version 1.335, 2022/08/31 08:23:16
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version 1.336, 2022/08/31 09:52:36
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/* $Id$ |
/* $Id$ |
$State$ |
$State$ |
$Log$ |
$Log$ |
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Revision 1.336 2022/08/31 09:52:36 brouard |
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*** empty log message *** |
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Revision 1.335 2022/08/31 08:23:16 brouard |
Revision 1.335 2022/08/31 08:23:16 brouard |
Summary: improvements... |
Summary: improvements... |
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Line 3508 double **matprod2(double **out, double *
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Line 3511 double **matprod2(double **out, double *
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double ***hpxij(double ***po, int nhstepm, double age, int hstepm, double *x, int nlstate, int stepm, double **oldm, double **savm, int ij, int nres ) |
double ***hpxij(double ***po, int nhstepm, double age, int hstepm, double *x, int nlstate, int stepm, double **oldm, double **savm, int ij, int nres ) |
{ |
{ |
/* Computes the transition matrix starting at age 'age' and dummies values in each resultline (loop on ij to find the corresponding combination) to over |
/* Already optimized with precov. |
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Computes the transition matrix starting at age 'age' and dummies values in each resultline (loop on ij to find the corresponding combination) to over |
'nhstepm*hstepm*stepm' months (i.e. until |
'nhstepm*hstepm*stepm' months (i.e. until |
age (in years) age+nhstepm*hstepm*stepm/12) by multiplying |
age (in years) age+nhstepm*hstepm*stepm/12) by multiplying |
nhstepm*hstepm matrices. |
nhstepm*hstepm matrices. |
Line 3839 double ***hbxij(double ***po, int nhstep
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Line 3843 double ***hbxij(double ***po, int nhstep
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/*************** log-likelihood *************/ |
/*************** log-likelihood *************/ |
double func( double *x) |
double func( double *x) |
{ |
{ |
int i, ii, j, k, mi, d, kk; |
int i, ii, j, k, mi, d, kk, kf=0; |
int ioffset=0; |
int ioffset=0; |
double l, ll[NLSTATEMAX+1], cov[NCOVMAX+1]; |
double l, ll[NLSTATEMAX+1], cov[NCOVMAX+1]; |
double **out; |
double **out; |
double lli; /* Individual log likelihood */ |
double lli; /* Individual log likelihood */ |
int s1, s2; |
int s1, s2; |
int iv=0, iqv=0, itv=0, iqtv=0 ; /* Index of varying covariate, fixed quantitative cov, time varying covariate, quantitative time varying covariate */ |
int iv=0, iqv=0, itv=0, iqtv=0 ; /* Index of varying covariate, fixed quantitative cov, time varying covariate, quantitative time varying covariate */ |
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double bbh, survp; |
double bbh, survp; |
long ipmx; |
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double agexact; |
double agexact; |
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double agebegin, ageend; |
/*extern weight */ |
/*extern weight */ |
/* We are differentiating ll according to initial status */ |
/* We are differentiating ll according to initial status */ |
/* for (i=1;i<=npar;i++) printf("%f ", x[i]);*/ |
/* for (i=1;i<=npar;i++) printf("%f ", x[i]);*/ |
Line 3871 double func( double *x)
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Line 3876 double func( double *x)
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*/ |
*/ |
ioffset=2+nagesqr ; |
ioffset=2+nagesqr ; |
/* Fixed */ |
/* Fixed */ |
