--- imach/src/imach.c 2002/11/20 17:35:59 1.63 +++ imach/src/imach.c 2002/11/21 08:46:21 1.64 @@ -1,4 +1,4 @@ -/* $Id: imach.c,v 1.63 2002/11/20 17:35:59 lievre Exp $ +/* $Id: imach.c,v 1.64 2002/11/21 08:46:21 lievre Exp $ Interpolated Markov Chain Short summary of the programme: @@ -944,13 +944,10 @@ double func( double *x) for (kk=1; kk<=cptcovage;kk++) { cov[Tage[kk]+2]=covar[Tvar[Tage[kk]]][i]*cov[2]; } - out=matprod2(newm,oldm,1,nlstate+ndeath,1,nlstate+ndeath, 1,nlstate+ndeath,pmij(pmmij,cov,ncovmodel,x,nlstate)); savm=oldm; oldm=newm; - - } /* end mult */ /*lli=log(out[s[mw[mi][i]][i]][s[mw[mi+1][i]][i]]);*/ /* Original formula */ @@ -968,13 +965,113 @@ double func( double *x) */ s1=s[mw[mi][i]][i]; s2=s[mw[mi+1][i]][i]; - bbh=(double)bh[mi][i]/(double)stepm; - - /* lli= (savm[s1][s2]>(double)1.e-8 ?(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]):log((1.+bbh)*out[s1][s2]));*/ - + bbh=(double)bh[mi][i]/(double)stepm; + /* bias is positive if real duration + * is higher than the multiple of stepm and negative otherwise. + */ + /* lli= (savm[s1][s2]>1.e-8 ?(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]):log((1.+bbh)*out[s1][s2]));*/ + 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=(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]);*/ + /*if(lli ==000.0)*/ + /*printf("bbh= %f lli=%f savm=%f out=%f %d\n",bbh,lli,savm[s1][s2], out[s[mw[mi][i]][i]][s[mw[mi+1][i]][i]],i); */ + ipmx +=1; + sw += weight[i]; + ll[s[mw[mi][i]][i]] += 2*weight[i]*lli; + } /* end of wave */ + } /* end of individual */ + } else if(mle==2){ + for (i=1,ipmx=0, sw=0.; i<=imx; i++){ + for (k=1; k<=cptcovn;k++) cov[2+k]=covar[Tvar[k]][i]; + for(mi=1; mi<= wav[i]-1; mi++){ + for (ii=1;ii<=nlstate+ndeath;ii++) + for (j=1;j<=nlstate+ndeath;j++){ + oldm[ii][j]=(ii==j ? 1.0 : 0.0); + savm[ii][j]=(ii==j ? 1.0 : 0.0); + } + for(d=0; d<=dh[mi][i]; d++){ + newm=savm; + cov[2]=agev[mw[mi][i]][i]+d*stepm/YEARM; + for (kk=1; kk<=cptcovage;kk++) { + cov[Tage[kk]+2]=covar[Tvar[Tage[kk]]][i]*cov[2]; + } + out=matprod2(newm,oldm,1,nlstate+ndeath,1,nlstate+ndeath, + 1,nlstate+ndeath,pmij(pmmij,cov,ncovmodel,x,nlstate)); + savm=oldm; + oldm=newm; + } /* end mult */ + + /*lli=log(out[s[mw[mi][i]][i]][s[mw[mi+1][i]][i]]);*/ /* Original formula */ + /* But now since version 0.9 we anticipate for bias and large stepm. + * If stepm is larger than one month (smallest stepm) and if the exact delay + * (in months) between two waves is not a multiple of stepm, we rounded to + * the nearest (and in case of equal distance, to the lowest) interval but now + * we keep into memory the bias bh[mi][i] and also the previous matrix product + * (i.e to dh[mi][i]-1) saved in 'savm'. The we inter(extra)polate the + * probability in order to take into account the bias as a fraction of the way + * from savm to out if bh is neagtive or even beyond if bh is positive. bh varies + * -stepm/2 to stepm/2 . + * For stepm=1 the results are the same as for previous versions of Imach. + * For stepm > 1 the results are less biased than in previous versions. + */ + s1=s[mw[mi][i]][i]; + s2=s[mw[mi+1][i]][i]; + bbh=(double)bh[mi][i]/(double)stepm; + /* bias is positive if real duration + * is higher than the multiple of stepm and negative otherwise. + */ 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]>1.e-8 ?(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]):log((1.