From 74d3bec1ce6f82cff8f31e4317622944f7ee1016 Mon Sep 17 00:00:00 2001 From: "N. Brouard" Date: Thu, 28 Dec 2000 18:49:56 +0000 Subject: [PATCH] Import de imach064 --- src/Makefile | 12 + src/README.htm | 133 + src/biaspar.txt | 28 + src/data1.txt | 8270 ++++++++++++++++++++++++++++++++++++++++++++ src/euroreves2.gif | Bin 0 -> 992 bytes src/hello.c | 10 + src/imach.c | 2215 ++++++++++++ src/imach.htm | 1024 ++++++ src/imachrun.log | 176 + src/index.htm | 19 + src/logo-ined.gif | Bin 0 -> 201 bytes 11 files changed, 11887 insertions(+) create mode 100644 src/Makefile create mode 100644 src/README.htm create mode 100644 src/biaspar.txt create mode 100644 src/data1.txt create mode 100644 src/euroreves2.gif create mode 100644 src/hello.c create mode 100644 src/imach.c create mode 100644 src/imach.htm create mode 100644 src/imachrun.log create mode 100644 src/index.htm create mode 100644 src/logo-ined.gif diff --git a/src/Makefile b/src/Makefile new file mode 100644 index 0000000..1bb13e1 --- /dev/null +++ b/src/Makefile @@ -0,0 +1,12 @@ +# Makefile for imach +CC= gcc +#LFLAGS= -O2 -mno-cygwin +LFLAGS= -g -mno-cygwin +CFLAGS= -g + +imach : imach.o + $(CC) $(LFLAGS) -o imach imach.o +imach.o : imach.c + $(CC) $(LFLAGS) -c imach.c +graph : + ./wgnuplot graph.gp diff --git a/src/README.htm b/src/README.htm new file mode 100644 index 0000000..0672b2b --- /dev/null +++ b/src/README.htm @@ -0,0 +1,133 @@ + + + + + +Computing Health Expectancies using IMaCh + + + + + +

+ +

Computing Health +Expectancies using IMaCh

+ +

(a Maximum +Likelihood Computer Program using Interpolation of Markov Chains)

+ +

INED and EUROREVES

+ +

March +2000

+ +

Authors of the program: Nicolas +Brouard, senior researcher at +the Institut +National d'Etudes Démographiques +(INED, Paris) in the "Mortality, Health and Epidemiology +Research Unit"

+ +

and Agnès Lièvre

+ +

Contribution to the mathematics: C. R. +Heathcote (Australian +National University, Canberra).

+ +

Contact: Agnès Lièvre (lievre@ined.fr)

+ +

Installation

+ +

Since the program produces many output files, we suggest to +have a separate directory for imach.
+

+ +

Uncompress the zip file imach.zip + into an empty directory, imach, for + example.
Different sub-directories are created:
- doc: most of the documentation. The main document + is doc/imach.htm .
- bin:
  - imach.exe the + executable for Windows 95/98/NT compiled + with gcc from cygwin.
  - graph.gp: an output file which is used by + the grapher, gnuplot, described next:
  +
- data: Here are two data files:
  - data1.txt + which is the main data file on which the + program has been run as the example in + the main documentation.
  - mydata.txt a + smaller data file which you can use for + your own trial.
  +
- mytry: A sub-directory to try your self. The + imach program outputs its results as text + files (tables) but also images + (actually in gif format) which are obtained in a + second step by running gnuplot (see below) on a + gnuplot command file named 'graph.gp' but also a + single html file 'index.htm' which can be clicked + to summarize your run.
- src: This subdirectory contains the source of the + program. It can be obtained by asking us by mail mailto:brouard@ined.fr + and mailto:lievre@ined.fr + .
- gp37mgw: This is the + directory of the gnuplot software. Gnuplot for + Unix or Windows 95/98/NT can be accessed freely + with its reference manual from http://www.cs.dartmouth.edu/gnuplot/. + The latest version, recently ported to windows, + has been included in imach.zip.
+
Click on the imach.exe icon to open a window and run the + program.
Read the file README.txt or, better, click on doc/imach.htm

+ +

Click +here to access to the detailed documentation

+ +

This software have been partly granted by Euro-REVES, a concerted +action from the European Union. It will be copyrighted +identically to a GNU software product, i.e. program and software +will be distributed freely for non commercial use. Sources are +not widely distributed today. You can get them by asking us with +a simple justification (name, email, institute) mailto:imach@ined.fr

+ +

Latest documentation can be accessed at http://euroreves.ined.fr/imach
+Imach latest version (0.63 of 16 march 2000) can be downloaded in +zip file http://euroreves.ined.fr/imach/imach.zip
+

