10-18-2019
sivakoya
Obsidian | Level 7
Member since
01-26-2015
- 40 Posts
- 18 Likes Given
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Latest posts by sivakoya
Subject Views Posted 1042 10-17-2019 03:34 PM 1940 09-03-2019 04:09 PM 2040 08-28-2019 04:20 PM 1510 02-13-2019 10:13 AM 1525 02-13-2019 08:07 AM 1526 02-13-2019 08:04 AM 1575 02-12-2019 02:03 PM 2907 09-10-2018 01:33 PM 2921 09-10-2018 11:23 AM 2933 09-10-2018 11:03 AM -
Activity Feed for sivakoya
- Posted Loop sum statement in PROC SQL on New SAS User. 10-17-2019 03:34 PM
- Posted Re: FORECAST month end balance using MULTIPLE VARIABLES on SAS Forecasting and Econometrics. 09-03-2019 04:09 PM
- Liked Re: FORECAST month end balance using MULTIPLE VARIABLES for dw_sas. 09-03-2019 04:09 PM
- Posted FORECAST month end balance using MULTIPLE VARIABLES on SAS Forecasting and Econometrics. 08-28-2019 04:20 PM
- Posted Re: Remove matching credit and debit entries in a dataset on New SAS User. 02-13-2019 10:13 AM
- Posted Re: Remove matching credit and debit entries in a dataset on New SAS User. 02-13-2019 08:07 AM
- Posted Re: Remove matching credit and debit entries in a dataset on New SAS User. 02-13-2019 08:04 AM
- Posted Remove matching credit and debit entries in a dataset on New SAS User. 02-12-2019 02:03 PM
- Posted Re: Vintage analysis balance forecast HELP! on SAS Forecasting and Econometrics. 09-10-2018 01:33 PM
- Posted Re: Vintage analysis balance forecast HELP! on SAS Forecasting and Econometrics. 09-10-2018 11:23 AM
- Posted Vintage analysis balance forecast HELP! on SAS Forecasting and Econometrics. 09-10-2018 11:03 AM
- Posted Compare values across multiple variables and flag duplicates on SAS Programming. 07-14-2017 01:13 PM
- Liked Re: Modeling Counts data using PROC genmod + poisson distribution_results interpretation for JacobSimonsen. 06-30-2017 10:02 AM
- Posted Re: Modeling Counts data using PROC genmod + poisson distribution_results interpretation on Statistical Procedures. 06-30-2017 09:27 AM
- Liked Re: Modeling Counts data using PROC genmod + poisson distribution_results interpretation for Rick_SAS. 06-30-2017 09:17 AM
- Posted Re: Modeling Counts data using PROC genmod + poisson distribution_results interpretation on Statistical Procedures. 06-30-2017 08:57 AM
- Liked Re: Modeling Counts data using PROC genmod + poisson distribution_results interpretation for JacobSimonsen. 06-30-2017 08:45 AM
- Posted Re: Modeling Counts data using PROC genmod + poisson distribution_results interpretation on Statistical Procedures. 06-29-2017 02:21 PM
- Posted Modeling Counts data using PROC genmod + poisson distribution_results interpretation on Statistical Procedures. 06-28-2017 11:33 AM
- Posted Re: Testing association between frequency of application grade and time of the day in hourly dataset on Statistical Procedures. 06-27-2017 02:07 PM
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Posts I Liked
Subject Likes Author Latest Post 1 3 1 3 1
10-17-2019
03:34 PM
proc sql; create table Vintage_Summary_final_payoff as select *, sum('201301_sur'n) as '201301_sur_Payoff'n, sum('201302_sur'n) as '201302_sur_Payoff'n, sum('201303_sur'n) as '201303_sur_Payoff'n, sum('201304_sur'n) as '201304_sur_Payoff'n, sum('201305_sur'n) as '201305_sur_Payoff'n, sum('201306_sur'n) as '201306_sur_Payoff'n, sum('201307_sur'n) as '201307_sur_Payoff'n, sum('201308_sur'n) as '201308_sur_Payoff'n, sum('201309_sur'n) as '201309_sur_Payoff'n, sum('201310_sur'n) as '201310_sur_Payoff'n, sum('201311_sur'n) as '201311_sur_Payoff'n, sum('201312_sur'n) as '201312_sur_Payoff'n, sum('201401_sur'n) as '201401_sur_Payoff'n, sum('201402_sur'n) as '201402_sur_Payoff'n, sum('201403_sur'n) as '201403_sur_Payoff'n, sum('201404_sur'n) as '201404_sur_Payoff'n, sum('201405_sur'n) as '201405_sur_Payoff'n, sum('201406_sur'n) as '201406_sur_Payoff'n, sum('201407_sur'n) as '201407_sur_Payoff'n, sum('201408_sur'n) as '201408_sur_Payoff'n, sum('201409_sur'n) as '201409_sur_Payoff'n, sum('201410_sur'n) as '201410_sur_Payoff'n, sum('201411_sur'n) as '201411_sur_Payoff'n, sum('201412_sur'n) as '201412_sur_Payoff'n, sum('201501_sur'n) as '201501_sur_Payoff'n, sum('201502_sur'n) as '201502_sur_Payoff'n, sum('201503_sur'n) as '201503_sur_Payoff'n, sum('201504_sur'n) as '201504_sur_Payoff'n, sum('201505_sur'n) as '201505_sur_Payoff'n, sum('201506_sur'n) as '201506_sur_Payoff'n, sum('201507_sur'n) as '201507_sur_Payoff'n, sum('201508_sur'n) as '201508_sur_Payoff'n, sum('201509_sur'n) as '201509_sur_Payoff'n, sum('201510_sur'n) as '201510_sur_Payoff'n, sum('201511_sur'n) as '201511_sur_Payoff'n, sum('201512_sur'n) as '201512_sur_Payoff'n, sum('201601_sur'n) as '201601_sur_Payoff'n, sum('201602_sur'n) as '201602_sur_Payoff'n, sum('201603_sur'n) as '201603_sur_Payoff'n, sum('201604_sur'n) as '201604_sur_Payoff'n, sum('201605_sur'n) as '201605_sur_Payoff'n, sum('201606_sur'n) as '201606_sur_Payoff'n, sum('201607_sur'n) as '201607_sur_Payoff'n, sum('201608_sur'n) as '201608_sur_Payoff'n, sum('201609_sur'n) as '201609_sur_Payoff'n, sum('201610_sur'n) as '201610_sur_Payoff'n, sum('201611_sur'n) as '201611_sur_Payoff'n, sum('201612_sur'n) as '201612_sur_Payoff'n, sum('201701_sur'n) as '201701_sur_Payoff'n, sum('201702_sur'n) as '201702_sur_Payoff'n, sum('201703_sur'n) as '201703_sur_Payoff'n, sum('201704_sur'n) as '201704_sur_Payoff'n, sum('201705_sur'n) as '201705_sur_Payoff'n, sum('201706_sur'n) as '201706_sur_Payoff'n, sum('201707_sur'n) as '201707_sur_Payoff'n, sum('201708_sur'n) as '201708_sur_Payoff'n, sum('201709_sur'n) as '201709_sur_Payoff'n, sum('201710_sur'n) as '201710_sur_Payoff'n, sum('201711_sur'n) as '201711_sur_Payoff'n, sum('201712_sur'n) as '201712_sur_Payoff'n, sum('201801_sur'n) as '201801_sur_Payoff'n, sum('201802_sur'n) as '201802_sur_Payoff'n, sum('201803_sur'n) as '201803_sur_Payoff'n, sum('201804_sur'n) as '201804_sur_Payoff'n, sum('201805_sur'n) as '201805_sur_Payoff'n, sum('201806_sur'n) as '201806_sur_Payoff'n, sum('201807_sur'n) as '201807_sur_Payoff'n, sum('201808_sur'n) as '201808_sur_Payoff'n, sum('201809_sur'n) as '201809_sur_Payoff'n, sum('201810_sur'n) as '201810_sur_Payoff'n, sum('201811_sur'n) as '201811_sur_Payoff'n, sum('201812_sur'n) as '201812_sur_Payoff'n, sum('201901_sur'n) as '201901_sur_Payoff'n, sum('201902_sur'n) as '201902_sur_Payoff'n, sum('201903_sur'n) as '201903_sur_Payoff'n, sum('201904_sur'n) as '201904_sur_Payoff'n, sum('201905_sur'n) as '201905_sur_Payoff'n, sum('201906_sur'n) as '201906_sur_Payoff'n, sum('201907_sur'n) as '201907_sur_Payoff'n, sum('201908_sur'n) as '201908_sur_Payoff'n, sum('201909_sur'n) as '201909_sur_Payoff'n from Vintage_Summary_final; quit; Need help automating above block of PROC sql if there is a way to loop through variables that end with '_sur' in Vintage_Summary_final data set
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09-03-2019
04:09 PM
Thanks
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08-28-2019
04:20 PM
