About T_Reddy

T_Reddy · ‎05-08-2020

This is indeed extremely useful! My model is nonlinear (mu = alpha/(1+exp(-gamma*(time-eta))). It does not appear that any of the procedures which support the effect statement will be able to accomodate our modeling approach.

T_Reddy · ‎05-07-2020

Thank you for your response. I have been examining the predictions and it looks like the issue is how I have programmed the piecewise model (splines). I use a separate dummy variable for each period (i_25, i_30, I_35 etc) and this results in a discontinuity, I would need to reparameterize, Is there any simple way (like the effect statement) in Proc NLIN or NLMIXED?

T_Reddy · ‎04-28-2020

Thank you for the speedy response. The issue is that the confidence interval estimated does not contain the value of the actual point estimate (for example the prediction for day 53 is close to 3900 but both the lower and upper bound of the CI are above 4000). It could signal an issue with the algorithm possibly

T_Reddy · ‎04-27-2020

time i_25 i_30 i_35 i_40 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 4 0 0 0 0 5 0 0 0 0 6 0 0 0 0 7 0 0 0 0 8 0 0 0 0 9 0 0 0 0 10 0 0 0 0 11 0 0 0 0 12 0 0 0 0 13 0 0 0 0 14 0 0 0 0 15 0 0 0 0 16 0 0 0 0 17 0 0 0 0 18 0 0 0 0 19 0 0 0 0 20 0 0 0 0 21 0 0 0 0 22 0 0 0 0 23 0 0 0 0 24 0 0 0 0 25 0 0 0 0 26 1 0 0 0 27 1 0 0 0 28 1 0 0 0 29 1 0 0 0 30 1 0 0 0 31 1 1 0 0 32 1 1 0 0 33 1 1 0 0 34 1 1 0 0 35 1 1 0 0 36 1 1 1 0 37 1 1 1 0 38 1 1 1 0 39 1 1 1 0 40 1 1 1 0 41 1 1 1 1 42 1 1 1 1 43 1 1 1 1 44 1 1 1 1 45 1 1 1 1 46 1 1 1 1 47 1 1 1 1 48 1 1 1 1

T_Reddy · ‎04-26-2020

Hi there. I am trying to use the parametric bootstrap to construct prediction intervals for 5, 10 and 15 day forecasts of the cumulative cases of a disease . I have fitted a three parameter logistic model to cumulative cases and use these parameter estimates to simulate daily new cases (the derivative of cumulative cases which is a function of cumulative cases) for the 1000 datasets. My bootstrap confidence intervals do not contain the true value for the various predictions though and I am not sure what I am doing wrong. I would greatly appreciate any assistance. /* Simulate multiple samples from a regression model */ data explanatory;set covid_window;keep time i_25 i_30 i_35 i_40;where time le 48;run; proc print data=parm2_log3pl;var parm_log3pl est_log3pl;run; %let NumSamples = 1000; /* number of samples */ data RegSim; call streaminit(123); set Explanatory; /* implicit loop over obs*/ ObsNum = _N_; /* observation number */ b1=2037.03 ; b11=2555.72; b2=0.3301; b21=-0.2659; b24=0.07861; b23=0.001893; b22=0.003788; b3=22.8728; b31=17.8590; gamma=b2+(b21*i_25)+(b22*i_30)+(b23*i_35)+(b24*i_40); eta=b3+(b31*i_25); alpha=b1+(b11*i_25); mu = alpha/(1+exp(-gamma*(time-eta))); lambda = (gamma*mu)*(1-(mu/alpha)); do SampleID = 1 to &NumSamples; Y = rand("Poisson",lambda); output; end; run; 　 proc sort data=RegSim; /* sort for BY-group processing */ by SampleID ObsNum; run; /********* Now generate cumulative************/ data RegSim;set RegSim;by SampleID; if first.SampleID then do; total_cases = 0; end; total_cases + Y; run; data pred_window; do SampleID=1 to 1000; do time=49 to 100; output; end; end; run; data covid_window;set RegSim pred_window;run; data covid_window;set covid_window; i_25=.;if time le 25 then i_25=0; if time gt 25 then i_25=1; i_30=.;if time le 30 then i_30=0; if time gt 30 then i_30=1; i_35=.;if time le 35 then i_35=0; if time gt 35 then i_35=1; i_40=.;if time le 40 then i_40=0; if time gt 40 then i_40=1; run; proc sort data=covid_window;by SampleID time;run; proc nlmixed data=covid_window gconv=0; parms b1=2000 b11=2916 b2=0.43 b21=-0.35 b24=0 b23=0 b22=0 b3=20 b31=18; by SampleID; gamma=b2+(b21*i_25)+(b22*i_30)+(b23*i_35)+(b24*i_40); eta=b3+(b31*i_25); alpha=b1+(b11*i_25); mu = alpha/(1+exp(-gamma*(time-eta))); model total_cases ~ poisson(mu); predict mu out=outputBS_log3pl; run; proc sort data=outputBS_log3pl;by time sampleID;run; proc univariate data=outputBS_log3pl noprint; var pred; by time; output out=Pctls pctlpts = 2.5 97.5 pctlpre = mu pctlname = lb_2_5 ub_97_5; run;