+ + diff --git a/src/biaspar.txt b/src/biaspar.txt new file mode 100644 index 0000000..64110ad --- /dev/null +++ b/src/biaspar.txt @@ -0,0 +1,28 @@ +#Imach version 0.63, February 2000, INED-EUROREVES +# +title=1st_example datafile=data1.txt lastobs=8600 firstpass=1 lastpass=4 +ftol=1e-8 stepm=1 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0 +# Parameters nlstate*nlstate*ncov a12*1 + b12 * age + ... +12 0. 0. +13 0. 0. +21 0. 0. +23 0. 0. +# Scales (for hessian or gradient estimation) +12 0. 0. +13 0. 0. +21 0. 0. +23 0. 0. +#covariance matrix# +121 0. +122 0. 0. +131 0. 0. 0. +132 0. 0. 0. 0. +211 0. 0. 0. 0. 0. +212 0. 0. 0. 0. 0. 0. +231 0. 0. 0. 0. 0. 0. 0. +232 0. 0. 0. 0. 0. 0. 0. 0. +# agemin agemax for lifexpectancy, bage fage (if mle==0 ie no data nor Max likelihood). +agemin=70 agemax=100 bage=50 fage=100 +# End. + + diff --git a/src/data1.txt b/src/data1.txt new file mode 100644 index 0000000..6c02f61 --- /dev/null +++ b/src/data1.txt @@ -0,0 +1,8270 @@ + 1 1 1 1 7/1914 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zCCiLzsc!;0)sa5Ucm*}dwCIk^X=X;O!J&q5Pg-OX>_A%@~yL~o@1{8 zW|8}}vyUYUJmmR!m`AU`yQCII)q3h}!a2;i)4eK@(`L`B-zWhGNyjARLu>2zyN+-4 z$MhXVKsZ0o0Q7b@(WkJE-i2zKHu#KHx)4AvE%Z;=6Jp>@FggO=19vjH_B}lT&tR)| z`=Q&8-r6D0wv;-zEAV`{!&G$FI5XBVl47aKsh+8=5Bu_Jo*gdi%PQ|mFKe!TT36hr z(Ivi$WOv53@%qz&6<4Fl1Dg2gV$U(%Nu9aL;uzRpyy-#Oz8A)b=4vY>n>roYhFqt= z;mbw;iM)_K^;FX9$Vg4xg}3(78-dFi%^ogmcaN!}pm}1jeTTC@veG-br8Dc2#du(L Sa`5qypT8c} +main() +{ + int i; + for (i=1; i<=10;i++){ + printf("Hello\n"); + printf("Hellu\n"); + } +} + diff --git a/src/imach.c b/src/imach.c new file mode 100644 index 0000000..023019a --- /dev/null +++ b/src/imach.c @@ -0,0 +1,2215 @@ + +/*********************** Imach ************************************** + This program computes Healthy Life Expectancies from cross-longitudinal + data. Cross-longitudinal consist in a first survey ("cross") where + individuals from different ages are interviewed on their health status + or degree of disability. At least a second wave of interviews + ("longitudinal") should measure each new individual health status. + Health expectancies are computed from the transistions observed between + waves and are computed for each degree of severity of disability (number + of life states). More degrees you consider, more time is necessary to + reach the Maximum Likekilhood of the parameters involved in the model. + The simplest model is the multinomial logistic model where pij is + the probabibility to be observed in state j at the second wave conditional + to be observed in state i at the first wave. Therefore the model is: + log(pij/pii)= aij + bij*age+ cij*sex + etc , where 'age' is age and 'sex' + is a covariate. If you want to have a more complex model than "constant and + age", you should modify the program where the markup + *Covariates have to be included here again* invites you to do it. + More covariates you add, less is the speed of the convergence. + + The advantage that this computer programme claims, comes from that if the + delay between waves is not identical for each individual, or if some + individual missed an interview, the information is not rounded or lost, but + taken into account using an interpolation or extrapolation. + hPijx is the probability to be + observed in state i at age x+h conditional to the observed state i at age + x. The delay 'h' can be split into an exact number (nh*stepm) of + unobserved intermediate states. This elementary transition (by month or + quarter trimester, semester or year) is model as a multinomial logistic. + The hPx matrix is simply the matrix product of nh*stepm elementary matrices + and the contribution of each individual to the likelihood is simply hPijx. + + Also this programme outputs the covariance matrix of the parameters but also + of the life expectancies. It also computes the prevalence limits. + + Authors: Nicolas Brouard (brouard@ined.fr) and Agnès Lièvre (lievre@ined.fr). + Institut national d'études démographiques, Paris. + This software have been partly granted by Euro-REVES, a concerted action + from the European Union. + It is copyrighted identically to a GNU software product, ie programme and + software can be distributed freely for non commercial use. Latest version + can be accessed at http://euroreves.ined.fr/imach . + **********************************************************************/ + +#include +#include +#include +#include + +#define MAXLINE 256 +#define FILENAMELENGTH 80 +/*#define DEBUG*/ +/*#define win*/ + +#define MAXPARM 30 /* Maximum number of parameters for the optimization */ +#define NPARMAX 64 /* (nlstate+ndeath-1)*nlstate*ncov */ + +#define NINTERVMAX 8 +#define NLSTATEMAX 8 /* Maximum number of live states (for func) */ +#define NDEATHMAX 8 /* Maximum number of dead states (for func) */ +#define NCOVMAX 8 /* Maximum number of covariates */ +#define MAXN 20000 +#define YEARM 12. /* Number of months per year */ +#define AGESUP 130 +#define AGEBASE 40 + + +int nvar; + +int npar=NPARMAX; +int nlstate=2; /* Number of live states */ +int ndeath=1; /* Number of dead states */ +int ncov; /* Total number of covariables including constant a12*1 +b12*x ncov=2 */ + +int *wav; /* Number of waves for this individuual 0 is possible */ +int maxwav; /* Maxim number of waves */ +int mle, weightopt; +int **mw; /* mw[mi][i] is number of the mi wave for this individual */ +int **dh; /* dh[mi][i] is number of steps between mi,mi+1 for this individual */ +double **oldm, **newm, **savm; /* Working pointers to matrices */ +double **oldms, **newms, **savms; /* Fixed working pointers to matrices */ +FILE *fic,*ficpar, *ficparo,*ficres, *ficrespl, *ficrespij, *ficrest; +FILE *ficgp, *fichtm; + +#define NR_END 1 +#define FREE_ARG char* +#define FTOL 1.0e-10 + +#define NRANSI +#define ITMAX 200 + +#define TOL 2.0e-4 + +#define CGOLD 0.3819660 +#define ZEPS 1.0e-10 +#define SHFT(a,b,c,d) (a)=(b);(b)=(c);(c)=(d); + +#define GOLD 1.618034 +#define GLIMIT 100.0 +#define TINY 1.0e-20 + +static double maxarg1,maxarg2; +#define FMAX(a,b) (maxarg1=(a),maxarg2=(b),(maxarg1)>(maxarg2)? (maxarg1):(maxarg2)) +#define FMIN(a,b) (maxarg1=(a),maxarg2=(b),(maxarg1)<(maxarg2)? (maxarg1):(maxarg2)) + +#define SIGN(a,b) ((b)>0.0 ? fabs(a) : -fabs(a)) +#define rint(a) floor(a+0.5) + +static double sqrarg; +#define SQR(a) ((sqrarg=(a)) == 0.0 ? 0.0 :sqrarg*sqrarg) +#define SWAP(a,b) {temp=(a);(a)=(b);(b)=temp;} + +int imx; +int stepm; +/* Stepm, step in month: minimum step interpolation*/ + +int m,nb; +int *num, firstpass=0, lastpass=2,*cod; +double **agev,*moisnais, *annais, *moisdc, *andc,**mint, **anint; +double **pmmij; + +double *weight; +int **s; /* Status */ +double *agedc, **covar, idx; + + +double ftol=FTOL; /* Tolerance for computing Max Likelihood */ +double ftolhess; /* Tolerance for computing hessian */ + + +/******************************************/ + +void replace(char *s, char*t) +{ + int i; + int lg=20; + i=0; + lg=strlen(t); + for(i=0; i<= lg; i++) { + (s[i] = t[i]); + if (t[i]== '\\') s[i]='/'; + } +} +void cut(char *u,char *v, char*t) +{ + int i,lg,j,p; + i=0; + for(j=0; j<=strlen(t); j++) { + if(t[j]=='\\') p=j; + } + + lg=strlen(t); + for(j=0; j=(p+1))(v[j-p-1] = t[j]); + } +} + +/********************** nrerror ********************/ + +void nrerror(char error_text[]) +{ + fprintf(stderr,"ERREUR ...