for (k=1; k<=ncovf;k++){ /* For each fixed covariate dummu or quant or prod */ |
for (kf=1; kf<=ncovf;kf++){ /* For each fixed covariate dummu or quant or prod */ |
/* # V1=sex, V2=raedyrs Quant Fixed, State=livarnb4..livarnb11, V3=iadl4..iald11, V4=adlw4..adlw11, V5=r4bmi..r11bmi */ |
/* # V1=sex, V2=raedyrs Quant Fixed, State=livarnb4..livarnb11, V3=iadl4..iald11, V4=adlw4..adlw11, V5=r4bmi..r11bmi */ |
/* V5+V4+V3+V4*V3+V5*age+V2+V1*V2+V1*age+V1 */ |
/* V5+V4+V3+V4*V3+V5*age+V2+V1*V2+V1*age+V1 */ |
/* TvarF[1]=Tvar[6]=2, TvarF[2]=Tvar[7]=7, TvarF[3]=Tvar[9]=1 ID of fixed covariates or product V2, V1*V2, V1 */ |
/* TvarF[1]=Tvar[6]=2, TvarF[2]=Tvar[7]=7, TvarF[3]=Tvar[9]=1 ID of fixed covariates or product V2, V1*V2, V1 */ |
/* TvarFind; TvarFind[1]=6, TvarFind[2]=7, TvarFind[3]=9 *//* Inverse V2(6) is first fixed (single or prod) */ |
/* TvarFind; TvarFind[1]=6, TvarFind[2]=7, TvarFind[3]=9 *//* Inverse V2(6) is first fixed (single or prod) */ |
cov[ioffset+TvarFind[k]]=covar[Tvar[TvarFind[k]]][i];/* V5+V4+V3+V4*V3+V5*age+V2+V1*V2+V1*age+V1, only V1 is fixed (TvarFind[1]=6)*/ |
cov[ioffset+TvarFind[kf]]=covar[Tvar[TvarFind[kf]]][i];/* V5+V4+V3+V4*V3+V5*age+V2+V1*V2+V1*age+V1, only V1 is fixed (TvarFind[1]=6)*/ |
/* V1*V2 (7) TvarFind[2]=7, TvarFind[3]=9 */ |
/* V1*V2 (7) TvarFind[2]=7, TvarFind[3]=9 */ |
} |
} |
/* In model V2+V1*V4+age*V3+V3*V2 Tvar[1] is V2, Tvar[2=V1*V4] |
/* In model V2+V1*V4+age*V3+V3*V2 Tvar[1] is V2, Tvar[2=V1*V4] |
Line 3891 double func( double *x)
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Line 3896 double func( double *x)
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But if the variable is not in the model TTvar[iv] is the real variable effective in the model: |
But if the variable is not in the model TTvar[iv] is the real variable effective in the model: |
meaning that decodemodel should be used cotvar[mw[mi+1][i]][TTvar[iv]][i] |
meaning that decodemodel should be used cotvar[mw[mi+1][i]][TTvar[iv]][i] |
*/ |
*/ |
for(mi=1; mi<= wav[i]-1; mi++){ |
for(mi=1; mi<= wav[i]-1; mi++){ /* Varying with waves */ |
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/* Wave varying (but not age varying) */ |
for(k=1; k <= ncovv ; k++){ /* Varying covariates in the model (single and product but no age )"V5+V4+V3+V4*V3+V5*age+V1*age+V1" +TvarVind 1,2,3,4(V4*V3) Tvar[1]@7{5, 4, 3, 6, 5, 1, 1 ; 6 because the created covar is after V5 and is 6, minus 1+1, 3,2,1,4 positions in cotvar*/ |
for(k=1; k <= ncovv ; k++){ /* Varying covariates in the model (single and product but no age )"V5+V4+V3+V4*V3+V5*age+V1*age+V1" +TvarVind 1,2,3,4(V4*V3) Tvar[1]@7{5, 4, 3, 6, 5, 1, 1 ; 6 because the created covar is after V5 and is 6, minus 1+1, 3,2,1,4 positions in cotvar*/ |
/* cov[ioffset+TvarVind[k]]=cotvar[mw[mi][i]][Tvar[TvarVind[k]]][i]; but where is the crossproduct? */ |
/* cov[ioffset+TvarVind[k]]=cotvar[mw[mi][i]][Tvar[TvarVind[k]]][i]; but where is the crossproduct? */ |
cov[ioffset+TvarVind[k]]=cotvar[mw[mi][i]][Tvar[TvarVind[k]]-ncovcol-nqv][i]; |
cov[ioffset+TvarVind[k]]=cotvar[mw[mi][i]][Tvar[TvarVind[k]]-ncovcol-nqv][i]; |
Line 3901 double func( double *x)
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Line 3907 double func( double *x)
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oldm[ii][j]=(ii==j ? 1.0 : 0.0); |
oldm[ii][j]=(ii==j ? 1.0 : 0.0); |
savm[ii][j]=(ii==j ? 1.0 : 0.0); |
savm[ii][j]=(ii==j ? 1.0 : 0.0); |
} |
} |
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agebegin=agev[mw[mi][i]][i]; /* Age at beginning of effective wave */ |
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ageend=agev[mw[mi][i]][i] + (dh[mi][i])*stepm/YEARM; /* Age at end of effective wave and at the end of transition */ |