T_Reddy · ‎04-29-2017

Yes, exactly!

T_Reddy · ‎04-29-2017

The initial values are c2 and c3. c1 is essential zero. This is how the initial values c2 and c3 are calculated: data probc;set prob;by id; if visit eq 2 then c=(lagproblt1000*problt1000) + (lagprobgt1000*problt1000) + (lagproblt1000*probgt1000); if visit eq 3 then c= (lag2problt1000*lagproblt1000*problt1000) + (lag2probgt1000*lagproblt1000*problt1000) + (lag2problt1000*lagprobgt1000*problt1000) + (lag2problt1000*lagproblt1000*probgt1000) + (lag2probgt1000*lagproblt1000*probgt1000); run; Thereafter for visit>3 c=(lag2c*lagproblt1000*probgt1000) + (lagc*problt1000) where lag2c is the value of c two visits earlier and lagc is the value of c one visit earlier.

T_Reddy · ‎04-28-2017

Sorry about that, this first line below is run on the data test to generate visit and therafter the regular code follows data prob;set test;by id;if first.id then visit=1;else visit+1;run; /****Part 1: generate c2-c3**********/ data probc;set prob;by id; if visit eq 2 then c=(lagprobgt1000*probgt1000) + (lagproblt1000*probgt1000) + (lagprobgt1000*problt1000); if visit eq 3 then c= (lag2probgt1000*lagprobgt1000*probgt1000) + (lag2problt1000*lagprobgt1000*probgt1000) + (lag2probgt1000*lagproblt1000*probgt1000) + (lag2probgt1000*lagprobgt1000*problt1000) + (lag2problt1000*lagprobgt1000*problt1000); run; /***** Part 2 Recursively calculate using the initial probabilities****** This is not the most efficient way *****/ data probc2;set probc; by id; lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); etc

T_Reddy · ‎04-28-2017

The variable visit is generated by the first line in the code I posted : /**** generate observation number**********/ data prob;set prob;by id;if first.id then visit=1;else visit+1;run; I am attaching the output for the variable c that I expect.

T_Reddy · ‎04-28-2017

I suspect that I should use retain statements but not sure how to go about it.