\n"); + fprintf(stderr,"%s\n",error_text); + exit(1); +} +/*********************** vector *******************/ +double *vector(int nl, int nh) +{ + double *v; + v=(double *) malloc((size_t)((nh-nl+1+NR_END)*sizeof(double))); + if (!v) nrerror("allocation failure in vector"); + return v-nl+NR_END; +} + +/************************ free vector ******************/ +void free_vector(double*v, int nl, int nh) +{ + free((FREE_ARG)(v+nl-NR_END)); +} + +/************************ivector *******************************/ +int *ivector(long nl,long nh) +{ + int *v; + v=(int *) malloc((size_t)((nh-nl+1+NR_END)*sizeof(int))); + if (!v) nrerror("allocation failure in ivector"); + return v-nl+NR_END; +} + +/******************free ivector **************************/ +void free_ivector(int *v, long nl, long nh) +{ + free((FREE_ARG)(v+nl-NR_END)); +} + +/******************* imatrix *******************************/ +int **imatrix(long nrl, long nrh, long ncl, long nch) + /* allocate a int matrix with subscript range m[nrl..nrh][ncl..nch] */ +{ + long i, nrow=nrh-nrl+1,ncol=nch-ncl+1; + int **m; + + /* allocate pointers to rows */ + m=(int **) malloc((size_t)((nrow+NR_END)*sizeof(int*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + + /* allocate rows and set pointers to them */ + m[nrl]=(int *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(int))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for(i=nrl+1;i<=nrh;i++) m[i]=m[i-1]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +/****************** free_imatrix *************************/ +void free_imatrix(m,nrl,nrh,ncl,nch) + int **m; + long nch,ncl,nrh,nrl; + /* free an int matrix allocated by imatrix() */ +{ + free((FREE_ARG) (m[nrl]+ncl-NR_END)); + free((FREE_ARG) (m+nrl-NR_END)); +} + +/******************* matrix *******************************/ +double **matrix(long nrl, long nrh, long ncl, long nch) +{ + long i, nrow=nrh-nrl+1, ncol=nch-ncl+1; + double **m; + + m=(double **) malloc((size_t)((nrow+NR_END)*sizeof(double*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + m[nrl]=(double *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(double))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for (i=nrl+1; i<=nrh; i++) m[i]=m[i-1]+ncol; + return m; +} + +/*************************free matrix ************************/ +void free_matrix(double **m, long nrl, long nrh, long ncl, long nch) +{ + free((FREE_ARG)(m[nrl]+ncl-NR_END)); + free((FREE_ARG)(m+nrl-NR_END)); +} + +/******************* ma3x *******************************/ +double ***ma3x(long nrl, long nrh, long ncl, long nch, long nll, long nlh) +{ + long i, j, nrow=nrh-nrl+1, ncol=nch-ncl+1, nlay=nlh-nll+1; + double ***m; + + m=(double ***) malloc((size_t)((nrow+NR_END)*sizeof(double*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + m[nrl]=(double **) malloc((size_t)((nrow*ncol+NR_END)*sizeof(double))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for (i=nrl+1; i<=nrh; i++) m[i]=m[i-1]+ncol; + + m[nrl][ncl]=(double *) malloc((size_t)((nrow*ncol*nlay+NR_END)*sizeof(double))); + if (!m[nrl][ncl]) nrerror("allocation failure 3 in matrix()"); + m[nrl][ncl] += NR_END; + m[nrl][ncl] -= nll; + for (j=ncl+1; j<=nch; j++) + m[nrl][j]=m[nrl][j-1]+nlay; + + for (i=nrl+1; i<=nrh; i++) { + m[i][ncl]=m[i-1l][ncl]+ncol*nlay; + for (j=ncl+1; j<=nch; j++) + m[i][j]=m[i][j-1]+nlay; + } + return m; +} + +/*************************free ma3x ************************/ +void free_ma3x(double ***m, long nrl, long nrh, long ncl, long nch,long nll, long nlh) +{ + free((FREE_ARG)(m[nrl][ncl]+ nll-NR_END)); + free((FREE_ARG)(m[nrl]+ncl-NR_END)); + free((FREE_ARG)(m+nrl-NR_END)); +} + +/***************** f1dim *************************/ +extern int ncom; +extern double *pcom,*xicom; +extern double (*nrfunc)(double []); + +double f1dim(double x) +{ + int j; + double f; + double *xt; + + xt=vector(1,ncom); + for (j=1;j<=ncom;j++) xt[j]=pcom[j]+x*xicom[j]; + f=(*nrfunc)(xt); + free_vector(xt,1,ncom); + return f; +} + +/*****************brent *************************/ +double brent(double ax, double bx, double cx, double (*f)(double), double tol, double *xmin) +{ + int iter; + double a,b,d,etemp; + double fu,fv,fw,fx; + double ftemp; + double p,q,r,tol1,tol2,u,v,w,x,xm; + double e=0.0; + + a=(ax < cx ? ax : cx); + b=(ax > cx ? ax : cx); + x=w=v=bx; + fw=fv=fx=(*f)(x); + for (iter=1;iter<=ITMAX;iter++) { + xm=0.5*(a+b); + tol2=2.0*(tol1=tol*fabs(x)+ZEPS); + /* if (2.0*fabs(fp-(*fret)) <= ftol*(fabs(fp)+fabs(*fret)))*/ + printf(".");fflush(stdout); +#ifdef DEBUG + printf("br %d,x=%.10e xm=%.10e b=%.10e a=%.10e tol=%.10e tol1=%.10e tol2=%.10e x-xm=%.10e fx=%.12e fu=%.12e,fw=%.12e,ftemp=%.12e,ftol=%.12e\n",iter,x,xm,b,a,tol,tol1,tol2,(x-xm),fx,fu,fw,ftemp,ftol); + /* if ((fabs(x-xm) <= (tol2-0.5*(b-a)))||(2.0*fabs(fu-ftemp) <= ftol*1.e-2*(fabs(fu)+fabs(ftemp)))) { */ +#endif + if (fabs(x-xm) <= (tol2-0.5*(b-a))){ + *xmin=x; + return fx; + } + ftemp=fu; + if (fabs(e) > tol1) { + r=(x-w)*(fx-fv); + q=(x-v)*(fx-fw); + p=(x-v)*q-(x-w)*r; + q=2.0*(q-r); + if (q > 0.0) p = -p; + q=fabs(q); + etemp=e; + e=d; + if (fabs(p) >= fabs(0.5*q*etemp) || p <= q*(a-x) || p >= q*(b-x)) + d=CGOLD*(e=(x >= xm ? a-x : b-x)); + else { + d=p/q; + u=x+d; + if (u-a < tol2 || b-u < tol2) + d=SIGN(tol1,xm-x); + } + } else { + d=CGOLD*(e=(x >= xm ? a-x : b-x)); + } + u=(fabs(d) >= tol1 ? x+d : x+SIGN(tol1,d)); + fu=(*f)(u); + if (fu <= fx) { + if (u >= x) a=x; else b=x; + SHFT(v,w,x,u) + SHFT(fv,fw,fx,fu) + } else { + if (u < x) a=u; else b=u; + if (fu <= fw || w == x) { + v=w; + w=u; + fv=fw; + fw=fu; + } else if (fu <= fv || v == x || v == w) { + v=u; + fv=fu; + } + } + } + nrerror("Too many iterations in brent"); + *xmin=x; + return fx; +} + +/****************** mnbrak ***********************/ + +void mnbrak(double *ax, double *bx, double *cx, double *fa, double *fb, double *fc, + double (*func)(double)) +{ + double ulim,u,r,q, dum; + double fu; + + *fa=(*func)(*ax); + *fb=(*func)(*bx); + if (*fb > *fa) { + SHFT(dum,*ax,*bx,dum) + SHFT(dum,*fb,*fa,dum) + } + *cx=(*bx)+GOLD*(*bx-*ax); + *fc=(*func)(*cx); + while (*fb > *fc) { + r=(*bx-*ax)*(*fb-*fc); + q=(*bx-*cx)*(*fb-*fa); + u=(*bx)-((*bx-*cx)*q-(*bx-*ax)*r)/ + (2.0*SIGN(FMAX(fabs(q-r),TINY),q-r)); + ulim=(*bx)+GLIMIT*(*cx-*bx); + if ((*bx-u)*(u-*cx) > 0.0) { + fu=(*func)(u); + } else if ((*cx-u)*(u-ulim) > 0.0) { + fu=(*func)(u); + if (fu < *fc) { + SHFT(*bx,*cx,u,*cx+GOLD*(*cx-*bx)) + SHFT(*fb,*fc,fu,(*func)(u)) + } + } else if ((u-ulim)*(ulim-*cx) >= 0.0) { + u=ulim; + fu=(*func)(u); + } else { + u=(*cx)+GOLD*(*cx-*bx); + fu=(*func)(u); + } + SHFT(*ax,*bx,*cx,u) + SHFT(*fa,*fb,*fc,fu) + } +} + +/*************** linmin ************************/ + +int ncom; +double *pcom,*xicom; +double (*nrfunc)(double []); + +void linmin(double p[], double xi[], int n, double *fret,double (*func)(double [])) +{ + double brent(double ax, double bx, double cx, + double (*f)(double), double tol, double *xmin); + double f1dim(double x); + void mnbrak(double *ax, double *bx, double *cx, double *fa, double *fb, + double *fc, double (*func)(double)); + int j; + double xx,xmin,bx,ax; + double fx,fb,fa; + + ncom=n; + pcom=vector(1,n); + xicom=vector(1,n); + nrfunc=func; + for (j=1;j<=n;j++) { + pcom[j]=p[j]; + xicom[j]=xi[j]; + } + ax=0.0; + xx=1.0; + mnbrak(&ax,&xx,&bx,&fa,&fx,&fb,f1dim); + *fret=brent(ax,xx,bx,f1dim,TOL,&xmin); +#ifdef DEBUG + printf("retour brent fret=%.12e xmin=%.12e\n",*fret,xmin); +#endif + for (j=1;j<=n;j++) { + xi[j] *= xmin; + p[j] += xi[j]; + } + free_vector(xicom,1,n); + free_vector(pcom,1,n); +} + +/*************** powell ************************/ +void powell(double p[], double **xi, int n, double ftol, int *iter, double *fret, + double (*func)(double [])) + +{ + + + void linmin(double p[], double xi[], int n, double *fret, + double (*func)(double [])); + int i,ibig,j; + double del,t,*pt,*ptt,*xit; + double fp,fptt; + double *xits; + pt=vector(1,n); + ptt=vector(1,n); + xit=vector(1,n); + xits=vector(1,n); + *fret=(*func)(p); + for (j=1;j<=n;j++) pt[j]=p[j]; + for (*iter=1;;++(*iter)) { + fp=(*fret); + ibig=0; + del=0.0; + printf("\nPowell iter=%d -2*LL=%.12f",*iter,*fret); + for (i=1;i<=n;i++) + printf(" %d %.12f",i, p[i]); + printf("\n"); + for (i=1;i<=n;i++) { + for (j=1;j<=n;j++) xit[j]=xi[j][i]; + fptt=(*fret); +#ifdef DEBUG + printf("fret=%lf \n",*fret); +#endif + printf("%d",i);fflush(stdout); + linmin(p,xit,n,fret,func); + if (fabs(fptt-(*fret)) > del) { + del=fabs(fptt-(*fret)); + ibig=i; + } +#ifdef DEBUG + printf("%d %.12e",i,(*fret)); + for (j=1;j<=n;j++) { + xits[j]=FMAX(fabs(p[j]-pt[j]),1.e-5); + printf(" x(%d)=%.12e",j,xit[j]); + } + for(j=1;j<=n;j++) + printf(" p=%.12e",p[j]); + printf("\n"); +#endif + } + if (2.0*fabs(fp-(*fret)) <= ftol*(fabs(fp)+fabs(*fret))) { +#ifdef DEBUG + int k[2],l; + k[0]=1; + k[1]=-1; + printf("Max: %.12e",(*func)(p)); + for (j=1;j<=n;j++) + printf(" %.12e",p[j]); + printf("\n"); + for(l=0;l<=1;l++) { + for (j=1;j<=n;j++) { + ptt[j]=p[j]+(p[j]-pt[j])*k[l]; + printf("l=%d j=%d ptt=%.12e, xits=%.12e, p=%.12e, xit=%.12e", l,j,ptt[j],xits[j],p[j],xit[j]); + } + printf("func(ptt)=%.12e, deriv=%.12e\n",(*func)(ptt),(ptt[j]-p[j])/((*func)(ptt)-(*func)(p))); + } +#endif + + + free_vector(xit,1,n); + free_vector(xits,1,n); + free_vector(ptt,1,n); + free_vector(pt,1,n); + return; + } + if (*iter == ITMAX) nrerror("powell exceeding maximum iterations."); + for (j=1;j<=n;j++) { + ptt[j]=2.0*p[j]-pt[j]; + xit[j]=p[j]-pt[j]; + pt[j]=p[j]; + } + fptt=(*func)(ptt); + if (fptt < fp) { + t=2.0*(fp-2.0*(*fret)+fptt)*SQR(fp-(*fret)-del)-del*SQR(fp-fptt); + if (t < 0.0) { + linmin(p,xit,n,fret,func); + for (j=1;j<=n;j++) { + xi[j][ibig]=xi[j][n]; + xi[j][n]=xit[j]; + } +#ifdef DEBUG + printf("Direction changed last moved %d in place of ibig=%d, new last is the average:\n",n,ibig); + for(j=1;j<=n;j++) + printf(" %.12e",xit[j]); + printf("\n"); +#endif + } + } + } +} + +/**** Prevalence limit ****************/ + +double **prevalim(double **prlim, int nlstate, double x[], double age, double **oldm, double **savm, double ftolpl) +{ + /* Computes the prevalence limit in each live state at age x by left multiplying the unit + matrix by transitions matrix until convergence is reached */ + + int i, ii,j,k; + double min, max, maxmin, maxmax,sumnew=0.; + double **matprod2(); + double **out, cov[NCOVMAX], **pmij(); + double **newm; + double agefin, delaymax=50 ; /* Max number of years to converge */ + + for (ii=1;ii<=nlstate+ndeath;ii++) + for (j=1;j<=nlstate+ndeath;j++){ + oldm[ii][j]=(ii==j ? 1.0 : 0.0); + } + /* Even if hstepm = 1, at least one multiplication by the unit matrix */ + for(agefin=age-stepm/YEARM; agefin>=age-delaymax; agefin=agefin-stepm/YEARM){ + newm=savm; + /* Covariates have to be included here again */ + cov[1]=1.; + cov[2]=agefin; + out=matprod2(newm, pmij(pmmij,cov,ncov,x,nlstate),1,nlstate+ndeath,1,nlstate+ndeath,1,nlstate+ndeath, oldm); +/* printf("age=%f agefin=%f po=%f pn=%f\n",age,agefin,oldm[1][1],newm[1][1]);*/ + + savm=oldm; + oldm=newm; + maxmax=0.; + for(j=1;j<=nlstate;j++){ + min=1.; + max=0.; + for(i=1; i<=nlstate; i++) { + sumnew=0; + for(k=1; k<=ndeath; k++) sumnew+=newm[i][nlstate+k]; + prlim[i][j]= newm[i][j]/(1-sumnew); + max=FMAX(max,prlim[i][j]); + min=FMIN(min,prlim[i][j]); + } + maxmin=max-min; + maxmax=FMAX(maxmax,maxmin); + } + if(maxmax < ftolpl){ + return prlim; + } + } +} + +/*************** transition probabilities **********/ + +double **pmij(double **ps, double *cov, int ncov, double *x, int nlstate ) +{ + double s1, s2; + /*double t34;*/ + int i,j,j1, nc, ii, jj; + + for(i=1; i<= nlstate; i++){ + for(j=1; ji s1=%.17e, s2=%.17e %lx %lx\n",s1,s2,s1,s2);*/ + } + ps[i][j]=s2; + } + } + for(i=1; i<= nlstate; i++){ + s1=0; + for(j=1; ji) { + printf(".%d%d",i,j);fflush(stdout); + hess[i][j]=hessij(p,delti,i,j); + hess[j][i]=hess[i][j]; + } + } + } + printf("\n"); + + printf("\nInverting the hessian to get the covariance matrix. Wait...\n"); + + a=matrix(1,npar,1,npar); + y=matrix(1,npar,1,npar); + x=vector(1,npar); + indx=ivector(1,npar); + for (i=1;i<=npar;i++) + for (j=1;j<=npar;j++) a[i][j]=hess[i][j]; + ludcmp(a,npar,indx,&pd); + + for (j=1;j<=npar;j++) { + for (i=1;i<=npar;i++) x[i]=0; + x[j]=1; + lubksb(a,npar,indx,x); + for (i=1;i<=npar;i++){ + matcov[i][j]=x[i]; + } + } + + printf("\n#Hessian matrix#\n"); + for (i=1;i<=npar;i++) { + for (j=1;j<=npar;j++) { + printf("%.3e ",hess[i][j]); + } + printf("\n"); + } + + /* Recompute Inverse */ + for (i=1;i<=npar;i++) + for (j=1;j<=npar;j++) a[i][j]=matcov[i][j]; + ludcmp(a,npar,indx,&pd); + + /* printf("\n#Hessian matrix recomputed#\n"); + + for (j=1;j<=npar;j++) { + for (i=1;i<=npar;i++) x[i]=0; + x[j]=1; + lubksb(a,npar,indx,x); + for (i=1;i<=npar;i++){ + y[i][j]=x[i]; + printf("%.3e ",y[i][j]); + } + printf("\n"); + } + */ + + free_matrix(a,1,npar,1,npar); + free_matrix(y,1,npar,1,npar); + free_vector(x,1,npar); + free_ivector(indx,1,npar); + free_matrix(hess,1,npar,1,npar); + + +} + +/*************** hessian matrix ****************/ +double hessii( double x[], double delta, int theta, double delti[]) +{ + int i; + int l=1, lmax=20; + double k1,k2; + double p2[NPARMAX+1]; + double res; + double delt, delts, nkhi=10.,nkhif=1., khi=1.e-4; + double fx; + int k=0,kmax=10; + double l1; + + fx=func(x); + for (i=1;i<=npar;i++) p2[i]=x[i]; + for(l=0 ; l <=lmax; l++){ + l1=pow(10,l); + delts=delt; + for(k=1 ; k khi/nkhif) || (k2 >khi/nkhif)){ /* Keeps lastvalue before 3.84/2 KHI2 5% 1d.f. */ + k=kmax; l=lmax*10.; + } + else if((k1 >khi/nkhi) || (k2 >khi/nkhi)){ + delts=delt; + } + } + } + delti[theta]=delts; + return res; + +} + +double hessij( double x[], double delti[], int thetai,int thetaj) +{ + int i; + int l=1, l1, lmax=20; + double k1,k2,k3,k4,res,fx; + double p2[NPARMAX+1]; + int k; + + fx=func(x); + for (k=1; k<=2; k++) { + for (i=1;i<=npar;i++) p2[i]=x[i]; + p2[thetai]=x[thetai]+delti[thetai]/k; + p2[thetaj]=x[thetaj]+delti[thetaj]/k; + k1=func(p2)-fx; + + p2[thetai]=x[thetai]+delti[thetai]/k; + p2[thetaj]=x[thetaj]-delti[thetaj]/k; + k2=func(p2)-fx; + + p2[thetai]=x[thetai]-delti[thetai]/k; + p2[thetaj]=x[thetaj]+delti[thetaj]/k; + k3=func(p2)-fx; + + p2[thetai]=x[thetai]-delti[thetai]/k; + p2[thetaj]=x[thetaj]-delti[thetaj]/k; + k4=func(p2)-fx; + res=(k1-k2-k3+k4)/4.0/delti[thetai]*k/delti[thetaj]*k/2.; /* Because of L not 2*L */ +#ifdef DEBUG + printf("%d %d k=%d, k1=%.12e k2=%.12e k3=%.12e k4=%.12e delti/k=%.12e deltj/k=%.12e, xi-de/k=%.12e xj-de/k=%.12e res=%.12e k1234=%.12e,k1-2=%.12e,k3-4=%.12e\n",thetai,thetaj,k,k1,k2,k3,k4,delti[thetai]/k,delti[thetaj]/k,x[thetai]-delti[thetai]/k,x[thetaj]-delti[thetaj]/k, res,k1-k2-k3+k4,k1-k2,k3-k4); +#endif + } + return res; +} + +/************** Inverse of matrix **************/ +void ludcmp(double **a, int n, int *indx, double *d) +{ + int i,imax,j,k; + double big,dum,sum,temp; + double *vv; + + vv=vector(1,n); + *d=1.0; + for (i=1;i<=n;i++) { + big=0.0; + for (j=1;j<=n;j++) + if ((temp=fabs(a[i][j])) > big) big=temp; + if (big == 0.0) nrerror("Singular matrix in routine ludcmp"); + vv[i]=1.0/big; + } + for (j=1;j<=n;j++) { + for (i=1;i= big) { + big=dum; + imax=i; + } + } + if (j != imax) { + for (k=1;k<=n;k++) { + dum=a[imax][k]; + a[imax][k]=a[j][k]; + a[j][k]=dum; + } + *d = -(*d); + vv[imax]=vv[j]; + } + indx[j]=imax; + if (a[j][j] == 0.0) a[j][j]=TINY; + if (j != n) { + dum=1.0/(a[j][j]); + for (i=j+1;i<=n;i++) a[i][j] *= dum; + } + } + free_vector(vv,1,n); /* Doesn't work */ +; +} + +void lubksb(double **a, int n, int *indx, double b[]) +{ + int i,ii=0,ip,j; + double sum; + + for (i=1;i<=n;i++) { + ip=indx[i]; + sum=b[ip]; + b[ip]=b[i]; + if (ii) + for (j=ii;j<=i-1;j++) sum -= a[i][j]*b[j]; + else if (sum) ii=i; + b[i]=sum; + } + for (i=n;i>=1;i--) { + sum=b[i]; + for (j=i+1;j<=n;j++) sum -= a[i][j]*b[j]; + b[i]=sum/a[i][i]; + } +} + +/************ Frequencies ********************/ +void freqsummary(char fileres[], int agemin, int agemax, int **s, double **agev, int nlstate, int