for(d=0; d<dh[mi][i]; d++){ |
for(d=0; d<dh[mi][i]; d++){ |
newm=savm; |
newm=savm; |
agexact=agev[mw[mi][i]][i]+d*stepm/YEARM; |
agexact=agev[mw[mi][i]][i]+d*stepm/YEARM; |
Line 3991 double func( double *x)
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Line 4000 double func( double *x)
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/*survp += out[s1][j]; */ |
/*survp += out[s1][j]; */ |
lli= log(survp); |
lli= log(survp); |
} |
} |
else if (s2==-4) { |
/* else if (s2==-4) { */ |
for (j=3,survp=0. ; j<=nlstate; j++) |
/* for (j=3,survp=0. ; j<=nlstate; j++) */ |
survp += (1.+bbh)*out[s1][j]- bbh*savm[s1][j]; |
/* survp += (1.+bbh)*out[s1][j]- bbh*savm[s1][j]; */ |
lli= log(survp); |
/* lli= log(survp); */ |
} |
/* } */ |
else if (s2==-5) { |
/* else if (s2==-5) { */ |
for (j=1,survp=0. ; j<=2; j++) |
/* for (j=1,survp=0. ; j<=2; j++) */ |
survp += (1.+bbh)*out[s1][j]- bbh*savm[s1][j]; |
/* survp += (1.+bbh)*out[s1][j]- bbh*savm[s1][j]; */ |
lli= log(survp); |
/* lli= log(survp); */ |
} |
/* } */ |
else{ |
else{ |
lli= log((1.+bbh)*out[s1][s2]- bbh*savm[s1][s2]); /* linear interpolation */ |
lli= log((1.+bbh)*out[s1][s2]- bbh*savm[s1][s2]); /* linear interpolation */ |
/* lli= (savm[s1][s2]>(double)1.e-8 ?log((1.+bbh)*out[s1][s2]- bbh*(savm[s1][s2])):log((1.+bbh)*out[s1][s2]));*/ /* linear interpolation */ |
/* lli= (savm[s1][s2]>(double)1.e-8 ?log((1.+bbh)*out[s1][s2]- bbh*(savm[s1][s2])):log((1.+bbh)*out[s1][s2]));*/ /* linear interpolation */ |
Line 4198 double funcone( double *x)
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Line 4207 double funcone( double *x)
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for(k=1; k<=nlstate; k++) ll[k]=0.; |
for(k=1; k<=nlstate; k++) ll[k]=0.; |
ioffset=0; |
ioffset=0; |
for (i=1,ipmx=0, sw=0.; i<=imx; i++){ |
for (i=1,ipmx=0, sw=0.; i<=imx; i++){ |
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/* Computes the values of the ncovmodel covariates of the model |
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depending if the covariates are fixed or varying (age dependent) and stores them in cov[] |
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Then computes with function pmij which return a matrix p[i][j] giving the elementary probability |
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to be observed in j being in i according to the model. |
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*/ |
/* ioffset=2+nagesqr+cptcovage; */ |
/* ioffset=2+nagesqr+cptcovage; */ |
ioffset=2+nagesqr; |
ioffset=2+nagesqr; |
/* Fixed */ |
/* Fixed */ |
Line 4215 double funcone( double *x)
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Line 4229 double funcone( double *x)
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/* cov[2+9]=covar[Tvar[9]][i]; */ |
/* cov[2+9]=covar[Tvar[9]][i]; */ |
/* cov[2+9]=covar[1][i]; V1 */ |
/* cov[2+9]=covar[1][i]; V1 */ |
} |
} |
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/* In model V2+V1*V4+age*V3+V3*V2 Tvar[1] is V2, Tvar[2=V1*V4] |
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is 5, Tvar[3=age*V3] should not be computed because of age Tvar[4=V3*V2]=6 |
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has been calculated etc */ |
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/* For an individual i, wav[i] gives the number of effective waves */ |
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/* We compute the contribution to Likelihood of each effective transition |
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mw[mi][i] is real wave of the mi th effectve wave */ |