T_Reddy · ‎04-28-2017

Sure. Thanks for the speedy response. See below data test; input day id probgt1000 problt1000 lagproblt1000 lag2problt1000 lag3problt1000 lagprobgt1000 lag2probgt1000 lag3probgt1000; datalines; 0 1 0.999999838 1.62E-07 2 1 0.999991138 8.86E-06 1.62E-07 0.999999838 7 1 0.987787081 0.012212919 8.86E-06 1.62E-07 0.999991138 0.999999838 16 1 0.237545783 0.762454217 0.012212919 8.86E-06 1.62E-07 0.987787081 0.999991138 0.999999838 29 1 0.079696412 0.920303588 0.762454217 0.012212919 8.86E-06 0.237545783 0.987787081 0.999991138 57 1 0.001868728 0.998131272 0.920303588 0.762454217 0.012212919 0.079696412 0.237545783 0.987787081 91 1 8.13E-06 0.999991865 0.998131272 0.920303588 0.762454217 0.001868728 0.079696412 0.237545783 120 1 1.15E-07 0.999999885 0.999991865 0.998131272 0.920303588 8.13E-06 0.001868728 0.079696412 150 1 6.64E-10 0.999999999 0.999999885 0.999991865 0.998131272 1.15E-07 8.13E-06 0.001868728 180 1 1.76E-12 1 0.999999999 0.999999885 0.999991865 6.64E-10 1.15E-07 8.13E-06 210 1 2.11E-15 1 1 0.999999999 0.999999885 1.76E-12 6.64E-10 1.15E-07 240 1 0 1 1 1 0.999999999 2.11E-15 1.76E-12 6.64E-10 0 2 0.954524864 0.045475137 2 2 0.808897126 0.191102874 0.045475137 0.954524864 7 2 0.121488567 0.878511433 0.191102874 0.045475137 0.808897126 0.954524864 14 2 2.84197E-05 0.99997158 0.878511433 0.191102874 0.045475137 0.121488567 0.808897126 0.954524864 29 2 2.70E-06 0.999997298 0.99997158 0.878511433 0.191102874 2.84197E-05 0.121488567 0.808897126 56 2 2.01E-08 0.99999998 0.999997298 0.99997158 0.878511433 2.70E-06 2.84197E-05 0.121488567 85 2 6.75E-11 1 0.99999998 0.999997298 0.99997158 2.01E-08 2.70E-06 2.84197E-05 120 2 7.38E-14 1 1 0.99999998 0.999997298 6.75E-11 2.01E-08 2.70E-06 150 2 1.11E-16 1 1 1 0.99999998 7.38E-14 6.75E-11 2.01E-08 167 2 0 1 1 1 1 1.11E-16 7.38E-14 6.75E-11 180 2 0 1 1 1 1 0 1.11E-16 7.38E-14 0 3 0.999999999 6.66E-10 7 3 0.999326107 0.000673893 6.66E-10 0.999999999 9 3 0.99155374 0.00844626 0.000673893 6.66E-10 0.999326107 0.999999999 14 3 0.635937832 0.364062168 0.00844626 0.000673893 6.66E-10 0.99155374 0.999326107 0.999999999 28 3 0.523640435 0.476359565 0.364062168 0.00844626 0.000673893 0.635937832 0.99155374 0.999326107 56 3 0.302450412 0.697549588 0.476359565 0.364062168 0.00844626 0.523640435 0.635937832 0.99155374 84 3 0.355108729 0.644891271 0.697549588 0.476359565 0.364062168 0.302450412 0.523640435 0.635937832 90 3 0.475417732 0.524582268 0.644891271 0.697549588 0.476359565 0.355108729 0.302450412 0.523640435 120 3 0.931607984 0.068392016 0.524582268 0.644891271 0.697549588 0.475417732 0.355108729 0.302450412 150 3 0.998806882 0.001193118 0.068392016 0.524582268 0.644891271 0.931607984 0.475417732 0.355108729 166 3 0.999944187 0.000055813 0.001193118 0.068392016 0.524582268 0.998806882 0.931607984 0.475417732 180 3 0.999997751 2.25E-06 0.000055813 0.001193118 0.068392016 0.999944187 0.998806882 0.931607984 210 3 1 4.22E-10 2.25E-06 0.000055813 0.001193118 0.999997751 0.999944187 0.998806882 240 3 1 7.59E-15 4.22E-10 2.25E-06 0.000055813 1 0.999997751 0.999944187 270 3 1 1.29E-20 7.59E-15 4.22E-10 2.25E-06 1 1 0.999997751 300 3 1 2.02E-27 1.29E-20 7.59E-15 4.22E-10 1 1 1 330 3 1 2.95E-35 2.02E-27 1.29E-20 7.59E-15 1 1 1 360 3 1 3.95E-44 2.95E-35 2.02E-27 1.29E-20 1 1 1 390 3 1 4.86E-54 3.95E-44 2.95E-35 2.02E-27 1 1 1 420 3 1 5.47E-65 4.86E-54 3.95E-44 2.95E-35 1 1 1 450 3 1 5.62E-77 5.47E-65 4.86E-54 3.95E-44 1 1 1 0 4 0.999588107 0.000411893 2 4 0.994266075 0.005733926 0.000411893 0.999588107 7 4 0.686761914 0.313238086 0.005733926 0.000411893 0.994266075 0.999588107 10 4 0.230209335 0.769790666 0.313238086 0.005733926 0.000411893 0.686761914 0.994266075 0.999588107 14 4 0.008863027 0.991136973 0.769790666 0.313238086 0.005733926 0.230209335 0.686761914 0.994266075 28 4 0.007810872 0.992189128 0.991136973 0.769790666 0.313238086 0.008863027 0.230209335 0.686761914 56 4 0.006031055 0.993968945 0.992189128 0.991136973 0.769790666 0.007810872 0.008863027 0.230209335 86 4 0.026406683 0.973593317 0.993968945 0.992189128 0.991136973 0.006031055 0.007810872 0.008863027 90 4 0.042237651 