imx) +{ /* Some frequencies */ + + int i, m, jk; + double ***freq; /* Frequencies */ + double *pp; + double pos; + FILE *ficresp; + char fileresp[FILENAMELENGTH]; + + pp=vector(1,nlstate); + + strcpy(fileresp,"p"); + strcat(fileresp,fileres); + if((ficresp=fopen(fileresp,"w"))==NULL) { + printf("Problem with prevalence resultfile: %s\n", fileresp); + exit(0); + } + + freq= ma3x(-1,nlstate+ndeath,-1,nlstate+ndeath,agemin,agemax+3); + for (i=-1; i<=nlstate+ndeath; i++) + for (jk=-1; jk<=nlstate+ndeath; jk++) + for(m=agemin; m <= agemax+3; m++) + freq[i][jk][m]=0; + + for (i=1; i<=imx; i++) { + for(m=firstpass; m<= lastpass-1; m++){ + if(agev[m][i]==0) agev[m][i]=agemax+1; + if(agev[m][i]==1) agev[m][i]=agemax+2; + freq[s[m][i]][s[m+1][i]][(int)agev[m][i]] += weight[i]; + freq[s[m][i]][s[m+1][i]][(int) agemax+3] += weight[i]; + } + } + + fprintf(ficresp, "#"); + for(i=1; i<=nlstate;i++) + fprintf(ficresp, " Age Prev(%d) N(%d) N",i,i); +fprintf(ficresp, "\n"); + + for(i=(int)agemin; i <= (int)agemax+3; i++){ + if(i==(int)agemax+3) + printf("Total"); + else + printf("Age %d", i); + for(jk=1; jk <=nlstate ; jk++){ + for(m=-1, pp[jk]=0; m <=nlstate+ndeath ; m++) + pp[jk] += freq[jk][m][i]; + } + for(jk=1; jk <=nlstate ; jk++){ + for(m=-1, pos=0; m <=0 ; m++) + pos += freq[jk][m][i]; + if(pp[jk]>=1.e-10) + printf(" %d.=%.0f loss[%d]=%.1f%%",jk,pp[jk],jk,100*pos/pp[jk]); + else + printf(" %d.=%.0f loss[%d]=NaNQ%%",jk,pp[jk],jk); + } + for(jk=1; jk <=nlstate ; jk++){ + for(m=1, pp[jk]=0; m <=nlstate+ndeath; m++) + pp[jk] += freq[jk][m][i]; + } + for(jk=1,pos=0; jk <=nlstate ; jk++) + pos += pp[jk]; + for(jk=1; jk <=nlstate ; jk++){ + if(pos>=1.e-5) + printf(" %d.=%.0f prev[%d]=%.1f%%",jk,pp[jk],jk,100*pp[jk]/pos); + else + printf(" %d.=%.0f prev[%d]=NaNQ%%",jk,pp[jk],jk); + if( i <= (int) agemax){ + if(pos>=1.e-5) + fprintf(ficresp," %d %.5f %.0f %.0f",i,pp[jk]/pos, pp[jk],pos); + else + fprintf(ficresp," %d NaNq %.0f %.0f",i,pp[jk],pos); + } + } + for(jk=-1; jk <=nlstate+ndeath; jk++) + for(m=-1; m <=nlstate+ndeath; m++) + if(freq[jk][m][i] !=0 ) printf(" %d%d=%.0f",jk,m,freq[jk][m][i]); + if(i <= (int) agemax) + fprintf(ficresp,"\n"); + printf("\n"); + } + + fclose(ficresp); + free_ma3x(freq,-1,nlstate+ndeath,-1,nlstate+ndeath,(int) agemin,(int) agemax+3); + free_vector(pp,1,nlstate); + +} /* End of Freq */ + +/************* Waves Concatenation ***************/ + +void concatwav(int wav[], int **dh, int **mw, int **s, double *agedc, double **agev, int firstpass, int lastpass, int imx, int nlstate, int stepm) +{ + /* Concatenates waves: wav[i] is the number of effective (useful waves) of individual i. + Death is a valid wave (if date is known). + mw[mi][i] is the mi (mi=1 to wav[i]) effective wave of individual i + dh[m][i] of dh[mw[mi][i][i] is the delay between two effective waves m=mw[mi][i] + and mw[mi+1][i]. dh depends on stepm. + */ + + int i, mi, m; + int j, k=0,jk, ju, jl,jmin=1e+5, jmax=-1; +float sum=0.; + + for(i=1; i<=imx; i++){ + mi=0; + m=firstpass; + while(s[m][i] <= nlstate){ + if(s[m][i]>=1) + mw[++mi][i]=m; + if(m >=lastpass) + break; + else + m++; + }/* end while */ + if (s[m][i] > nlstate){ + mi++; /* Death is another wave */ + /* if(mi==0) never been interviewed correctly before death */ + /* Only death is a correct wave */ + mw[mi][i]=m; + } + + wav[i]=mi; + if(mi==0) + printf("Warning, no any valid information for:%d line=%d\n",num[i],i); + } + + for(i=1; i<=imx; i++){ + for(mi=1; mi nlstate) { + j= rint(agedc[i]*12-agev[mw[mi][i]][i]*12); + if(j=0) j=1; /* Survives at least one month after exam */ + } + else{ + j= rint( (agev[mw[mi+1][i]][i]*12 - agev[mw[mi][i]][i]*12)); + k=k+1; + if (j >= jmax) jmax=j; + else if (j <= jmin)jmin=j; + sum=sum+j; + } + jk= j/stepm; + jl= j -jk*stepm; + ju= j -(jk+1)*stepm; + if(jl <= -ju) + dh[mi][i]=jk; + else + dh[mi][i]=jk+1; + if(dh[mi][i]==0) + dh[mi][i]=1; /* At least one step */ + } + } + } + printf("Delay (in months) between two waves Min=%d Max=%d Mean=%f\n\n ",jmin, jmax,sum/k); +} + +/*********** Health Expectancies ****************/ + +void evsij(char fileres[], double ***eij, double x[], int nlstate, int stepm, int bage, int fage, double **oldm, double **savm) +{ + /* Health expectancies */ + int i, j, nhstepm, hstepm, h; + double age, agelim,hf; + double ***p3mat; + + FILE *ficreseij; + char filerese[FILENAMELENGTH]; + + strcpy(filerese,"e"); + strcat(filerese,fileres); + if((ficreseij=fopen(filerese,"w"))==NULL) { + printf("Problem with Health Exp. resultfile: %s\n", filerese); exit(0); + } + printf("Computing Health Expectancies: result on file '%s' \n", filerese); + + fprintf(ficreseij,"# Health expectancies\n"); + fprintf(ficreseij,"# Age"); + for(i=1; i<=nlstate;i++) + for(j=1; j<=nlstate;j++) + fprintf(ficreseij," %1d-%1d",i,j); + fprintf(ficreseij,"\n"); + + hstepm=1*YEARM; /* Every j years of age (in month) */ + hstepm=hstepm/stepm; /* Typically in stepm units, if j= 2 years, = 2/6 months = 4 */ + + agelim=AGESUP; + for (age=bage; age<=fage; age ++){ /* If stepm=6 months */ + /* nhstepm age range expressed in number of stepm */ + nhstepm=(int) rint((agelim-age)*YEARM/stepm); + /* Typically if 20 years = 20*12/6=40 stepm */ + if (stepm >= YEARM) hstepm=1; + nhstepm = nhstepm/hstepm;/* Expressed in hstepm, typically 40/4=10 */ + p3mat=ma3x(1,nlstate+ndeath,1, nlstate+ndeath, 0,nhstepm); + /* Computed by stepm unit matrices, product of hstepm matrices, stored + in an array of nhstepm length: nhstepm=10, hstepm=4, stepm=6 months */ + hpxij(p3mat,nhstepm,age,hstepm,x,nlstate,stepm,oldm, savm); + + + for(i=1; i<=nlstate;i++) + for(j=1; j<=nlstate;j++) + for (h=0, eij[i][j][(int)age]=0; h<=nhstepm; h++){ + eij[i][j][(int)age] +=p3mat[i][j][h]; + } + + hf=1; + if (stepm >= YEARM) hf=stepm/YEARM; + fprintf(ficreseij,"%.0f",age ); + for(i=1; i<=nlstate;i++) + for(j=1; j<=nlstate;j++){ + fprintf(ficreseij," %.4f", hf*eij[i][j][(int)age]); + } + fprintf(ficreseij,"\n"); + free_ma3x(p3mat,1,nlstate+ndeath,1, nlstate+ndeath, 0,nhstepm); + } + fclose(ficreseij); +} + +/************ Variance ******************/ +void varevsij(char fileres[], double ***vareij, double **matcov, double x[], double delti[], int nlstate, int stepm, double bage, double fage, double **oldm, double **savm, double **prlim, double ftolpl) +{ + /* Variance of health expectancies */ + /* double **prevalim(double **prlim, int nlstate, double *xp, double age, double **oldm, double ** savm,double ftolpl);*/ + double **newm; + double **dnewm,**doldm; + int i, j, nhstepm, hstepm, h; + int k; + FILE *ficresvij; + char fileresv[FILENAMELENGTH]; + double *xp; + double **gp, **gm; + double ***gradg, ***trgradg; + double ***p3mat; + double age,agelim; + int theta; + + strcpy(fileresv,"v"); + strcat(fileresv,fileres); + if((ficresvij=fopen(fileresv,"w"))==NULL) { + printf("Problem with variance resultfile: %s\n", fileresv);exit(0); + } + printf("Computing Variance-covariance of DFLEs: file '%s' \n", fileresv); + + + fprintf(ficresvij,"# Covariances of life expectancies\n"); + fprintf(ficresvij,"# Age"); + for(i=1; i<=nlstate;i++) + for(j=1; j<=nlstate;j++) + fprintf(ficresvij," Cov(e%1d, e%1d)",i,j); + fprintf(ficresvij,"\n"); + + xp=vector(1,npar); + dnewm=matrix(1,nlstate,1,npar); + doldm=matrix(1,nlstate,1,nlstate); + + hstepm=1*YEARM; /* Every year of age */ + hstepm=hstepm/stepm; /* Typically in stepm units, if j= 2 years, = 2/6 months = 4 */ + agelim = AGESUP; + for (age=bage; age<=fage; age ++){ /* If stepm=6 months */ + nhstepm=(int) rint((agelim-age)*YEARM/stepm); /* Typically 20 years = 20*12/6=40 */ + if (stepm >= YEARM) hstepm=1; + nhstepm = nhstepm/hstepm; /* Typically 40/4=10 */ + p3mat=ma3x(1,nlstate+ndeath,1, nlstate+ndeath, 0,nhstepm); + gradg=ma3x(0,nhstepm,1,npar,1,nlstate); + gp=matrix(0,nhstepm,1,nlstate); + gm=matrix(0,nhstepm,1,nlstate); + + for(theta=1; theta <=npar; theta++){ + for(i=1; i<=npar; i++){ /* Computes gradient */ + xp[i] = x[i] + (i==theta ?delti[theta]:0); + } + hpxij(p3mat,nhstepm,age,hstepm,xp,nlstate,stepm,oldm,savm); + prevalim(prlim,nlstate,xp,age,oldm,savm,ftolpl); + for(j=1; j<= nlstate; j++){ + for(h=0; h<=nhstepm; h++){ + for(i=1, gp[h][j]=0.;i<=nlstate;i++) + gp[h][j] += prlim[i][i]*p3mat[i][j][h]; + } + } + + for(i=1; i<=npar; i++) /* Computes gradient */ + xp[i] = x[i] - (i==theta ?delti[theta]:0); + hpxij(p3mat,nhstepm,age,hstepm,xp,nlstate,stepm,oldm,savm); + prevalim(prlim,nlstate,xp,age,oldm,savm,ftolpl); + for(j=1; j<= nlstate; j++){ + for(h=0; h<=nhstepm; h++){ + for(i=1, gm[h][j]=0.;i<=nlstate;i++) + gm[h][j] += prlim[i][i]*p3mat[i][j][h]; + } + } + for(j=1; j<= nlstate; j++) + for(h=0; h<=nhstepm; h++){ + gradg[h][theta][j]= (gp[h][j]-gm[h][j])/2./delti[theta]; + } + } /* End theta */ + + trgradg =ma3x(0,nhstepm,1,nlstate,1,npar); + + for(h=0; h<=nhstepm; h++) + for(j=1; j<=nlstate;j++) + for(theta=1; theta <=npar; theta++) + trgradg[h][j][theta]=gradg[h][theta][j]; + + for(i=1;i<=nlstate;i++) + for(j=1;j<=nlstate;j++) + vareij[i][j][(int)age] =0.; + for(h=0;h<=nhstepm;h++){ + for(k=0;k<=nhstepm;k++){ + matprod2(dnewm,trgradg[h],1,nlstate,1,npar,1,npar,matcov); + matprod2(doldm,dnewm,1,nlstate,1,npar,1,nlstate,gradg[k]); + for(i=1;i<=nlstate;i++) + for(j=1;j<=nlstate;j++) + vareij[i][j][(int)age] += doldm[i][j]; + } + } + h=1; + if (stepm >= YEARM) h=stepm/YEARM; + fprintf(ficresvij,"%.0f ",age ); + for(i=1; i<=nlstate;i++) + for(j=1; j<=nlstate;j++){ + fprintf(ficresvij," %.4f", h*vareij[i][j][(int)age]); + } + fprintf(ficresvij,"\n"); + free_matrix(gp,0,nhstepm,1,nlstate); + free_matrix(gm,0,nhstepm,1,nlstate); + free_ma3x(gradg,0,nhstepm,1,npar,1,nlstate); + free_ma3x(trgradg,0,nhstepm,1,nlstate,1,npar); + free_ma3x(p3mat,1,nlstate+ndeath,1, nlstate+ndeath, 0,nhstepm); + } /* End age */ + fclose(ficresvij); + free_vector(xp,1,npar); + free_matrix(doldm,1,nlstate,1,npar); + free_matrix(dnewm,1,nlstate,1,nlstate); + +} + +/************ Variance of prevlim ******************/ +void varprevlim(char fileres[], double **varpl, double **matcov, double x[], double delti[], int nlstate, int stepm, double bage, double fage, double **oldm, double **savm, double **prlim, double ftolpl) +{ + /* Variance of health expectancies */ + /* double **prevalim(double **prlim, int nlstate, double *xp, double age, double **oldm, double ** savm,double ftolpl);*/ + double **newm; + double **dnewm,**doldm; + int i, j, nhstepm, hstepm; + int k; + FILE *ficresvpl; + char fileresvpl[FILENAMELENGTH]; + double *xp; + double *gp, *gm; + double **gradg, **trgradg; + double age,agelim; + int theta; + + strcpy(fileresvpl,"vpl"); + strcat(fileresvpl,fileres); + if((ficresvpl=fopen(fileresvpl,"w"))==NULL) { + printf("Problem with variance prev lim resultfile: %s\n", fileresvpl); + exit(0); + } + printf("Computing Variance-covariance of Prevalence limit: file '%s' \n", fileresvpl); + + + fprintf(ficresvpl,"# Standard deviation of prevalences limit\n"); + fprintf(ficresvpl,"# Age"); + for(i=1; i<=nlstate;i++) + fprintf(ficresvpl," %1d-%1d",i,i); + fprintf(ficresvpl,"\n"); + + xp=vector(1,npar); + dnewm=matrix(1,nlstate,1,npar); + doldm=matrix(1,nlstate,1,nlstate); + + hstepm=1*YEARM; /* Every year of age */ + hstepm=hstepm/stepm; /* Typically in stepm units, if j= 2 years, = 2/6 months = 4 */ + agelim = AGESUP; + for (age=bage; age<=fage; age ++){ /* If stepm=6 months */ + nhstepm=(int) rint((agelim-age)*YEARM/stepm); /* Typically 20 years = 20*12/6=40 */ + if (stepm >= YEARM) hstepm=1; + nhstepm = nhstepm/hstepm; /* Typically 40/4=10 */ + gradg=matrix(1,npar,1,nlstate); + gp=vector(1,nlstate); + gm=vector(1,nlstate); + + for(theta=1; theta <=npar; theta++){ + for(i=1; i<=npar; i++){ /* Computes gradient */ + xp[i] = x[i] + (i==theta ?delti[theta]:0); + } + prevalim(prlim,nlstate,xp,age,oldm,savm,ftolpl); + for(i=1;i<=nlstate;i++) + gp[i] = prlim[i][i]; + + for(i=1; i<=npar; i++) /* Computes gradient */ + xp[i] = x[i] - (i==theta ?delti[theta]:0); + prevalim(prlim,nlstate,xp,age,oldm,savm,ftolpl); + for(i=1;i<=nlstate;i++) + gm[i] = prlim[i][i]; + + for(i=1;i<=nlstate;i++) + gradg[theta][i]= (gp[i]-gm[i])/2./delti[theta]; + } /* End theta */ + + trgradg =matrix(1,nlstate,1,npar); + + for(j=1; j<=nlstate;j++) + for(theta=1; theta <=npar; theta++) + trgradg[j][theta]=gradg[theta][j]; + + for(i=1;i<=nlstate;i++) + varpl[i][(int)age] =0.; + matprod2(dnewm,trgradg,1,nlstate,1,npar,1,npar,matcov); + matprod2(doldm,dnewm,1,nlstate,1,npar,1,nlstate,gradg); + for(i=1;i<=nlstate;i++) + varpl[i][(int)age] = doldm[i][i]; /* Covariances are useless */ + + fprintf(ficresvpl,"%.0f ",age ); + for(i=1; i<=nlstate;i++) + fprintf(ficresvpl," %.5f (%.5f)",prlim[i][i],sqrt(varpl[i][(int)age])); + fprintf(ficresvpl,"\n"); + free_vector(gp,1,nlstate); + free_vector(gm,1,nlstate); + free_matrix(gradg,1,npar,1,nlstate); + free_matrix(trgradg,1,nlstate,1,npar); + } /* End age */ + fclose(ficresvpl); + free_vector(xp,1,npar); + free_matrix(doldm,1,nlstate,1,npar); + free_matrix(dnewm,1,nlstate,1,nlstate); + +} + + + +/***********************************************/ +/**************** Main Program *****************/ +/***********************************************/ + +/*int main(int argc, char *argv[])*/ +int main() +{ + + int i,j, k, n=MAXN,iter,m,size; + double agedeb, agefin,hf; + double agemin=1.e20, agemax=-1.e20; + + double fret; + double **xi,tmp,delta; + + double dum; /* Dummy variable */ + double ***p3mat; + int *indx; + char line[MAXLINE], linepar[MAXLINE]; + char title[MAXLINE]; + char optionfile[FILENAMELENGTH], datafile[FILENAMELENGTH], filerespl[FILENAMELENGTH]; + char fileres[FILENAMELENGTH], filerespij[FILENAMELENGTH], filereso[FILENAMELENGTH]; + char filerest[FILENAMELENGTH]; + char fileregp[FILENAMELENGTH]; + char path[80],pathc[80],pathcd[80],pathtot[80]; + int firstobs=1, lastobs=10; + int sdeb, sfin; /* Status at beginning and end */ + int c, h , cpt,l; + int ju,jl, mi; + int i1,j1, k1,jk,aa,bb, stepsize; + int jnais,jdc,jint4,jint1,jint2,jint3,**outcome,**adl,*tab; + + int hstepm, nhstepm; + double bage, fage, age, agelim, agebase; + double ftolpl=FTOL; + double **prlim; + double *severity; + double ***param; /* Matrix of parameters */ + double *p; + double **matcov; /* Matrix of covariance */ + double ***delti3; /* Scale */ + double *delti; /* Scale */ + double ***eij, ***vareij; + double **varpl; /* Variances of prevalence limits by age */ + double *epj, vepp; + char version[80]="Imach version 0.64, May 2000, INED-EUROREVES "; + char *alph[]={"a","a","b","c","d","e"}, str[4]; + char z[1]="c"; +#include +#include + + /* long total_usecs; + struct timeval start_time, end_time; + + gettimeofday(&start_time, (struct timezone*)0); */ /* at first time */ + + + printf("\nIMACH, Version 0.64"); + printf("\nEnter the parameter file name: "); +#define windows 1 +#ifdef windows + scanf("%s",pathtot); + getcwd(pathcd, size); + cut(path,optionfile,pathtot); + chdir(path); + replace(pathc,path); +#endif +#ifdef unix + scanf("%s",optionfile); +#endif + +/*-------- arguments in the command line --------*/ + + strcpy(fileres,"r"); + strcat(fileres, optionfile); + + /*---------arguments file --------*/ + + if((ficpar=fopen(optionfile,"r"))==NULL) { + printf("Problem with optionfile %s\n",optionfile); + goto end; + } + + strcpy(filereso,"o"); + strcat(filereso,fileres); + if((ficparo=fopen(filereso,"w"))==NULL) { + printf("Problem with Output resultfile: %s\n", filereso);goto end; + } + +/*--------- index.htm --------*/ + + if((fichtm=fopen("index.htm","w"))==NULL) { + printf("Problem with index.htm \n");goto end; + } + + fprintf(fichtm,"

