title 'car sales example';
data carsales;
input Initial	prob	NFCC	ctrNFCC;
cards;
0.0	0.25	3.22	-0.3438686663342411
0.0	0.25	4.02	1.190403129914463
1.0	0.8	3.7	0.5766944114149821
0.0	0.5	4.34	1.8041118484139458
1.0	0.25	3.6	0.38491043688389376
1.0	0.5	4.1	1.3438303095393338
0.0	0.5	4.39	1.9000038356794897
1.0	0.25	4.18	1.4972574891642045
1.0	0.25	3.46	0.11641287254037005
0.0	0.5	4.26	1.6506846687890753
1.0	0.5	3.88	0.9219055655709403
0.0	0.2	3.61	0.40408883433700216
0.0	0.5	3.73	0.6342296037743081
1.0	0.25	2.66	-1.417858923708335
1.0	0.5	2.75	-1.245253346630356
0.0	0.3	2.97	-0.8233286026619616
0.0	0.5	3.23	-0.3246902688811327
1.0	0.25	3.68	0.5383376165087644
0.0	0.25	2.86	-1.0342909746461593
0.0	0.36	3.83	0.8260135783053966
1.0	0.18	3.59	0.3657320394307845
1.0	0.1	3.36	-0.0753711019907183
0.0	0.5	3.45	0.09723447508726164
0.0	0.25	2.9	-0.9575773848337239
0.0	0.25	3.08	-0.6123662306777649
0.0	0.5	3.96	1.075332745195811
1.0	0.5	3.43	0.05887768018104397
1.0	0.2	4.29	1.708219861148402
0.0	0.5	3.59	0.3657320394307845
1.0	0.25	3.56	0.3081968470714584
1.0	0.25	3.74	0.6534080012274175
1.0	0.33	3.93	1.0177975528364849
1.0	0.4	3.19	-0.4014038586935681
0.0	0.5	3.33	-0.1329062943500444
1.0	0.25	3.03	-0.7082582179433095
1.0	0.5	2.7	-1.3411453338958996
1.0	0.2	3.43	0.05887768018104397
1.0	0.25	3.7	0.5766944114149821
0.0	0.25	3.02	-0.7274366153964179
0.0	0.25	3.29	-0.20961988416247973
1.0	0.25	4.5	2.110966207663687
0.0	0.2	4.0	1.1520463350082464
0.0	0.5	3.38	-0.037014307084500625
1.0	0.5	3.07	-0.6315446281308741
1.0	0.5	1.75	-3.1630930919412377
0.0	0.25	2.5	-1.7247132829580765
1.0	0.25	2.99	-0.7849718077557439
0.0	0.3	3.01	-0.7466150128495271
0.0	0.5	2.77	-1.2068965517241383
1.0	0.25	4.09	1.3246519120862255
1.0	0.32	2.96	-0.8425070001150708
0.0	0.5	3.27	-0.2479766790686974
0.0	0.2	3.09	-0.5931878332246564
1.0	0.25	4.05	1.24793832227379
0.0	0.25	3.3	-0.1904414867093713
1.0	0.5	3.18	-0.4205822561466765
0.0	0.46	3.9	0.960262360477158
1.0	0.5	3.68	0.5383376165087644
0.0	0.5	3.41	0.020520885274826303
1.0	0.9	3.65	0.4808024241494375
0.0	0.2	3.37	-0.05619270453760904
0.0	0.5	3.39	-0.017835909631391367
0.0	0.25	2.12	-2.4534923861762112
1.0	0.25	3.11	-0.5548310383184387
0.0	0.5	3.47	0.1355912699934793
1.0	0.35	3.79	0.7492999884929612
0.0	0.15	2.38	-1.9548540523953826
0.0	0.25	2.4	-1.9164972574891648
0.0	0.45	3.11	-0.5548310383184387
1.0	0.5	2.07	-2.549384373441756
1.0	0.25	2.89	-0.9767557822868322
1.0	0.7	4.21	1.5547926815235313
0.0	0.4	3.87	0.9027271681178319
0.0	0.1	2.87	-1.01511257719305
1.0	0.4	2.56	-1.6096428982394235
1.0	0.5	4.36	1.8424686433201645
0.0	0.5	3.67	0.5191592190556552
1.0	0.25	3.75	0.6725863986805258
1.0	0.8	4.12	1.3821871044455523
1.0	0.5	3.97	1.0945111426489202
0.0	0.6	2.82	-1.1110045644585946
0.0	0.5	2.94	-0.8808637950212885
1.0	0.4	3.08	-0.6123662306777649
0.0	0.5	3.08	-0.6123662306777649
1.0	0.15	3.89	0.9410839630240495
0.0	0.5	3.31	-0.17126308925626205
1.0	0.85	3.5	0.1931264623528054
0.0	0.5	4.28	1.6890414636952937
1.0	0.2	3.58	0.3465536419776761
1.0	0.33	2.65	-1.4370373211614444
0.0	0.2	3.45	0.09723447508726164
1.0	0.1	3.21	-0.3630470637873504
1.0	0.1	4.25	1.6315062713359667
