired output table: Table 1: Using Z Score from this   Col1 Value 1 Z value 1 0.86% -2.38 2 26.02% -0.64 3 42.43% -0.19 4 64.78% 0.38   Table 2: Bucket-wise Actual values for each year   Col1 2015 2016 2017 2018 1 1.15% 0.66% 0.51% 1.13% 2 32.38% 22.38% 18.04% 31.26% 3 50.08% 37.32% 38.41% 43.91% 4 69.31% 59.29% 57.13% 73.38% 5 99.72% 100.00% 100.00% 100.00%   Table 3: Index values are random values generated, and used in calculating predicted values in bold   Index value 0.100000 0.170699 -0.133416 -0.178382 0.135047 Col1   2015 2016 2017 2018 1   1.35% 0.59% 0.52% 1.23% 2   31.84% 21.88% 20.58% 30.58% 3   49.19% 37.28% 35.59% 47.77% 4   70.88% 59.71% 57.96% 69.65%   Table 4: Min sum of squares (between actual values and predicted values) value and Index value.     Sum of Squares Index   0.000   2015 0.000 0.17 2016 0.000 -0.13 2017 0.002 -0.18 2018 0.003 0.14     I have written the below code using proc fcmp for the above problem, but I am not able to create the right code for this:   PROC FCMP OUTLIB=work.functions.ci; function predict(currentValue); return(CDF('NORMAL', currentValue + index)); endsub; function sum_square_error(value1,value2); diff = (value1-value2); return(diff*diff); endsub; function minimum(y,sse,index,value1,value2); return(solve("sum_square_error",.,y,min(sse)); endsub; QUIT; options cmplib=( work.functions); data test; set test; predicted = cred_index(Z_score); err = sum_square_error(actual,predicted); run;   Please help with this problem. ... View more