Outputs files

\n + - Observed prevalence in each state: p%s
\n +- Estimated parameters and the covariance matrix: %s
+ - Stationary prevalence in each state: pl%s
+ - Transition probabilities: pij%s
+ - Copy of the parameter file: o%s
+ - Life expectancies by age and initial health status: e%s
+ - Variances of life expectancies by age and initial health status: v%s
+ - Health expectancies with their variances: t%s
+ - Standard deviation of stationary prevalences: vpl%s

",fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres,fileres); + + fprintf(fichtm,"
Graphs

"); + +for(cpt=1; cpt +
",strtok(optionfile, "."),strtok(optionfile, "."),cpt); + for(cpt=1; cpt<=nlstate;cpt++) + fprintf(fichtm,"- Observed and stationary prevalence (with confident +interval) in state (%d): v%s%d.gif
+
",cpt,strtok(optionfile, "."),cpt,strtok(optionfile, "."),cpt); + + for(cpt=1; cpt<=nlstate;cpt++) + fprintf(fichtm,"- Health life expectancies by age and initial health state (%d): exp%s%d.gif
+
",cpt,strtok(optionfile, "."),cpt,strtok(optionfile, "."),cpt); + + fprintf(fichtm,"- Total life expectancy by age and + health expectancies in states (1) and (2): e%s.gif
+