I am trying to forecast month end balances of the TOTAL portfolio using PROC ARIMA procedure. INside the portfolio there are 3 major product segments A, B and C. Each of which has a separate trend and seasonality as shown below in the graph I wanted to try two methods, method 1: Forecast of the TOTAL portfolio balance using PROC ARIMA (univariate) method 2: Forecast of the TOTAL portfolio balance using PROC ARIMA (multi variate) using segment A, B and C BALANCES as input variables Need help in forecasting the portoflio balance with multiple input variables, like average term , average interest rate, prepayments etc.. Which procedure can I use to achieve this? Method 1 works fine but with method 2 I am getting these warnings More values of input variable A are needed More values of input variable B are needed More values of input variable C are needed METHOD 1: proc arima data=upb_2 plots=all; identify var=UPB(1,12) noprint; estimate noconstant method=ml; forecast lead=17 interval=month id=RPT_PRD_DATE out=outf; run; METHOD 2: proc arima data=UPB_INDUSTRY_2 plots=all; identify var=TOTAL(1,12) noprint crosscorr=(A B C); estimate input=(A B C) noconstant method=ml; forecast lead=17 interval=month id=RPT_PRD_DATE_2 out=outf_input; run;
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02-13-2019
10:13 AM
yes, that transaction should stay. error from my end
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02-13-2019
08:07 AM
There is transaction code. 600 is for normal transaction and 608 is for reversal. if that's what you meant by another identifier HAVE: LOAN_NBR INT_AMT_PAID PRIN_AMT_PAID EFF_FROM_DT TRAN_CODE 60016100 20.58 -98.50 07Jan2019 600 60016101 29.72 -80.28 07Jan2019 600 60016101 15.19 -94.81 28Jan2019 600 60016102 52.70 -57.35 16Jan2019 600 60016102 0.00 -110.05 16Jan2019 600 60016103 121.08 -235.92 04Jan2019 600 60016122 41.29 -89.06 17Jan2019 600 60016122 -41.29 89.06 24Jan2019 608 60016122 57.27 -73.08 29Jan2019 600 60016212 29.24 -73.85 09Jan2019 600 60016212 -29.24 73.85 17Jan2019 608 60016274 142.96 -186.55 11Jan2019 600 60016274 -142.96 186.55 21Jan2019 608 60016326 45.99 -96.08 03Jan2019 600 60016326 -45.99 96.08 08Jan2019 608 60016326 57.48 -84.59 10Jan2019 600 60016468 61.45 -113.64 31Dec2018 600 60016468 -61.45 113.64 31Dec2018 608 60016501 17.69 -80.70 15Jan2019 600 60016501 18.76 -79.63 17Jan2019 600 60016501 -17.69 80.70 17Jan2019 608 60016701 18.76 -81.24 08Jan2019 600 60016701 -18.76 81.24 14Jan2019 608 60016701 26.48 -73.52 22Jan2019 600 60016701 3.75 -96.25 29Jan2019 600 60016760 24.25 -102.86 31Dec2018 600 60016760 5.15 -121.96 07Jan2019 600 60016760 -5.15 121.96 14Jan2019 608 60016760 13.24 -113.87 18Jan2019 600 60016848 32.63 -77.37 21Dec2018 600 60016848 50.90 -55.10 04Jan2019 600 60016848 -32.63 77.37 04Jan2019 608 60016848 18.04 -87.57 18Jan2019 600 WANT: LOAN_NBR INT_AMT_PAID PRIN_AMT_PAID EFF_FROM_DT TRAN_CODE 60016100 20.58 -98.50 07Jan2019 600 60016101 29.72 -80.28 07Jan2019 600 60016101 15.19 -94.81 28Jan2019 600 60016102 52.70 -57.35 16Jan2019 600 60016102 0.00 -110.05 16Jan2019 600 60016103 121.08 -235.92 04Jan2019 600 60016122 41.29 -89.06 17Jan2019 600 60016122 -41.29 89.06 24Jan2019 608 60016122 57.27 -73.08 29Jan2019 600 60016212 29.24 -73.85 09Jan2019 600 60016212 -29.24 73.85 17Jan2019 608 60016274 142.96 -186.55 11Jan2019 600 60016274 -142.96 186.55 21Jan2019 608 60016326 45.99 -96.08 03Jan2019 600 60016326 -45.99 96.08 08Jan2019 608 60016326 57.48 -84.59 10Jan2019 600 60016468 61.45 -113.64 31Dec2018 600 60016468 -61.45 113.64 31Dec2018 608 60016501 17.69 -80.70 15Jan2019 600 60016501 18.76 -79.63 17Jan2019 600 60016501 -17.69 80.70 17Jan2019 608 60016701 18.76 -81.24 08Jan2019 600 60016701 -18.76 81.24 14Jan2019 608 60016701 26 -73.52 43487.00 600 60016701 4 -96.25 43494.00 600 60016760 24 -102.86 43465.00 600 60016760 5.15 -121.96 07Jan2019 600 60016760 -5.15 121.96 14Jan2019 608 60016760 13.24 -113.87 18Jan2019 600 60016848 32.63 -77.37 21Dec2018 600 60016848 50.90 -55.10 04Jan2019 600 60016848 -32.63 77.37 04Jan2019 608 60016848 18.04 -87.57 18Jan2019 600
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02-13-2019
08:04 AM
@novinosrin not all transactions will have reversals