1.0	0.25	3.46	0.11641287254037005
1.0	0.5	3.22	-0.3438686663342411
1.0	0.4	3.72	0.6150512063211998
0.0	0.2	3.58	0.3465536419776761
0.0	0.25	2.71	-1.3219669364427913
0.0	0.5	3.67	0.5191592190556552
0.0	0.4	3.95	1.0561543477427027
0.0	0.2	2.95	-0.8616853975681793
0.0	0.25	3.56	0.3081968470714584
0.0	0.5	3.73	0.6342296037743081
1.0	0.25	3.31	-0.17126308925626205
1.0	0.8	4.57	2.2452149898354494
0.0	0.2	2.96	-0.8425070001150708
0.0	0.5	3.57	0.32737524452456684
1.0	0.2	3.15	-0.4781174485060034
1.0	0.5	4.01	1.1712247324613547
0.0	0.5	3.73	0.6342296037743081
0.0	0.5	2.54	-1.647999693145641
0.0	0.9	3.12	-0.5356526408653295
0.0	0.5	3.48	0.15476966744658774
0.0	0.3	3.76	0.6917647961336343
0.0	0.6	2.6	-1.5329293084269882
0.0	0.15	3.8	0.7684783859460697
0.0	0.25	2.85	-1.0534693720992676
1.0	0.5	3.84	0.8451919757585049
1.0	0.2	3.66	0.49998082160254675
0.0	0.98	4.21	1.5547926815235313
1.0	0.25	3.63	0.44244562924321984
1.0	0.5	3.6	0.38491043688389376
0.0	0.5	3.35	-0.0945494994438267
1.0	0.999	3.63	0.44244562924321984
0.0	0.5	3.99	1.1328679375551378
0.0	0.5	2.37	-1.974032449848491
0.0	0.2	3.12	-0.5356526408653295
1.0	0.6	3.54	0.26984005216524076
1.0	0.6	3.17	-0.4397606535997857
0.0	0.5	3.12	-0.5356526408653295
1.0	0.2	3.63	0.44244562924321984
1.0	0.25	3.26	-0.26715507652180664
1.0	0.5	2.87	-1.01511257719305
0.0	0.5	2.97	-0.8233286026619616
1.0	0.25	3.29	-0.20961988416247973
1.0	0.25	3.55	0.28901844961834916
1.0	0.5	3.13	-0.516474243412221
0.0	0.5	3.05	-0.6699014230370918
1.0	0.2	3.0	-0.7657934103026355
0.0	0.5	3.51	0.21230485980591382
0.0	0.5	2.87	-1.01511257719305
0.0	0.5	3.28	-0.228798281615589
0.0	0.4	4.26	1.6506846687890753
1.0	0.37	3.17	-0.4397606535997857
0.0	0.75	3.17	-0.4397606535997857
1.0	0.25	2.88	-0.9959341797399416
0.0	0.5	3.34	-0.11372789689693596
1.0	0.25	3.55	0.28901844961834916
1.0	0.5	2.49	-1.7438916804111848
0.0	0.4	3.14	-0.4972958459591118
1.0	0.13	3.55	0.28901844961834916
1.0	0.25	3.31	-0.17126308925626205
1.0	0.5	3.56	0.3081968470714584
1.0	0.25	3.77	0.7109431935867435
0.0	0.5	3.95	1.0561543477427027
;
run;
;
proc nlmixed data = carsales tech = trureg hess cov itdetails;
*Starting values;
parms B01 = -0.3, G01 = -1.0, Q1 = 0.25, Q11 = -0.6, Q2 = -0.7, Q12 = 0.5, Q13 = -0.5;
title 'prob on Initial and NFCC (example 3 code), mixture model';
*This is the final model;
*Initial is the Car versus Salesperson prime;
*NFCC is the mean of the need for certainty and need for closure scores;
*ctrNFCC is NFCC transformed to a z-score variable;
*Composition submodel;
C1 = EXP(Q1 + Q11*Initial + Q12*ctrNFCC + Q13*Initial*ctrNFCC)/(1+EXP(Q1 + Q11*Initial + Q12*ctrNFCC + Q13*Initial*ctrNFCC)+EXP(Q2 - Q11*Initial - Q12*ctrNFCC - Q13*Initial*ctrNFCC));
C2 = EXP(Q2 - Q11*Initial - Q12*ctrNFCC - Q13*Initial*ctrNFCC)/(1+EXP(Q1 + Q11*Initial + Q12*ctrNFCC + Q13*Initial*ctrNFCC)+EXP(Q2 - Q11*Initial - Q12*ctrNFCC - Q13*Initial*ctrNFCC));
*Location submodel;
M1 = EXP(B01)/(1+EXP(B01));
M2 = 1/2;
M3 = 1/4;
*Dispersion submodel;
PHI1 = EXP(-G01);
D = .01;
ll = log((1-C1-C2)*PDF('BETA',prob, M1*PHI1, PHI1 - M1*PHI1,0,1)+C1* PDF('BETA',prob,1,1,M2-D,M2+D)+C2* PDF('BETA',prob,1,1,M3-D,M3+D));
model prob ~ general(ll);
;
run;