0.957762349 0.973593317 0.993968945 0.992189128 0.026406683 0.006031055 0.007810872 120 4 0.443685146 0.556314854 0.957762349 0.973593317 0.993968945 0.042237651 0.026406683 0.006031055 150 4 0.925352277 0.074647723 0.556314854 0.957762349 0.973593317 0.443685146 0.042237651 0.026406683 180 4 0.998759633 0.001240367 0.074647723 0.556314854 0.957762349 0.925352277 0.443685146 0.042237651 210 4 0.99999798 2.02E-06 0.001240367 0.074647723 0.556314854 0.998759633 0.925352277 0.443685146 240 4 1 2.95E-10 2.02E-06 0.001240367 0.074647723 0.99999798 0.998759633 0.925352277 270 4 1 3.72E-15 2.95E-10 2.02E-06 0.001240367 1 0.99999798 0.998759633 300 4 1 3.98E-21 3.72E-15 2.95E-10 2.02E-06 1 1 0.99999798 330 4 1 3.55E-28 3.98E-21 3.72E-15 2.95E-10 1 1 1 360 4 1 2.64E-36 3.55E-28 3.98E-21 3.72E-15 1 1 1 390 4 1 1.62E-45 2.64E-36 3.55E-28 3.98E-21 1 1 1 420 4 1 8.19E-56 1.62E-45 2.64E-36 3.55E-28 1 1 1 450 4 1 3.41E-67 8.19E-56 1.62E-45 2.64E-36 1 1 1 0 5 0.999999999 9.08E-10 8 5 0.996994532 0.003005468 9.08E-10 0.999999999 9 5 0.990329715 0.009670285 0.003005468 9.08E-10 0.996994532 0.999999999 14 5 0.616951368 0.383048632 0.009670285 0.003005468 9.08E-10 0.990329715 0.996994532 0.999999999 28 5 0.066678872 0.933321128 0.383048632 0.009670285 0.003005468 0.616951368 0.990329715 0.996994532 56 5 1.72E-07 0.999999828 0.933321128 0.383048632 0.009670285 0.066678872 0.616951368 0.990329715 90 5 1.13E-13 1 0.999999828 0.933321128 0.383048632 1.72E-07 0.066678872 0.616951368 120 5 3.31E-13 1 1 0.999999828 0.933321128 1.13E-13 1.72E-07 0.066678872 150 5 9.52E-13 1 1 1 0.999999828 3.31E-13 1.13E-13 1.72E-07 168 5 1.78E-12 1 1 1 1 9.52E-13 3.31E-13 1.13E-13 180 5 2.68E-12 1 1 1 1 1.78E-12 9.52E-13 3.31E-13 210 5 7.39E-12 1 1 1 1 2.68E-12 1.78E-12 9.52E-13 240 5 1.99E-11 1 1 1 1 7.39E-12 2.68E-12 1.78E-12 270 5 5.27E-11 1 1 1 1 1.99E-11 7.39E-12 2.68E-12 300 5 1.37E-10 1 1 1 1 5.27E-11 1.99E-11 7.39E-12 330 5 3.47E-10 1 1 1 1 1.37E-10 5.27E-11 1.99E-11 360 5 8.61E-10 0.999999999 1 1 1 3.47E-10 1.37E-10 5.27E-11 390 5 2.10E-09 0.999999998 0.999999999 1 1 8.61E-10 3.47E-10 1.37E-10 420 5 5.00E-09 0.999999995 0.999999998 0.999999999 1 2.10E-09 8.61E-10 3.47E-10 450 5 1.17E-08 0.999999988 0.999999995 0.999999998 0.999999999 5.00E-09 2.10E-09 8.61E-10 0 6 0.999999992 7.86E-09 2 6 0.999999341 6.59E-07 7.86E-09 0.999999992 6 6 0.999321861 0.000678139 6.59E-07 7.86E-09 0.999999341 0.999999992 9 6 0.976089034 0.023910966 0.000678139 6.59E-07 7.86E-09 0.999321861 0.999999341 0.999999992 13 6 0.635258637 0.364741363 0.023910966 0.000678139 6.59E-07 0.976089034 0.999321861 0.999999341 27 6 0.386715096 0.613284904 0.364741363 0.023910966 0.000678139 0.635258637 0.976089034 0.999321861 51 6 0.240699785 0.759300215 0.613284904 0.364741363 0.023910966 0.386715096 0.635258637 0.976089034 90 6 0.112612642 0.887387358 0.759300215 0.613284904 0.364741363 0.240699785 0.386715096 0.635258637 120 6 0.077983686 0.922016314 0.887387358 0.759300215 0.613284904 0.112612642 0.240699785 0.386715096 150 6 0.052104636 0.947895364 0.922016314 0.887387358 0.759300215 0.077983686 0.112612642 0.240699785 180 6 0.033565496 0.966434504 0.947895364 0.922016314 0.887387358 0.052104636 0.077983686 0.112612642 210 6 0.02083448 0.979165521 0.966434504 0.947895364 0.922016314 0.033565496 0.052104636 0.077983686 240 6 0.012454003 0.987545997 0.979165521 0.966434504 0.947895364 0.02083448 0.033565496 0.052104636 270 6 0.007165821 0.99283418 0.987545997 0.979165521 0.966434504 0.012454003 0.02083448 0.033565496 300 6 0.003967102 0.996032898 0.99283418 0.987545997 0.979165521 0.007165821 0.012454003 0.02083448 330 6 0.002112387 0.997887613 0.996032898 0.99283418 0.987545997 0.003967102 0.007165821 0.012454003 360 6 0.001081502 0.998918498 0.997887613 0.996032898 0.99283418 0.002112387 0.003967102 0.007165821 390 6 0.000532246 0.999467754 0.998918498 0.997887613 0.996032898 0.001081502 0.002112387 0.003967102 420 6 0.000251721 0.999748279 0.999467754 0.998918498 0.997887613 0.000532246 0.001081502 0.002112387 450 6 0.000114381 0.999885619 0.999748279 0.999467754 0.998918498 0.000251721 0.000532246 0.001081502 ; run;