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02-12-2019
02:03 PM
I need to remove matching credit and debit entries from a dataset what is the easiest way to do this? HAVE: LOAN_NBR INT_AMT_PAID PRIN_AMT_PAID EFF_FROM_DT 60016100 20.58 -98.50 07Jan2019 60016101 29.72 -80.28 07Jan2019 60016101 15.19 -94.81 28Jan2019 60016102 52.70 -57.35 16Jan2019 60016102 0.00 -110.05 16Jan2019 60016103 121.08 -235.92 04Jan2019 60016122 41.29 -89.06 17Jan2019 60016122 -41.29 89.06 24Jan2019 60016122 57.27 -73.08 29Jan2019 60016212 29.24 -73.85 09Jan2019 60016212 -29.24 73.85 17Jan2019 60016274 142.96 -186.55 11Jan2019 60016274 -142.96 186.55 21Jan2019 60016326 45.99 -96.08 03Jan2019 60016326 -45.99 96.08 08Jan2019 60016326 57.48 -84.59 10Jan2019 60016468 61.45 -113.64 31Dec2018 60016468 -61.45 113.64 31Dec2018 60016501 17.69 -80.70 15Jan2019 60016501 18.76 -79.63 17Jan2019 60016501 -17.69 80.70 17Jan2019 60016701 18.76 -81.24 08Jan2019 60016701 -18.76 81.24 14Jan2019 60016701 26.48 -73.52 22Jan2019 60016701 3.75 -96.25 29Jan2019 60016760 24.25 -102.86 31Dec2018 60016760 5.15 -121.96 07Jan2019 60016760 -5.15 121.96 14Jan2019 60016760 13.24 -113.87 18Jan2019 60016848 32.63 -77.37 21Dec2018 60016848 50.90 -55.10 04Jan2019 60016848 -32.63 77.37 04Jan2019 60016848 18.04 -87.57 18Jan2019 WANT: LOAN_NBR INT_AMT_PAID PRIN_AMT_PAID EFF_FROM_DT 60016100 20.58 -98.50 07Jan2019 60016101 29.72 -80.28 07Jan2019 60016101 15.19 -94.81 28Jan2019 60016102 52.70 -57.35 16Jan2019 60016102 0.00 -110.05 16Jan2019 60016103 121.08 -235.92 04Jan2019 60016122 41.29 -89.06 17Jan2019 60016122 -41.29 89.06 24Jan2019 60016122 57.27 -73.08 29Jan2019 60016212 29.24 -73.85 09Jan2019 60016212 -29.24 73.85 17Jan2019 60016274 142.96 -186.55 11Jan2019 60016274 -142.96 186.55 21Jan2019 60016326 45.99 -96.08 03Jan2019 60016326 -45.99 96.08 08Jan2019 60016326 57.48 -84.59 10Jan2019 60016468 61.45 -113.64 31Dec2018 60016468 -61.45 113.64 31Dec2018 60016501 17.69 -80.70 15Jan2019 60016501 18.76 -79.63 17Jan2019 60016501 -17.69 80.70 17Jan2019 60016701 18.76 -81.24 08Jan2019 60016701 -18.76 81.24 14Jan2019 60016701 26 -73.52 43487.00 60016701 4 -96.25 43494.00 60016760 24 -102.86 43465.00 60016760 5.15 -121.96 07Jan2019 60016760 -5.15 121.96 14Jan2019 60016760 13.24 -113.87 18Jan2019 60016848 32.63 -77.37 21Dec2018 60016848 50.90 -55.10 04Jan2019 60016848 -32.63 77.37 04Jan2019 60016848 18.04 -87.57 18Jan2019
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09-10-2018
01:33 PM
sure, thanks!
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09-10-2018
11:23 AM
I do have SAS/ETS license. is there any particular procedure that suits this requirement?
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09-10-2018
11:03 AM
Attached data has balance at the end of each month for different Vintages in the portfolio. I need to forecast the balance for each Vintage at the end of 2019. Is there any time series forecast procedure I could use to solve this problem? Period Months 2010Vintage Months 2011Vintage Months 2012Vintage Months 2013Vintage Months 2014Vintage Months 2015Vintage Months 2016Vintage Months 2017Vintage Months 2018Vintage 20100131 0 $14,816,094.47 20100228 1 $36,073,822.81 20100331 2 $88,085,723.58 20100430 3 $166,044,748.58 20100531 4 $233,364,277.08 20100630 5 $306,311,958.90 20100731 6 $363,381,329.39 20100831 7 $411,055,725.29 20100930 8 $450,345,979.39 20101031 9 $486,305,056.45 20101130 10 $521,087,617.36 20101231 11 $553,758,985.43 20110131 12 $533,557,999.31 0 $37,127,481.90 20110228 13 $512,169,116.43 1 $87,465,609.58 20110331 14 $487,615,080.00 2 $189,230,144.11 20110430 15 $468,013,614.86 3 $324,981,287.31 20110531 16 $448,184,546.28 4 $450,679,702.76 20110630 17 $429,654,785.81 5 $554,236,748.35 20110731 