-bbh)*out[s1][s2]));*/ + /* lli= (savm[s1][s2]>1.e-8 ?(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]):log((1.+bbh)*out[s1][s2]));*/ + /*lli= (savm[s1][s2]>1.e-8 ?(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]):log((1.-+bh)*out[s1][s2])); */ /* exponential interpolation */ + /*lli=(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]);*/ + /*if(lli ==000.0)*/ + /*printf("bbh= %f lli=%f savm=%f out=%f %d\n",bbh,lli,savm[s1][s2], out[s[mw[mi][i]][i]][s[mw[mi+1][i]][i]],i); */ + ipmx +=1; + sw += weight[i]; + ll[s[mw[mi][i]][i]] += 2*weight[i]*lli; + } /* end of wave */ + } /* end of individual */ + } else if(mle==3){ /* exponential inter-extrapolation */ + for (i=1,ipmx=0, sw=0.; i<=imx; i++){ + for (k=1; k<=cptcovn;k++) cov[2+k]=covar[Tvar[k]][i]; + for(mi=1; mi<= wav[i]-1; mi++){ + for (ii=1;ii<=nlstate+ndeath;ii++) + for (j=1;j<=nlstate+ndeath;j++){ + oldm[ii][j]=(ii==j ? 1.0 : 0.0); + savm[ii][j]=(ii==j ? 1.0 : 0.0); + } + for(d=0; d 1 the results are less biased than in previous versions. + */ + s1=s[mw[mi][i]][i]; + s2=s[mw[mi+1][i]][i]; + bbh=(double)bh[mi][i]/(double)stepm; + /* bias is positive if real duration + * is higher than the multiple of stepm and negative otherwise. + */ + /* 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]>1.e-8 ?(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]):log((1.+bbh)*out[s1][s2])); /* exponential inter-extrapolation */ /*lli=(1.+bbh)*log(out[s1][s2])- bbh*log(savm[s1][s2]);*/ /*if(lli ==000.0)*/ /*printf("bbh= %f lli=%f savm=%f out=%f %d\n",bbh,lli,savm[s1][s2], out[s[mw[mi][i]][i]][s[mw[mi+1][i]][i]],i); */ @@ -983,7 +1080,7 @@ double func( double *x) ll[s[mw[mi][i]][i]] += 2*weight[i]*lli; } /* end of wave */ } /* end of individual */ - } else{ + }else{ /* ml=4 no inter-extrapolation */ for (i=1,ipmx=0, sw=0.; i<=imx; i++){ for (k=1; k<=cptcovn;k++) cov[2+k]=covar[Tvar[k]][i]; for(mi=1; mi<= wav[i]-1; mi++){ @@ -1618,22 +1715,35 @@ void concatwav(int wav[], int **dh, int jk= j/stepm; jl= j -jk*stepm; ju= j -(jk+1)*stepm; - if(jl <= -ju){ - dh[mi][i]=jk; - bh[mi][i]=jl; - } - else{ - dh[mi][i]=jk+1; - bh[mi][i]=ju; - } - if(dh[mi][i]==0){ - dh[mi][i]=1; /* At least one step */ - bh[mi][i]=ju; /* At least one step */ - printf(" bh=%d ju=%d jl=%d dh=%d jk=%d stepm=%d %d\n",bh[mi][i],ju,jl,dh[mi][i],jk,stepm,i); + if(mle <=1){ + if(jl==0){ + dh[mi][i]=jk; + bh[mi][i]=0; + }else{ /* We want a negative bias in order to only have interpolation ie + * at the price of an extra matrix product in likelihood */ + dh[mi][i]=jk+1; + bh[mi][i]=ju; + } + }else{ + if(jl <= -ju){ + dh[mi][i]=jk; + bh[mi][i]=jl; /* bias is positive if real duration + * is higher than the multiple of stepm and negative otherwise. + */ + } + else{ + dh[mi][i]=jk+1; + bh[mi][i]=ju; + } + if(dh[mi][i]==0){ + dh[mi][i]=1; /* At least one step */ + bh[mi][i]=ju; /* At least one step */ + printf(" bh=%d ju=%d jl=%d dh=%d jk=%d stepm=%d %d\n",bh[mi][i],ju,jl,dh[mi][i],jk,stepm,i); + } + if(i==298 || i==287 || i==763 ||i==1061)printf(" bh=%d ju=%d jl=%d dh=%d jk=%d stepm=%d",bh[mi][i],ju,jl,dh[mi][i],jk,stepm); } - if(i==298 || i==287 || i==763 ||i==1061)printf(" bh=%d ju=%d jl=%d dh=%d jk=%d stepm=%d",bh[mi][i],ju,jl,dh[mi][i],jk,stepm); - } - } + } /* end if mle */ + } /* end wave */ } jmean=sum/k; printf("Delay (in months) between two waves Min=%d Max=%d Mean=%f\n\n ",jmin, jmax,jmean);