T_Reddy · ‎04-28-2017

I have multiple observations for each individual and I have a complex function of probabilties that I wish to calculate - it is a function of the first and second previous values. I manually calculate c2 and c3 in Part 1 below and then I use these to calculate the subsequent values c4, c5 etc in Part 2 until the last visit value for each ID. Unfortunately I had to repeat the code (as seen below in part two) several times since lag does not retain the value. Is there a more efficient way I could do this? I would greatly appreciate any assistance. Thanks data prob;set prob;by id;if first.id then visit=1;else visit+1;run; /****Part 1: generate c2-c3**********/ data probc;set prob;by id; if visit eq 2 then c=(lagprobgt1000*probgt1000) + (lagproblt1000*probgt1000) + (lagprobgt1000*problt1000); if visit eq 3 then c= (lag2probgt1000*lagprobgt1000*probgt1000) + (lag2problt1000*lagprobgt1000*probgt1000) + (lag2probgt1000*lagproblt1000*probgt1000) + (lag2probgt1000*lagprobgt1000*problt1000) + (lag2problt1000*lagprobgt1000*problt1000); run; /***** Part 2 Recursively calculate using the initial probabilities****** This is not the most efficient way *****/ data probc2;set probc; by id; lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c); lag2c = lag2(c); if c=. then c=(lag2c*lagprobgt1000*problt1000) + (lagc*probgt1000); lagc = lag(c);run;

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Re: Parametric bootstrap: Poisson regression

Re: Parametric bootstrap: Poisson regression

Re: Parametric bootstrap: Poisson regression

Re: Parametric bootstrap: Poisson regression

Parametric bootstrap: Poisson regression

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Parametric bootstrap: Poisson regression

Re: Parametric bootstrap: Poisson regression

Re: Parametric bootstrap: Poisson regression

Re: Parametric bootstrap: Poisson regression

Re: Parametric bootstrap: Poisson regression

Parametric bootstrap: Poisson regression

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Re: Recursively calculate value using function of previous values

Recursively calculate value using function of previous values