/* sample from a mixture distribution */ %let N = 5000; data States(drop=i); call streaminit(12345); array prob [2] _temporary_ (0.7 0.3); do i = 1 to &N; type = rand("Table", of prob[*]); if type=1 then Return = rand("Normal", 0, 3); /* state 1 */ else Return = rand("Normal", 5, 10); /* steta 2 */ output; end; run; proc means data = States mean var std maxdec=5 missing n nmiss ; *Taking mean and std of return in state 1 and 2; var return; class type; output out=Statesmu mean=mu var=var std=std; title 'Mean, Variance and Standard Deviation of the return in 2 states'; run; data work.States; *Getting A,B,C,D from the table; set work.States; Pi = constant("pi"); Res1 = Return + 0.06780; Res2 = Return - 5.21819; UncDen = (0.7/(sqrt(2*3.141592657)*2.98245))*(exp(((-0.5)*((Res1)/2.98245))**2)) + (0.3/(sqrt(2*3.141592657)*9.96359))*(exp(((-0.5)*((Res2)/9.96359))**2)); ConA = (1/(sqrt(2*3.141592657)*2.98245))*(exp(((-0.5)*((Res1)/2.98245))**2)); ConB = (1/(sqrt(2*3.141592657)*9.96359))*(exp(((-0.5)*((Res2)/9.96359))**2)); pi1 = 3400/5000; pi2 = 1600/5000; ConprobA = ConA*pi1; ConprobB = ConB*pi2; PrC = ConprobA/UncDen; PrD = ConprobB/UncDen; Run; Hi, I have a basic question with matrix calculation. If: I have 15 columns variables say 5000 by 15 Need: take only column 15 and inverse it, and then multiply with a 2 by 1 vector. Can anyone give some hint how to do that? I don't know how to take a column and treat it as a vector or combine two columns and treat it as a vector. Example: As shown in the example, Iet's say I need to combine ConprobA and ConprobB as a vector and then multiply with a 2 by 1 vector. Any suggestion would be appreciated. Thank you. ... View more

Hi, I used simulation to generate mixed distribution returns from state 1 & 2, where N =5000, P(state 1) = 70%, P(state 2) = 30%. Then I separate return 1 and return 2, and filling the missing value as 0. Now the question is how to create the average value scalar for each return? I mean create this number for future use. I tried transpose since the average number does not have the same dimension with my variables. However, after I count for the number of each return, R1 has 3400 whil R2 has 1600. Should these be 3500 for R1 and 1500 for R2? I would appreciate any comments and suggestion. Following is my sample and code: /* sample from a mixture distribution */ %let N = 5000; data States(drop=i); call streaminit(12345); array prob [2] _temporary_ (0.7 0.3); do i = 1 to &N; type = rand("Table", of prob[*]); if type=1 then Return = rand("Normal", 0, 3); /* state 1 */ else Return = rand("Normal", 5, 10); /* steta 2 */ output; end; run; data work.States; set work.States; if type = 1 then R1 = Return + 0; else R2 = Return + 0; if R1 =. then R1 =0; if R2 =. then R2 =0; Run; proc transpose data=work.States; run; data want; set data1; array cnt col1-col5000; array cnts cols1-cols5000; do over cnt; if cnt in (0,.) then cnts=1; else cnts=.; allcnts=nmiss(of cols1-cols5000); end; drop col: cols:; run; Here is a short example for the result: N     type     return      R1       R2 1        1        0.58       0.58      0 2        2        0.36         0       0.36 3        1       -0.74      -0.74      0 ... View more

Hi, I am writing a code for sequential simulation with N=5000, state 1 = N(0,3), state 2 = N(5,10), P(state1)=70%, P(state2)=30%. I wrote a code for this simulation and I also want to show how does this simulation look like. However, in my "showing simulation" part, the result given is 10,000 rather than 5,000. Could anyone provide some hint? Thank you. P.S. Does streaminit (12345) and streaminit(123) make any significant difference? Althogh I can see the results differentiate from different streaminits, I don't quite understand how my result is going to be affected, so any further explanation would be appreciate. Here is my code: /* sample from a mixture distribution */ %let N = 5000; data States(drop=i); call streaminit(12345); array prob [2] _temporary_ (0.7 0.3); do i = 1 to &N; type = rand("Table", of prob[*]); if type=1 then Time = rand("Normal", 0, 3); /* state 1 */ else Time = rand("Normal", 5, 10); /* steta 2 */ output; end; run; proc univariate data=States; ods select Histogram; histogram Time / vscale=proportion kernel(lower=0 c=SJPI); run; /* Showing simulation */ data A(drop=i); call streaminit(123); do i = 1 to 5000; S1 = rand("Normal", 0, 3); output; S2 = rand("Normal", 5, 10); output; end; run; proc print data=A; run; ... View more