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/* Then statuses are computed at each begin and end of an effective wave s1=s[ mw[mi][i] ][i]; |
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s2=s[mw[mi+1][i]][i]; |
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And the iv th varying covariate is the cotvar[mw[mi+1][i]][iv][i] |
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But if the variable is not in the model TTvar[iv] is the real variable effective in the model: |
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meaning that decodemodel should be used cotvar[mw[mi+1][i]][TTvar[iv]][i] |
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*/ |
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/* This part may be useless now because everythin should be in covar */ |
/* for (k=1; k<=nqfveff;k++){ /\* Simple and product fixed Quantitative covariates without age* products *\/ */ |
/* for (k=1; k<=nqfveff;k++){ /\* Simple and product fixed Quantitative covariates without age* products *\/ */ |
/* cov[++ioffset]=coqvar[TvarFQ[k]][i];/\* V5+V4+V3+V4*V3+V5*age+V2+V1*V2+V1*age+V1, only V2 and V1*V2 is fixed (k=6 and 7?)*\/ */ |
/* cov[++ioffset]=coqvar[TvarFQ[k]][i];/\* V5+V4+V3+V4*V3+V5*age+V2+V1*V2+V1*age+V1, only V2 and V1*V2 is fixed (k=6 and 7?)*\/ */ |
/* } */ |
/* } */ |
Line 4272 double funcone( double *x)
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Line 4299 double funcone( double *x)
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savm=oldm; |
savm=oldm; |
oldm=newm; |
oldm=newm; |
} /* end mult */ |
} /* end mult */ |
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/*lli=log(out[s[mw[mi][i]][i]][s[mw[mi+1][i]][i]]);*/ /* Original formula */ |
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/* But now since version 0.9 we anticipate for bias at large stepm. |
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* If stepm is larger than one month (smallest stepm) and if the exact delay |
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* (in months) between two waves is not a multiple of stepm, we rounded to |
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* the nearest (and in case of equal distance, to the lowest) interval but now |
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* we keep into memory the bias bh[mi][i] and also the previous matrix product |
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* (i.e to dh[mi][i]-1) saved in 'savm'. Then we inter(extra)polate the |
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* probability in order to take into account the bias as a fraction of the way |
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* from savm to out if bh is negative or even beyond if bh is positive. bh varies |
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* -stepm/2 to stepm/2 . |
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* For stepm=1 the results are the same as for previous versions of Imach. |
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* For stepm > 1 the results are less biased than in previous versions. |
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*/ |
s1=s[mw[mi][i]][i]; |
s1=s[mw[mi][i]][i]; |
s2=s[mw[mi+1][i]][i]; |
s2=s[mw[mi+1][i]][i]; |
/* if(s2==-1){ */ |
/* if(s2==-1){ */ |
Line 6234 void concatwav(int wav[], int **dh, int
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Line 6273 void concatwav(int wav[], int **dh, int
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/* Covariances of health expectancies eij and of total life expectancies according |
/* Covariances of health expectancies eij and of total life expectancies according |
