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    1: #Imach version 0.98q4, July 2015,INED-EUROREVES-Institut de longevite-Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research 25293121), Intel Software 2015
    2: #$Revision: 1.1 $ $Date: 2015/09/16 08:50:20 $
    3: # ADL ANALYSIS USING SABE 2000 2006 AND 2010 by sex basic model with weights
    4: #
    5: title=testV1V2 datafile=data2.txt lastobs=2500 firstpass=1 lastpass=3
    6: ftol=1.e-08 stepm=24 ncovcol=2 nlstate=2 ndeath=1 maxwav=3 mle=1 weight=1
    7: model=1+age+V1+V2.
    8: # Parameters nlstate*nlstate*ncov a12*1 + b12 * age + ...
    9: 12   -8.1682774    0.0847996  0.0  0.2682606 
   10: 13   -8.1383206    0.0766705  0.0  0.4020807 
   11: 21    2.3321826   -0.0492910  0.0 -0.4643334 
   12: 23   -8.2079189    0.0888604  0.0  0.1497600 
   13: # Scales (for hessian or gradient estimation)
   14: 12 0. 0. 0. 0.
   15: 13 0. 0. 0. 0.
   16: 21 0. 0. 0. 0.
   17: 23 0. 0. 0. 0.
   18: # Covariance matrix
   19: #121 Var(a12)
   20: #122 Cov(b12,a12) Var(b12)
   21: #123 Cov(c12,a12) Cov(c12,b12) Var(c12)
   22: #124 Cov(d12,a12) Cov(d12,b12) Cov(d12,c12) Var(d12)
   23: #131 Cov(a13,a12) Cov(a13,b12) Cov(a13,c12) Cov(a13,d12) Var(a13)
   24: #132 Cov(b13,a12) Cov(b13,b12) Cov(b13,c12) Cov(b13,d12) Cov(b13,a13) Var(b13)
   25: #133 Cov(c13,a12) Cov(c13,b12) Cov(c13,c12) Cov(c13,d12) Cov(c13,a13) Cov(c13,b13) Var(c13)
   26: #134 Cov(d13,a12) Cov(d13,b12) Cov(d13,c12) Cov(d13,d12) Cov(d13,a13) Cov(d13,b13) Cov(d13,c13) Var(d13)
   27: #211 Cov(a21,a12) Cov(a21,b12) Cov(a21,c12) Cov(a21,d12) Cov(a21,a13) Cov(a21,b13) Cov(a21,c13) Cov(a21,d13) Var(a21)
   28: #212 Cov(b21,a12) Cov(b21,b12) Cov(b21,c12) Cov(b21,d12) Cov(b21,a13) Cov(b21,b13) Cov(b21,c13) Cov(b21,d13) Cov(b21,a21) Var(b21)
   29: #213 Cov(c21,a12) Cov(c21,b12) Cov(c21,c12) Cov(c21,d12) Cov(c21,a13) Cov(c21,b13) Cov(c21,c13) Cov(c21,d13) Cov(c21,a21) Cov(c21,b21) Var(c21)
   30: #214 Cov(d21,a12) Cov(d21,b12) Cov(d21,c12) Cov(d21,d12) Cov(d21,a13) Cov(d21,b13) Cov(d21,c13) Cov(d21,d13) Cov(d21,a21) Cov(d21,b21) Cov(d21,c21) Var(d21)
   31: #231 Cov(a23,a12) Cov(a23,b12) Cov(a23,c12) Cov(a23,d12) Cov(a23,a13) Cov(a23,b13) Cov(a23,c13) Cov(a23,d13) Cov(a23,a21) Cov(a23,b21) Cov(a23,c21) Cov(a23,d21) Var(a23)
   32: #232 Cov(b23,a12) Cov(b23,b12) Cov(b23,c12) Cov(b23,d12) Cov(b23,a13) Cov(b23,b13) Cov(b23,c13) Cov(b23,d13) Cov(b23,a21) Cov(b23,b21) Cov(b23,c21) Cov(b23,d21) Cov(b23,a23) Var(b23)
   33: #233 Cov(c23,a12) Cov(c23,b12) Cov(c23,c12) Cov(c23,d12) Cov(c23,a13) Cov(c23,b13) Cov(c23,c13) Cov(c23,d13) Cov(c23,a21) Cov(c23,b21) Cov(c23,c21) Cov(c23,d21) Cov(c23,a23) Cov(c23,b23) Var(c23)
   34: #234 Cov(d23,a12) Cov(d23,b12) Cov(d23,c12) Cov(d23,d12) Cov(d23,a13) Cov(d23,b13) Cov(d23,c13) Cov(d23,d13) Cov(d23,a21) Cov(d23,b21) Cov(d23,c21) Cov(d23,d21) Cov(d23,a23) Cov(d23,b23) Cov(d23,c23) Var(d23)
   35: 121 0.
   36: 122 0. 0.
   37: 123 0. 0. 0.
   38: 124 0. 0. 0. 0.
   39: 131 0. 0. 0. 0. 0.
   40: 132 0. 0. 0. 0. 0. 0.
   41: 133 0. 0. 0. 0. 0. 0. 0.
   42: 134 0. 0. 0. 0. 0. 0. 0. 0.
   43: 211 0. 0. 0. 0. 0. 0. 0. 0. 0.
   44: 212 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
   45: 213 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
   46: 214 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
   47: 231 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
   48: 232 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
   49: 233 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
   50: 234 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
   51: # agemin agemax for life expectancy, bage fage (if mle==0 ie no data nor Max likelihood).
   52: agemin=60 agemax=95 bage=55 fage=95
   53: # Observed prevalence period
   54: begin-prev-date=1/1/2000 end-prev-date=12/12/2006 estepm=1
   55: # Health expectancies computed from stable (period) prevalence (pop_based=0) or population based (1)
   56: pop_based=1
   57: # Prevalence forecasting
   58: starting-proj-date=1/1/2007 final-proj-date=12/12/2011 mov_average=0