hello, I'm trying to code up the likelihood below using Proc NLMIXED.   I have dataset that is person period, sorted by id and time. here is the structure of dataset, sorted by ID and time.   ID Time y (Event) last_ob x1 1 1 0 1 20 2 1 0 0 21 2 2 0 1 20 2 3 0 0 10 2 4 1 1 5 3 1 0 0 12 3 2 0 0 23 3 3 0 0 10 3 4 0 0 4 3 5 0 0 23 3 6 0 0 10 3 7 0 1 5     Below is the sas proc NLMIXED code:     proc nlmixed data=your_data qpoints=50; parms mu=0 sigma2=1 beta1=0; bounds sigma2 > 0; /* Linear predictor */ linpred = eta + beta1*x1; theta = exp(linpred) / (1 + exp(linpred)); log_surv = log(1 - theta); /* Accumulate log survival up to time t-1 using ZLAG */ if time = 1 then acc_log_surv = 0; else acc_log_surv = ZLAG(acc_log_surv) + ZLAG(log_surv); /* Event or censoring contribution */ if y = 1 then loglik = acc_log_surv + log(theta); else if last_obs = 1 then loglik = acc_log_surv + log(1 - theta); else loglik = .; model y ~ general(loglik); random eta ~ normal(mu, sigma2) subject=id; run;     I have two part question,   what would "y" in model general(loglik) be? and also how do I code up cumulative product (or log sum) of P(T) and S(T) since retain and first.id is not available. I just used zlag based on time = 1? is this correct?   Any help would be greatly appreciated.   Thanks   Sri         ... View more