18 $413,207,051.26 6 $629,886,339.85 20110831 19 $395,690,799.44 7 $714,247,404.51 20110930 20 $379,256,133.08 8 $788,282,125.87 20111031 21 $363,235,500.00 9 $843,586,030.48 20111130 22 $348,482,189.28 10 $902,364,141.74 20111231 23 $334,235,745.73 11 $956,817,529.96 20120131 24 $319,573,546.61 12 $921,874,086.73 0 $64,586,196.68 20120229 25 $361,910,653.25 13 $886,452,711.23 1 $152,581,132.28 20120331 26 $342,380,725.50 14 $846,206,649.63 2 $343,993,453.64 20120430 27 $324,353,357.48 15 $809,590,716.71 3 $563,868,148.92 20120531 28 $307,557,959.77 16 $774,992,546.56 4 $754,594,235.56 20120630 29 $291,673,275.10 17 $743,132,329.27 5 $907,157,346.00 20120731 30 $276,258,757.62 18 $712,279,477.92 6 $1,018,046,010.82 20120831 31 $261,314,809.87 19 $680,905,577.45 7 $1,140,724,744.03 20120930 32 $248,091,764.53 20 $653,905,368.65 8 $1,254,656,070.88 20121031 33 $233,993,326.40 21 $623,380,884.41 9 $1,350,556,892.29 20121130 34 $221,334,017.97 22 $596,527,594.16 10 $1,402,388,632.61 20121231 35 $209,755,388.83 23 $571,221,132.14 11 $1,446,045,641.11 20130131 36 $197,611,306.60 24 $544,660,957.96 12 $1,393,484,108.61 0 $85,102,561.15 20130228 37 $185,426,506.79 25 $518,862,568.14 13 $1,343,608,821.67 1 $167,729,888.10 20130331 38 $172,550,575.65 26 $490,392,424.40 14 $1,289,103,867.04 2 $312,321,877.93 20130430 39 $160,525,786.69 27 $462,663,638.93 15 $1,236,139,534.77 3 $550,730,813.52 20130531 40 $149,620,101.10 28 $437,323,804.42 16 $1,187,256,113.91 4 $790,658,875.91 20130630 41 $140,173,339.63 29 $414,664,837.59 17 $1,143,766,963.31 5 $976,875,029.76 20130731 42 $130,407,186.11 30 $391,116,063.78 18 $1,096,712,504.39 6 $1,147,625,985.79 20130831 43 $121,467,848.14 31 $369,135,278.50 19 $1,051,633,027.73 7 $1,289,409,850.89 20130930 44 $113,159,867.98 32 $348,182,248.23 20 $1,009,465,604.34 8 $1,402,457,790.27 20131031 45 $105,162,374.47 33 $328,027,278.14 21 $966,934,920.09 9 $1,492,175,380.68 20131130 46 $98,249,061.87 34 $309,875,919.23 22 $928,642,908.04 10 $1,550,181,775.93 20131231 47 $91,713,175.97 35 $292,306,650.11 23 $890,942,192.78 11 $1,619,305,990.84 20140131 48 $85,121,438.56 36 $274,559,109.29 24 $852,419,850.14 12 $1,564,405,844.22 0 $79,658,363.84 20140228 49 $78,074,434.45 37 $257,192,323.61 25 $814,709,244.45 13 $1,508,626,242.87 1 $165,672,726.26 20140331 50 $70,984,991.63 38 $238,524,043.14 26 $772,362,602.37 14 $1,445,557,361.29 2 $333,126,365.02 20140430 51 $64,499,149.75 39 $222,058,703.29 27 $733,066,539.82 15 $1,388,349,812.11 3 $584,661,186.60 20140531 52 $58,853,653.30 40 $207,250,486.84 28 $696,201,552.02 16 $1,333,271,135.46 4 $819,181,575.30 20140630 53 $53,619,661.09 41 $193,547,049.96 29 $660,248,029.13 17 $1,279,232,622.58 5 $1,002,908,345.41 20140731 54 $48,495,820.08 42 $180,217,094.88 30 $625,079,410.18 18 $1,226,416,495.62 6 $1,164,472,920.32 20140831 55 $43,986,926.82 43 $168,479,950.10 31 $591,836,269.08 19 $1,176,211,643.36 7 $1,299,620,435.59 20140930 56 $39,437,260.26 44 $156,901,110.96 32 $558,671,888.72 20 $1,124,666,367.02 8 $1,418,670,317.42 20141031 57 $35,171,233.96 45 $146,438,997.35 33 $526,677,999.83 21 $1,075,397,908.70 9 $1,517,594,890.73 20141130 58 $31,644,664.94 46 $137,667,222.58 34 $499,505,689.70 22 $1,032,966,898.65 10 $1,590,461,993.86 20141231 59 $27,879,719.73 47 $128,258,624.25 35 $470,512,492.48 23 $986,611,052.39 11 $1,675,184,544.74 20150131 60 $24,444,694.23 48 $119,929,907.14 36 $442,981,533.87 24 $942,728,508.43 12 $1,618,848,654.34 0 $94,685,593.23 20150228 61 $21,182,123.08 49 $111,160,206.08 37 $416,077,007.81 25 $899,403,019.01 13 $1,561,099,328.01 1 $207,121,218.33 20150331 62 $18,009,277.10 50 $101,625,935.40 38 $386,721,886.60 26 $849,395,823.18 14 $1,494,241,791.36 2 $410,786,293.85 