to initial status i, ei. . |
to initial status i, ei. . |
*/ |
*/ |
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/* Very time consuming function, but already optimized with precov */ |
int i, j, nhstepm, hstepm, h, nstepm, k, cptj, cptj2, i2, j2, ij, ji; |
int i, j, nhstepm, hstepm, h, nstepm, k, cptj, cptj2, i2, j2, ij, ji; |
int nhstepma, nstepma; /* Decreasing with age */ |
int nhstepma, nstepma; /* Decreasing with age */ |
double age, agelim, hf; |
double age, agelim, hf; |
Line 11608 int back_prevalence_limit(double *p, dou
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Line 11648 int back_prevalence_limit(double *p, dou
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int hPijx(double *p, int bage, int fage){ |
int hPijx(double *p, int bage, int fage){ |
/*------------- h Pij x at various ages ------------*/ |
/*------------- h Pij x at various ages ------------*/ |
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/* to be optimized with precov */ |
int stepsize; |
int stepsize; |
int agelim; |
int agelim; |
int hstepm; |
int hstepm; |
Line 11685 int hPijx(double *p, int bage, int fage)
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Line 11725 int hPijx(double *p, int bage, int fage)
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int hBijx(double *p, int bage, int fage, double ***prevacurrent){ |
int hBijx(double *p, int bage, int fage, double ***prevacurrent){ |
/*------------- h Bij x at various ages ------------*/ |
/*------------- h Bij x at various ages ------------*/ |
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/* To be optimized with precov */ |
int stepsize; |
int stepsize; |
/* int agelim; */ |
/* int agelim; */ |
int ageminl; |
int ageminl; |
Line 12447 Please run with mle=-1 to get a correct
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Line 12487 Please run with mle=-1 to get a correct
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mint=matrix(1,maxwav,firstobs,lastobs); |
mint=matrix(1,maxwav,firstobs,lastobs); |
anint=matrix(1,maxwav,firstobs,lastobs); |
anint=matrix(1,maxwav,firstobs,lastobs); |
s=imatrix(1,maxwav+1,firstobs,lastobs); /* s[i][j] health state for wave i and individual j */ |
s=imatrix(1,maxwav+1,firstobs,lastobs); /* s[i][j] health state for wave i and individual j */ |
printf("BUG ncovmodel=%d NCOVMAX=%d 2**ncovmodel=%f BUG\n",ncovmodel,NCOVMAX,pow(2,ncovmodel)); |
/* printf("BUG ncovmodel=%d NCOVMAX=%d 2**ncovmodel=%f BUG\n",ncovmodel,NCOVMAX,pow(2,ncovmodel)); */ |
tab=ivector(1,NCOVMAX); |
tab=ivector(1,NCOVMAX); |
ncodemax=ivector(1,NCOVMAX); /* Number of code per covariate; if O and 1 only, 2**ncov; V1+V2+V3+V4=>16 */ |
ncodemax=ivector(1,NCOVMAX); /* Number of code per covariate; if O and 1 only, 2**ncov; V1+V2+V3+V4=>16 */ |
ncodemaxwundef=ivector(1,NCOVMAX); /* Number of code per covariate; if - 1 O and 1 only, 2**ncov; V1+V2+V3+V4=>16 */ |
ncodemaxwundef=ivector(1,NCOVMAX); /* Number of code per covariate; if - 1 O and 1 only, 2**ncov; V1+V2+V3+V4=>16 */ |
Line 13761 Please run with mle=-1 to get a correct
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Line 13801 Please run with mle=-1 to get a correct
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/*---------- State-specific expectancies and variances ------------*/ |
/*---------- State-specific expectancies and variances ------------*/ |
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/* Should be moved in a function */ |
strcpy(filerest,"T_"); |
strcpy(filerest,"T_"); |
strcat(filerest,fileresu); |
strcat(filerest,fileresu); |
if((ficrest=fopen(filerest,"w"))==NULL) { |
if((ficrest=fopen(filerest,"w"))==NULL) { |