How do I interpret the outlier effects such as level shift on log transformed data, clearly exponentiating the coefficients is not correct.   As an example, see below is what I get. how to interpret Nov 2008 level shift value of -0.09102?   below is a reproducible example to produce the above coefficients.     data retail(label="Retail Sales"); attrib Date length=8 label="Date" format=MONYY7. informat=MONYY7. sales length=8 label="Retail Sales" ; input Date sales; datalines; Jan1992 146376 Feb1992 147079 Mar1992 159336 Apr1992 163669 May1992 170068 Jun1992 168663 Jul1992 169890 Aug1992 170364 Sep1992 164617 Oct1992 173655 Nov1992 171547 Dec1992 208838 Jan1993 153221 Feb1993 150087 Mar1993 170439 Apr1993 176456 May1993 182231 Jun1993 181535 Jul1993 183682 Aug1993 183318 Sep1993 177406 Oct1993 182737 Nov1993 187443 Dec1993 224540 Jan1994 161349 Feb1994 162841 Mar1994 192319 Apr1994 189569 May1994 194927 Jun1994 197946 Jul1994 193355 Aug1994 202388 Sep1994 193954 Oct1994 197956 Nov1994 202520 Dec1994 241111 Jan1995 175344 Feb1995 172138 Mar1995 201279 Apr1995 196039 May1995 210478 Jun1995 211844 Jul1995 203411 Aug1995 214248 Sep1995 202122 Oct1995 204044 Nov1995 212190 Dec1995 247491 Jan1996 185019 Feb1996 192380 Mar1996 212110 Apr1996 211718 May1996 226936 Jun1996 217511 Jul1996 218111 Aug1996 226062 Sep1996 209250 Oct1996 222663 Nov1996 223953 Dec1996 258081 Jan1997 200389 Feb1997 197556 Mar1997 225133 Apr1997 220329 May1997 234190 Jun1997 227365 Jul1997 231521 Aug1997 235252 Sep1997 222807 Oct1997 232251 Nov1997 228284 Dec1997 271054 Jan1998 207853 Feb1998 203863 Mar1998 230313 Apr1998 234503 May1998 245027 Jun1998 244067 Jul1998 241431 Aug1998 240462 Sep1998 231243 Oct1998 244234 Nov1998 240991 Dec1998 288969 Jan1999 218126 Feb1999 220650 Mar1999 253550 Apr1999 250783 May1999 262113 Jun1999 260918 Jul1999 262051 Aug1999 265089 Sep1999 253905 Oct1999 258040 Nov1999 264106 Dec1999 317659 Jan2000 236422 Feb2000 250580 Mar2000 279515 Apr2000 264417 May2000 283706 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405608 Jan2006 318546 Feb2006 314051 Mar2006 361993 Apr2006 351667 May2006 373560 Jun2006 366615 Jul2006 362203 Aug2006 375795 Sep2006 346214 Oct2006 348796 Nov2006 356928 Dec2006 417991 Jan2007 328877 Feb2007 323162 Mar2007 374142 Apr2007 358535 May2007 391512 Jun2007 376639 Jul2007 372354 Aug2007 388016 Sep2007 353936 Oct2007 368681 Nov2007 377802 Dec2007 426077 Jan2008 342639 Feb2008 343893 Mar2008 372907 Apr2008 368920 May2008 397956 Jun2008 378507 Jul2008 383726 Aug2008 382862 Sep2008 350542 Oct2008 349847 Nov2008 335545 Dec2008 384236 Jan2009 310188 Feb2009 299412 Mar2009 328526 Apr2009 329854 May2009 347728 Jun2009 344405 Jul2009 348071 Aug2009 353392 Sep2009 324646 Oct2009 338610 Nov2009 339363 Dec2009 400281 Jan2010 314557 Feb2010 310925 Mar2010 360679 Apr2010 356333 May2010 365605 Jun2010 358604 Jul2010 361939 Aug2010 362627 Sep2010 345999 Oct2010 355209 Nov2010 365828 Dec2010 426663 Jan2011 335587 Feb2011 337314 Mar2011 387088 Apr2011 380775 May2011 391999 Jun2011 388700 Jul2011 384682 Aug2011 394609 Sep2011 375025 Oct2011 379482 Nov2011 391220 Dec2011 451821 Jan2012 355184 Feb2012 372401 Mar2012 414149 Apr2012 392949 May2012 418608 Jun2012 400975 Jul2012 396026 Aug2012 417922 Sep2012 385609 Oct2012 399400 Nov2012 411065 Dec2012 462102 Jan2013 375587 Feb2013 373987 Mar2013 421719 Apr2013 408544 May2013 437188 Jun2013 414714 Jul2013 422410 Aug2013 435005 Sep2013 396213 Oct2013 415700 Nov2013 423786 Dec2013 476910 Jan2014 383054 Feb2014 380019 Mar2014 432651 Apr2014 431396 May2014 459231 Jun2014 433282 Jul2014 443281 Aug2014 451366 Sep2014 421424 Oct2014 438457 Nov2014 439165 Dec2014 502330 Jan2015 398027 Feb2015 388063 Mar2015 445970 Apr2015 439637 May2015 464785 Jun2015 449794 Jul2015 459533 Aug2015 457905 Sep2015 432782 Oct2015 446593 Nov2015 446773 Dec2015 519625 Jan2016 401982 Feb2016 415189 Mar2016 461198 Apr2016 451468 May2016 470430 Jun2016 465023 Jul2016 462162 Aug2016 471996 Sep2016 448749 Oct2016 453088 Nov2016 467765 Dec2016 540273 Jan2017 421560 Feb2017 417983 Mar2017 483059 Apr2017 466058 May2017 495264 Jun2017 482456 Jul2017 475984 Aug2017 491090 Sep2017 470406 Oct2017 476925 Nov2017 499446 Dec2017 560379 Jan2018 445484 Feb2018 437005 Mar2018 510380 Apr2018 482412 May2018 530082 Jun2018 510029 Jul2018 508010 Aug2018 523933 Sep2018 481094 Oct2018 506360 Nov2018 522804 Dec2018 563497 Jan2019 459143 Feb2019 444794 Mar2019 518304 Apr2019 510176 May2019 547036 Jun2019 517984 Jul2019 533058 ; run; data xretail; set retail; lsales = log(sales); run; proc arima data=xretail; identify var= lsales(1,12); estimate q=(1)(12) noint method=ml; outlier type=(ao ls tc(5)) maxnum=10 id=Date; run;       ... 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Hello, I would l would like to know how one could specify the following compounded transfer function. This gives a compounded effect of abrupt spike and decay and settles in a new mean.   This can be visualized as follows. I can easily code the first part of the equation, the question is how to compound both with same intervention variable. Thanks in advance   ... View more

I'm trying to plot multiple time series plot which looks like a spahetti plot. I'm trying to avoid spaghetti plot by using lattice or panel like plots.   Here is the SAS dataset to replicate,I have dataset called "temp" with grouping variable called group. The data is time series with time variable "date" and y variable "air".   data alldata1; set sashelp.air; group = "REF"; keep date air group; run; data alldata2; set sashelp.air; group = "A1"; air=air+30; keep date air group; run; data alldata3; set sashelp.air; group = "A2"; air=air+60; keep date air group; run; data alldata4; set sashelp.air; group = "A3"; air=air-30; keep date air group; run; data alldata5; set sashelp.air; group = "A4"; air=air-60; keep date air group; run; data temp; set alldata1 alldata2 alldata3 alldata4 alldata5; run; The grouping variable "group" has 5 categories. "REF", A1,A2,A3,A4. I need to creat a chart like the one below where "REF" group highlighted in "red" and each category is highlighted in blue and the rest of the category in light grey.     Below are my questions: How do I replicate thie above using a SAS procedure ? how can I choose the number of rows and columns, in the above chart it is 2 (rows) by 2 (colums). What if I have more than 8 categories and want to display it as ? how can I export this cart as ps or png high resolution file?   Thanks ... View more

Is it possible to access proc optlso in SAS/IML ? If yes, is it possible to provide few example from the documentation on SAS/IML ?   Thanks ... View more