20150430 63 $15,431,750.70 51 $93,496,123.83 39 $359,798,480.92 27 $804,084,077.87 15 $1,434,293,394.54 3 $724,892,892.43 20150531 64 $13,177,575.31 52 $85,913,943.47 40 $335,621,273.35 28 $762,536,181.90 16 $1,378,891,481.45 4 $1,002,250,688.98 20150630 65 $11,299,058.58 53 $78,467,147.16 41 $311,576,733.50 29 $718,733,975.08 17 $1,321,041,001.43 5 $1,224,613,464.77 20150731 66 $9,657,875.94 54 $71,461,415.62 42 $288,587,940.88 30 $677,351,817.55 18 $1,264,865,047.17 6 $1,427,452,062.66 20150831 67 $8,405,723.83 55 $65,195,186.03 43 $268,012,936.81 31 $637,668,889.71 19 $1,211,372,116.32 7 $1,577,053,715.92 20150930 68 $7,356,875.72 56 $59,160,140.96 44 $248,506,700.99 32 $599,406,381.00 20 $1,160,250,775.75 8 $1,714,652,471.15 20151031 69 $6,480,795.27 57 $53,450,012.89 45 $229,782,846.76 33 $562,540,087.17 21 $1,110,000,396.43 9 $1,814,956,977.67 20151130 70 $5,750,055.06 58 $48,367,874.41 46 $212,984,156.02 34 $528,081,626.03 22 $1,062,443,398.71 10 $1,892,038,991.36 20151231 71 $5,248,898.69 59 $43,386,941.37 47 $196,784,957.22 35 $494,220,834.27 23 $1,015,133,096.31 11 $1,971,086,601.19 20160131 72 $4,822,041.83 60 $38,916,609.99 48 $182,233,021.66 36 $463,587,266.32 24 $971,231,320.15 12 $1,908,909,571.52 0 $130,721,294.27 20160229 73 $4,365,844.42 61 $34,018,063.63 49 $165,651,113.32 37 $429,491,237.15 25 $921,174,906.28 13 $1,836,074,135.97 1 $290,083,621.26 20160331 74 $3,964,780.71 62 $29,684,512.09 50 $149,653,396.26 38 $395,065,623.87 26 $870,609,225.88 14 $1,760,772,566.49 2 $591,329,263.07 20160430 75 $3,639,422.27 63 $26,187,669.55 51 $136,536,204.87 39 $364,907,565.57 27 $824,526,764.97 15 $1,691,294,644.98 3 $911,733,254.08 20160531 76 $3,388,545.17 64 $23,062,423.23 52 $124,406,548.27 40 $336,894,195.29 28 $779,888,204.43 16 $1,623,684,013.21 4 $1,188,859,719.95 20160630 77 $3,140,723.88 65 $20,172,728.27 53 $113,259,104.90 41 $310,718,145.31 29 $736,961,617.81 17 $1,558,262,420.07 5 $1,439,374,798.55 20160731 78 $2,920,528.68 66 $17,943,895.82 54 $103,033,383.38 42 $286,524,548.32 30 $696,848,882.27 18 $1,497,177,754.50 6 $1,611,096,562.16 20160831 79 $2,734,885.09 67 $15,864,979.49 55 $92,921,405.56 43 $262,057,298.36 31 $654,701,204.15 19 $1,432,234,512.96 7 $1,785,145,815.04 20160930 80 $2,572,139.24 68 $14,081,528.82 56 $83,863,982.57 44 $239,903,671.19 32 $615,470,883.55 20 $1,371,128,879.36 8 $1,938,036,022.45 20161031 81 $2,432,106.20 69 $12,701,647.49 57 $75,548,569.80 45 $219,481,679.82 33 $577,896,074.05 21 $1,312,651,408.45 9 $2,039,832,879.17 20161130 82 $2,291,441.91 70 $11,483,239.66 58 $67,985,988.22 46 $200,773,279.83 34 $543,345,143.14 22 $1,257,470,419.20 10 $2,119,240,601.20 20161231 83 $2,176,766.65 71 $10,479,646.87 59 $60,838,941.54 47 $183,083,715.19 35 $509,419,444.76 23 $1,203,551,753.99 11 $2,198,068,373.75 20170131 84 $2,100,665.37 72 $9,618,765.64 60 $54,047,757.42 48 $165,971,927.78 36 $475,875,545.26 24 $1,148,121,392.37 12 $2,123,021,142.05 0 $115,839,429.92 20170228 85 $1,995,009.76 73 $8,797,674.92 61 $47,618,364.47 49 $149,256,199.08 37 $443,416,430.18 25 $1,094,818,108.31 13 $2,049,228,917.40 1 $241,078,214.61 20170331 86 $1,883,481.91 74 $7,875,117.03 62 $41,826,044.73 50 $131,653,756.96 38 $407,603,978.89 26 $1,034,001,631.28 14 $1,964,853,570.00 2 $476,648,887.07 20170430 87 $1,808,373.86 75 $7,322,513.76 63 $37,375,499.88 51 $118,358,526.05 39 $378,280,079.97 27 $982,209,864.08 15 $1,894,818,103.52 3 $811,142,069.10 20170531 88 $1,699,535.34 76 $6,732,964.36 64 $33,305,581.18 52 $104,996,126.60 40 $348,213,285.65 28 $928,119,616.12 16 $1,818,605,917.47 4 $1,118,228,693.52 20170630 89 $1,605,970.68 77 $6,230,685.34 65 $29,742,244.13 53 $93,545,415.90 41 $321,351,582.78 29 $877,573,384.02 17 $1,747,068,035.68 5 $1,361,337,327.31 20170731 90 $1,535,387.33 78 $5,760,666.12 66 $26,668,076.18 54 $83,667,980.00 42 $296,392,818.16 30 $829,346,695.59 18 $1,678,887,041.00 6 $1,564,676,377.51 20170831 91 $1,460,371.52 79 $5,327,703.02 67 $24,036,932.53 55 $74,722,544.78 43 $271,880,143.29 31 $781,743,717.76 19 $1,609,006,596.14 7 $1,760,934,265.15 20170930 92 $1,413,321.61 80 $4,990,106.09 68 $22,012,420.11 56 $66,933,696.83 44 $250,347,351.40 32 $737,763,451.20 20 $1,544,760,041.58 8 $1,907,073,752.15 20171031 93 $1,324,526.93 81 $4,719,105.30 69 $20,120,763.55 57 $59,485,680.41 45 $228,571,426.36 33 $693,617,421.70 21 $1,478,721,530.21 9 $2,023,921,130.25 20171130 94 $1,290,986.08 82 $4,453,860.36 70 $18,613,203.09 58 $52,959,653.99 46 $209,599,551.14 34 $652,892,173.36 22 $1,417,716,854.93 10 $2,112,179,764.37 20171231 95 $1,255,462.34 83 $4,235,055.74 71 $17,330,662.25 59 $46,972,439.95 47 $192,161,658.75 35 $614,449,160.50 23 $1,357,941,979.25 11 $2,198,825,460.34 20180131 96 $1,220,659.01 84 $3,971,340.79 72 $16,231,490.21 60 $41,005,285.12 48 $174,739,916.22 36 $574,874,504.74 24 $1,296,557,611.53 12 $2,121,553,323.91 0 $122,235,033.14 20180228 97 $1,176,631.10 85 $3,698,507.95 73 $15,135,773.73 61 $35,371,533.19 49 $158,147,957.18 37 $536,773,065.02 25 $1,236,990,263.03 13 $2,045,154,611.61 1 $254,922,049.44 20180331 98 $1,118,925.96 86 $3,488,698.16 74 $14,089,088.02 62 $30,111,303.32 50 $140,811,648.14 38 $495,234,176.30 26 $1,170,922,760.60 14 $1,959,518,577.66 2 $531,981,238.62 20180430 99 $1,061,173.76 87 $3,253,513.38 75 $13,286,483.23 63 $25,960,274.52 51 $126,794,234.18 39 $458,635,667.95 27 $1,111,817,056.92 15 $1,882,385,271.76 3 $844,907,882.41 20180531 100 $1,021,849.97 88 $3,062,574.22 76 $12,470,608.76 64 $22,289,675.40 52 $114,003,804.36 40 $423,761,790.89 28 $1,054,239,744.20 16 $1,806,256,837.47 4 $1,223,589,591.93 20180630 101 $995,869.66 89 $2,918,059.86 77 $11,712,270.61 65 $19,134,935.74 53 $102,958,926.89 41 $391,850,793.14 29 $998,602,372.05 17 $1,733,912,526.59 5 $1,522,706,322.33 20180731 102 $976,380.12 90 $2,809,873.99 78 $10,999,339.10 66 $16,477,201.90 54 $92,510,773.58 42 $361,505,405.60 30 $944,366,801.25 18 $1,662,572,330.00 6 $1,746,624,391.43 20180831 103 $933,923.19 91 $2,703,576.95 79 $10,435,446.20 67 $14,039,763.64 55 $82,715,618.33 43 $332,336,877.94 31 $890,028,967.30 19 $1,592,052,716.74 7 $1,947,848,239.84 20180930 80 68 56 44 32 20 8 20181031 81 69 57 45 33 21 9 20181130 82 70 58 46 34 22 10 20181231 83 71 59 47 35 23 11 20190131 84 72 60 48 36 24 12 20190228 85 73 61 49 37 25 13 20190331 86 74 62 50 38 26 14 20190430 87 75 63 51 39 27 15 20190531 88 76 64 52 40 28 16 20190630 89 77 65 53 41 29 17 20190731 90 78 66 54 42 30 18 20190831 91 79 67 55 43 31 19 20190930 92 80 68 56 44 32 20 20191031 93 81 69 57 45 33 21 20191130 94 82 70 58 46 34 22 20191231 95 ???? 83 ???? 71 ???? 59 ???? 47 ???? 35 ???? 23 ????
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07-14-2017
01:13 PM
ID cell(char-variable) home(char-variable) work(char-variable) Dup_phone All_same Dup_from 1 (XXX)XXX-XXXX (XXX)XXX-XXXX (YYY)YYY-YYYY Y N CELL_HOME 2 (ZZZ)ZZZ-ZZZZ (YYY)YYY-YYYY (ZZZ)ZZZ-ZZZZ Y N CELL_WORK 3 (ZZZ)ZZZ-ZZZZ (YYY)YYY-YYYY (YYY)YYY-YYYY Y N HOME_WORK 4 (XXX)XXX-XXXX (XXX)XXX-XXXX (XXX)XXX-XXXX Y Y ALL_DUP 5 (XXX)XXX-XXXX (YYY)YYY-YYYY (ZZZ)ZZZ-ZZZZ N N NO_DUP 6 (XXX)XXX-XXXX missing missing N N NO_DUP I have above dataset with ID, cell, home, work variables. For each ID, I need to look if we have any duplicate phone information and and also identify which ones are duplicates. I need to be able to create last three columns of above data. MIssing values shouldnt be accounted for equality. Any quick logic using data step? Thanks!
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06-30-2017
09:27 AM
@Rick_SAS Thanks for the link its very useful! I am getting exact same results even after using FREQ accs; proc genmod data=DTI_mod descending; class grade DTI; freq accs; model bad = Grade DTI grade*DTI/ dist=binomial link=log; run;
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06-30-2017
08:57 AM
Thanks. I have modified my data so I have a 2 level target variable and tried genmod with binomial distribution. It is giving me similar results compared to poisson. data: Grade DTI year bad Accs A1 A-(0-45%) 2013 N 51453 A1 A-(0-45%) 2013 Y 103 A1 A-(0-45%) 2014 N 49798 A1 A-(0-45%) 2014 Y 120 A1 A-(0-45%) 2015 N 59573 A1 A-(0-45%) 2015 Y 150 A1 A-(0-45%) 2016 N 3912 code: proc genmod data=DTI_mod descending; class grade DTI; /*weight accs;*/ model bad = Grade DTI grade*DTI/ dist=binomial link=log; weight accs; run; log: NOTE: PROC GENMOD is modeling the probability that bad='Y'. WARNING: The negative of the Hessian is not positive definite. The convergence is questionable. WARNING: The procedure is continuing but the validity of the model fit is questionable. WARNING: The specified model did not converge. NOTE: The Pearson chi-square and deviance are not computed since the AGGREGATE option is not specified. WARNING: Negative of Hessian not positive definite. NOTE: The scale parameter was held fixed. NOTE: PROCEDURE GENMOD used (Total process time): real time 0.15 seconds cpu time 0.07 seconds results: Parameter DF Estimate Grade*DTI A1 A-(0-45%) 1 0.1193 Grade*DTI A2 A-(0-45%) 1 0.7107 Grade*DTI A3 A-(0-45%) 1 1.202 Grade*DTI D1 A-(0-45%) 1 0.5473 Grade*DTI D2 A-(0-45%) 1 16.6717 Grade*DTI DN A-(0-45%) 1 15.7341 Grade*DTI DS A-(0-45%) 0 0
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06-29-2017
02:21 PM
@Ksharp@JacobSimonsen thanks! when i try to use @Ksharp solution I am getting below errors. proc genmod data=DTI; class grade DTI; /*weight accs;*/ model bad_Accs = Grade DTI grade*DTI/ dist=poisson link=log offset=log(accs); run; ERROR: Variable LOG not found. (or) proc genmod data=DTI; class grade DTI; /*weight accs;*/ model bad_Accs = Grade DTI grade*DTI/ dist=poisson link=log offset=accs; run; ERROR: The mean parameter is either invalid or at a limit of its range for some observations. I have also tried @JacobSimonsen your approach but still not able to relate the results with observed results. proc genmod data=DTI_new; class grade DTI; /* rate = bad_accs/total_accs */ model rate = Grade DTI grade*DTI/ dist=poisson link=log; weight total_accs; run; observed: Grade DTI bad rate Total Accs Bad Accs A A-(0-45%) 0.23% 165121 385 B A-(0-45%) 0.68% 250156 1708 C A-(0-45%) 1.92% 240478 4609 D A-(0-45%) 3.05% 33809 1030 E A-(0-45%) 3.89% 2853 111 F A-(0-45%) 1.52% 7417 113 G A-(0-45%) 3.30% 3026 100 model results: Parameter DF Estimate Grade*DTI A A-(0-45%) 1 0.1193 Grade*DTI B A-(0-45%) 1 0.7107 Grade*DTI C A-(0-45%) 1 1.202 Grade*DTI D A-(0-45%) 1 0.5473 Grade*DTI E A-(0-45%) 1 16.0838 Grade*DTI F A-(0-45%) 1 15.1462 Grade*DTI G A-(0-45%) 0 0 Maybe I am not reading it right ( I am trying to relate model estimate to the observed bad rate %), but it doesn't seem to rank order the bad rate by grade*DTI correctly.
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06-28-2017
11:33 AM
I am trying to model counts data to rank order risk of accounts going bad by grade and band. data looks like this: Grade band year bad rate Total Accs Bad Accs A A-(0-45%) 2016 0.31% 3924 12 A A-(0-45%) 2013 0.20% 51556 103 A A-(0-45%) 2014 0.24% 49918 120 A A-(0-45%) 2015 0.25% 59723 150 A B-(>45-55%) 2016 0.80% 249 2 A B-(>45-55%) 2015 0.22% 3664 8 A B-(>45-55%) 2013 0.32% 3149 10 when I summarize the above data, I see below observed bad rate by grade*band: Grade band bad rate Total Accs Bad Accs A A-(0-45%) 0.23% 165121 385 B A-(0-45%) 0.68% 250156 1708 C A-(0-45%) 1.92% 240478 4609 D A-(0-45%) 3.05% 33809 1030 E A-(0-45%) 3.89% 2853 111 F A-(0-45%) 1.52% 7417 113 G A-(0-45%) 3.30% 3026 100 I have used proc genmod with poisson distribution to model the above data to compare if it rank ordering according to the observed results. proc genmod data=data; class grade band; model bad_Accs = Grade bands grade*DTI/ dist=poisson link=log; run; I see the below results: Analysis of Maximum likelihood paramter estimates parameter Estimate Grade*band G A-(0-45%) 0 Grade*band D A-(0-45%) 0.5404 Grade*band A A-(0-45%) 0.9426 Grade*band B A-(0-45%) 1.0461 Grade*band C A-(0-45%) 1.2279 Grade*band E A-(0-45%) 17.6989 Grade*band F A-(0-45%) 17.7168 from the results it suggests that a F grade with A-(0-45%) is 17.71 % more likely to go bad compared to other grades? but from the observed results, I see bad rate is high for grade E, should'nt grade E have higher parameter estimate in genmod ? or am I modeling wrong vraiable? I feel like I should model for Total Accs/Bad Accs instead of just Bad Accs to consider severity. when I try to do that as below, its givng me an error: proc genmod data=DTI; class grade DTI; model (Accs/bad_Accs)*100 = Grade DTI grade*DTI/ dist=poisson link=log; run; 19 model (Accs/bad_Accs)*100 = Grade DTI grade*DTI/ dist=poisson link=log; _ 22 76 ERROR 22-322: Syntax error, expecting one of the following: a name, ','. ERROR 76-322: Syntax error, statement will be ignored. Any suggestion on how to model for bad accs including severity as well in the model?
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