",strtok(optionfile, "."),strtok(optionfile, ".")); + + +fclose(fichtm); + + /* Reads comments: lines beginning with '#' */ + while((c=getc(ficpar))=='#' && c!= EOF){ + ungetc(c,ficpar); + fgets(line, MAXLINE, ficpar); + puts(line); + fputs(line,ficparo); + } + ungetc(c,ficpar); + + fscanf(ficpar,"title=%s datafile=%s lastobs=%d firstpass=%d lastpass=%d\nftol=%lf stepm=%d ncov=%d nlstate=%d ndeath=%d maxwav=%d mle=%d weight=%d\n",title, datafile, &lastobs, &firstpass,&lastpass,&ftol, &stepm, &ncov, &nlstate,&ndeath, &maxwav, &mle, &weightopt); + printf("title=%s datafile=%s lastobs=%d firstpass=%d lastpass=%d\nftol=%e stepm=%d ncov=%d nlstate=%d ndeath=%d maxwav=%d mle=%d weight=%d\n", title, datafile, lastobs, firstpass,lastpass,ftol, stepm, ncov, nlstate,ndeath, maxwav, mle, weightopt); + fprintf(ficparo,"title=%s datafile=%s lastobs=%d firstpass=%d lastpass=%d\nftol=%e stepm=%d ncov=%d nlstate=%d ndeath=%d maxwav=%d mle=%d weight=%d\n", title, datafile, lastobs, firstpass,lastpass,ftol,stepm,ncov,nlstate,ndeath,maxwav, mle, weightopt); + + nvar=ncov-1; /* Suppressing age as a basic covariate */ + + /* Read guess parameters */ + /* Reads comments: lines beginning with '#' */ + while((c=getc(ficpar))=='#' && c!= EOF){ + ungetc(c,ficpar); + fgets(line, MAXLINE, ficpar); + puts(line); + fputs(line,ficparo); + } + ungetc(c,ficpar); + + param= ma3x(1,nlstate,1,nlstate+ndeath-1,1,ncov); + for(i=1; i <=nlstate; i++) + for(j=1; j <=nlstate+ndeath-1; j++){ + fscanf(ficpar,"%1d%1d",&i1,&j1); + fprintf(ficparo,"%1d%1d",i1,j1); + printf("%1d%1d",i,j); + for(k=1; k<=ncov;k++){ + fscanf(ficpar," %lf",¶m[i][j][k]); + printf(" %lf",param[i][j][k]); + fprintf(ficparo," %lf",param[i][j][k]); + } + fscanf(ficpar,"\n"); + printf("\n"); + fprintf(ficparo,"\n"); + } + + npar= (nlstate+ndeath-1)*nlstate*ncov; + p=param[1][1]; + + /* Reads comments: lines beginning with '#' */ + while((c=getc(ficpar))=='#' && c!= EOF){ + ungetc(c,ficpar); + fgets(line, MAXLINE, ficpar); + puts(line); + fputs(line,ficparo); + } + ungetc(c,ficpar); + + delti3= ma3x(1,nlstate,1,nlstate+ndeath-1,1,ncov); + delti=vector(1,npar); /* Scale of each paramater (output from hesscov) */ + for(i=1; i <=nlstate; i++){ + for(j=1; j <=nlstate+ndeath-1; j++){ + fscanf(ficpar,"%1d%1d",&i1,&j1); + printf("%1d%1d",i,j); + fprintf(ficparo,"%1d%1d",i1,j1); + for(k=1; k<=ncov;k++){ + fscanf(ficpar,"%le",&delti3[i][j][k]); + printf(" %le",delti3[i][j][k]); + fprintf(ficparo," %le",delti3[i][j][k]); + } + fscanf(ficpar,"\n"); + printf("\n"); + fprintf(ficparo,"\n"); + } + } + delti=delti3[1][1]; + + /* Reads comments: lines beginning with '#' */ + while((c=getc(ficpar))=='#' && c!= EOF){ + ungetc(c,ficpar); + fgets(line, MAXLINE, ficpar); + puts(line); + fputs(line,ficparo); + } + ungetc(c,ficpar); + + matcov=matrix(1,npar,1,npar); + for(i=1; i <=npar; i++){ + fscanf(ficpar,"%s",&str); + printf("%s",str); + fprintf(ficparo,"%s",str); + for(j=1; j <=i; j++){ + fscanf(ficpar," %le",&matcov[i][j]); + printf(" %.5le",matcov[i][j]); + fprintf(ficparo," %.5le",matcov[i][j]); + } + fscanf(ficpar,"\n"); + printf("\n"); + fprintf(ficparo,"\n"); + } + for(i=1; i <=npar; i++) + for(j=i+1;j<=npar;j++) + matcov[i][j]=matcov[j][i]; + + printf("\n"); + + + if(mle==1){ + /*-------- data file ----------*/ + if((ficres =fopen(fileres,"w"))==NULL) { + printf("Problem with resultfile: %s\n", fileres);goto end; + } + fprintf(ficres,"#%s\n",version); + + if((fic=fopen(datafile,"r"))==NULL) { + printf("Problem with datafile: %s\n", datafile);goto end; + } + + n= lastobs; + severity = vector(1,maxwav); + outcome=imatrix(1,maxwav+1,1,n); + num=ivector(1,n); + moisnais=vector(1,n); + annais=vector(1,n); + moisdc=vector(1,n); + andc=vector(1,n); + agedc=vector(1,n); + cod=ivector(1,n); + weight=vector(1,n); + for(i=1;i<=n;i++) weight[i]=1.0; /* Equal weights, 1 by default */ + mint=matrix(1,maxwav,1,n); + anint=matrix(1,maxwav,1,n); + covar=matrix(1,NCOVMAX,1,n); + s=imatrix(1,maxwav+1,1,n); + adl=imatrix(1,maxwav+1,1,n); + tab=ivector(1,NCOVMAX); + i=1; + while (fgets(line, MAXLINE, fic) != NULL) { + if ((i >= firstobs) && (i 0){ + if (s[m][i] == nlstate+1) { + if(agedc[i]>0) + agev[m][i]=agedc[i]; + else{ + printf("Warning negative age at death: %d line:%d\n",num[i],i); + agev[m][i]=-1; + } + } + else if(s[m][i] !=9){ /* Should no more exist */ + agev[m][i]=(mint[m][i]/12.+1./24.+anint[m][i])-(moisnais[i]/12.+1./24.+annais[i]); + if(mint[m][i]==99 || anint[m][i]==9999) + agev[m][i]=1; + else if(agev[m][i] agemax){ + agemax=agev[m][i]; + /* printf(" anint[%d][%d]=%.0f annais[%d]=%.0f, agemax=%.0f\n",m,i,anint[m][i], i,annais[i], agemax);*/ + } + /*agev[m][i]=anint[m][i]-annais[i];*/ + /* agev[m][i] = age[i]+2*m;*/ + } + else { /* =9 */ + agev[m][i]=1; + s[m][i]=-1; + } + } + else /*= 0 Unknown */ + agev[m][i]=1; + } + + } + for (i=1; i<=imx; i++) { + for(m=1; (m<= maxwav); m++){ + if (s[m][i] > (nlstate+ndeath)) { + printf("Error: Wrong value in nlstate or ndeath\n"); + goto end; + } + } + } + +printf("Total number of individuals= %d, Agemin = %.2f, Agemax= %.2f\n\n", imx, agemin, agemax); + + free_vector(severity,1,maxwav); + free_imatrix(outcome,1,maxwav+1,1,n); + free_vector(moisnais,1,n); + free_vector(annais,1,n); + free_matrix(mint,1,maxwav,1,n); + free_matrix(anint,1,maxwav,1,n); + free_vector(moisdc,1,n); + free_vector(andc,1,n); + + + wav=ivector(1,imx); + dh=imatrix(1,lastpass-firstpass+1,1,imx); + mw=imatrix(1,lastpass-firstpass+1,1,imx); + + /* Concatenates waves */ + concatwav(wav, dh, mw, s, agedc, agev, firstpass, lastpass, imx, nlstate, stepm); + + /* Calculates basic frequencies. Computes observed prevalence at single age + and prints on file fileres'p'. */ + freqsummary(fileres, agemin, agemax, s, agev, nlstate, imx); + + pmmij= matrix(1,nlstate+ndeath,1,nlstate+ndeath); /* creation */ + oldms= matrix(1,nlstate+ndeath,1,nlstate+ndeath); /* creation */ + newms= matrix(1,nlstate+ndeath,1,nlstate+ndeath); /* creation */ + savms= matrix(1,nlstate+ndeath,1,nlstate+ndeath); /* creation */ + oldm=oldms; newm=newms; savm=savms; /* Keeps fixed addresses to free */ + + /* For Powell, parameters are in a vector p[] starting at p[1] + so we point p on param[1][1] so that p[1] maps on param[1][1][1] */ + p=param[1][1]; /* *(*(*(param +1)+1)+0) */ + + mlikeli(ficres,p, npar, ncov, nlstate, ftol, func); + + + /*--------- results files --------------*/ + fprintf(ficres,"\ntitle=%s datafile=%s lastobs=%d firstpass=%d lastpass=%d\nftol=%e stepm=%d ncov=%d nlstate=%d ndeath=%d maxwav=%d mle=%d weight=%d\n", title, datafile, lastobs, firstpass,lastpass,ftol, stepm, ncov, nlstate, ndeath, maxwav, mle,weightopt); + + jk=1; + fprintf(ficres,"# Parameters\n"); + printf("# Parameters\n"); + for(i=1,jk=1; i <=nlstate; i++){ + for(k=1; k <=(nlstate+ndeath); k++){ + if (k != i) + { + printf("%d%d ",i,k); + fprintf(ficres,"%1d%1d ",i,k); + for(j=1; j <=ncov; j++){ + printf("%f ",p[jk]); + fprintf(ficres,"%f ",p[jk]); + jk++; + } + printf("\n"); + fprintf(ficres,"\n"); + } + } + } + + /* Computing hessian and covariance matrix */ + ftolhess=ftol; /* Usually correct */ + hesscov(matcov, p, npar, delti, ftolhess, func); + fprintf(ficres,"# Scales\n"); + printf("# Scales\n"); + for(i=1,jk=1; i <=nlstate; i++){ + for(j=1; j <=nlstate+ndeath; j++){ + if (j!=i) { + fprintf(ficres,"%1d%1d",i,j); + printf("%1d%1d",i,j); + for(k=1; k<=ncov;k++){ + printf(" %.5e",delti[jk]); + fprintf(ficres," %.5e",delti[jk]); + jk++; + } + printf("\n"); + fprintf(ficres,"\n"); + } + } + } + + k=1; + fprintf(ficres,"# Covariance\n"); + printf("# Covariance\n"); + for(i=1;i<=npar;i++){ + /* if (k>nlstate) k=1; + i1=(i-1)/(ncov*nlstate)+1; + fprintf(ficres,"%s%d%d",alph[k],i1,tab[i]); + printf("%s%d%d",alph[k],i1,tab[i]);*/ + fprintf(ficres,"%3d",i); + printf("%3d",i); + for(j=1; j<=i;j++){ + fprintf(ficres," %.5e",matcov[i][j]); + printf(" %.5e",matcov[i][j]); + } + fprintf(ficres,"\n"); + printf("\n"); + k++; + } + + while((c=getc(ficpar))=='#' && c!= EOF){ + ungetc(c,ficpar); + fgets(line, MAXLINE, ficpar); + puts(line); + fputs(line,ficparo); + } + ungetc(c,ficpar); + + fscanf(ficpar,"agemin=%lf agemax=%lf bage=%lf fage=%lf\n",&agemin,&agemax, &bage, &fage); + + if (fage <= 2) { + bage = agemin; + fage = agemax; + } + + fprintf(ficres,"# agemin agemax for life expectancy, bage fage (if mle==0 ie no data nor Max likelihood).\n"); + fprintf(ficres,"agemin=%.0f agemax=%.0f bage=%.0f fage=%.0f\n",agemin,agemax,bage,fage); +/*------------ gnuplot -------------*/ +chdir(pathcd); + if((ficgp=fopen("graph.gp","w"))==NULL) { + printf("Problem with file graph.gp");goto end; + } +#ifdef windows + fprintf(ficgp,"cd \"%s\" \n",pathc); +#endif + /* 1eme*/ + + for (cpt=1; cpt<= nlstate ; cpt ++) { +#ifdef windows + fprintf(ficgp,"set xlabel \"Age\" \nset ylabel \"Probability\" \nset ter gif small size 400,300\nplot [%.f:%.f] \"vpl%s\" u 1:%d \"\%%lf",agemin,fage,fileres,cpt*2); +#endif +#ifdef unix +fprintf(ficgp,"set xlabel \"Age\" \nset ylabel \"Probability\" \nplot [%.f:%.f] \"vpl%s\" u 1:%d \"\%%lf",agemin,fage,fileres,cpt*2); +#endif + for (i=1; i<= nlstate ; i ++) fprintf(ficgp," \%%lf (\%%lf)"); + fprintf(ficgp,"\" t\"Stationary prevalence\" w l 0,\"vpl%s\" u 1:($%d+2*$%d) \"\%%lf",fileres,2*cpt,cpt*2+1); + for (i=1; i<= nlstate ; i ++) fprintf(ficgp," \%%lf (\%%lf)"); + fprintf(ficgp,"\" t\"95\%% CI\" w l 1,\"vpl%s\" u 1:($%d-2*$%d) \"\%%lf",fileres,2*cpt,2*cpt+1); + for (i=1; i<= nlstate ; i ++) fprintf(ficgp," \%%lf (\%%lf)"); + fprintf(ficgp,"\" t\"\" w l 1,\"p%s\" u 1:($%d) t\"Observed prevalence \" w l 2",fileres,2+4*(cpt-1)); +#ifdef unix +fprintf(ficgp,"\nset ter gif small size 400,300"); +#endif +fprintf(ficgp,"\nset out \"v%s%d.gif\" \nreplot\n\n",strtok(optionfile, "."),cpt); + + } + /*2 eme*/ + + fprintf(ficgp,"set ylabel \"Years\" \nset ter gif small size 400,300\nplot [%.f:%.f] ",agemin,fage); + for (i=1; i<= nlstate+1 ; i ++) { +k=2*i; + fprintf(ficgp,"\"t%s\" u 1:%d \"\%%lf \%%lf (\%%lf) \%%lf (\%%lf)",fileres,k); + for (j=1; j< nlstate ; j ++) fprintf(ficgp," \%%lf (\%%lf)"); + if (i== 1) fprintf(ficgp,"\" t\"TLE\" w l ,"); + else fprintf(ficgp,"\" t\"LE in state (%d)\" w l ,",i-1); + fprintf(ficgp,"\"t%s\" u 1:($%d-2*$%d) \"\%%lf \%%lf (\%%lf) \%%lf (\%%lf)",fileres,k,k+1); + for (j=1; j< nlstate ; j ++) fprintf(ficgp," \%%lf (\%%lf)"); + fprintf(ficgp,"\" t\"\" w l 0,"); +fprintf(ficgp,"\"t%s\" u 1:($%d+2*$%d) \"\%%lf \%%lf (\%%lf) \%%lf (\%%lf)",fileres,k,k+1); + for (j=1; j< nlstate ; j ++) fprintf(ficgp," \%%lf (\%%lf)"); + if (i== (nlstate+1)) fprintf(ficgp,"\" t\"\" w l 0"); +else fprintf(ficgp,"\" t\"\" w l 0,"); + } + fprintf(ficgp,"\nset out \"e%s.gif\" \nreplot\n\n",strtok(optionfile, ".")); + + /*3eme*/ +for (cpt=1; cpt<= nlstate ; cpt ++) { + k=2+nlstate*(cpt-1); + fprintf(ficgp,"set ter gif small size 400,300\nplot [%.f:%.f] \"e%s\" u 1:%d t \"e%d1\" w l",agemin,fage,fileres,k,cpt); +for (i=1; i< nlstate ; i ++) { +fprintf(ficgp,",\"e%s\" u 1:%d t \"e%d%d\" w l",fileres,k+1,cpt,i+1); +} +fprintf(ficgp,"\nset out \"ex%s%d.gif\" \nreplot\n\n",strtok(optionfile, "."),cpt); +} + +/* CV preval stat */ +for (cpt=1; cpt=bage; agedeb--){ /* If stepm=6 months */ + nhstepm=(int) rint((agelim-agedeb)*YEARM/stepm); /* Typically 20 years = 20*12/6=40 */ + nhstepm = nhstepm/hstepm; /* Typically 40/4=10 */ + p3mat=ma3x(1,nlstate+ndeath,1, nlstate+ndeath, 0,nhstepm); + oldm=oldms;savm=savms; + hpxij(p3mat,nhstepm,agedeb,hstepm,p,nlstate,stepm,oldm,savm); + fprintf(ficrespij,"# Age"); + for(i=1; i<=nlstate;i++) + for(j=1; j<=nlstate+ndeath;j++) + fprintf(ficrespij," %1d-%1d",i,j); + fprintf(ficrespij,"\n"); + for (h=0; h<=nhstepm; h++){ + fprintf(ficrespij,"%.0f %.0f",agedeb, agedeb+ h*hstepm/YEARM*stepm ); + for(i=1; i<=nlstate;i++) + for(j=1; j<=nlstate+ndeath;j++) + fprintf(ficrespij," %.5f", p3mat[i][j][h]); + fprintf(ficrespij,"\n"); + } + free_ma3x(p3mat,1,nlstate+ndeath,1, nlstate+ndeath, 0,nhstepm); + fprintf(ficrespij,"\n"); + } + fclose(ficrespij); + + /*---------- Health expectancies and variances ------------*/ + + eij=ma3x(1,nlstate,1,nlstate,(int) bage, (int) fage); + oldm=oldms;savm=savms; + evsij(fileres, eij, p, nlstate, stepm, (int) bage, (int)fage, oldm, savm); + + vareij=ma3x(1,nlstate,1,nlstate,(int) bage, (int) fage); + oldm=oldms;savm=savms; + varevsij(fileres, vareij, matcov, p, delti, nlstate, stepm, (int) bage, (int) fage, oldm, savm, prlim, ftolpl); + + strcpy(filerest,"t"); + strcat(filerest,fileres); + if((ficrest=fopen(filerest,"w"))==NULL) { + printf("Problem with total LE resultfile: %s\n", filerest);goto end; + } + printf("Computing Total LEs with variances: file '%s' \n", filerest); + fprintf(ficrest,"#Total LEs with variances: e.. (std) "); + for (i=1;i<=nlstate;i++) fprintf(ficrest,"e.%d (std) ",i); + fprintf(ficrest,"\n"); + + hf=1; + if (stepm >= YEARM) hf=stepm/YEARM; + epj=vector(1,nlstate+1); + for(age=bage; age <=fage ;age++){ + prevalim(prlim, nlstate, p, age, oldm, savm,ftolpl); + fprintf(ficrest," %.0f",age); + for(j=1, epj[nlstate+1]=0.;j <=nlstate;j++){ + for(i=1, epj[j]=0.;i <=nlstate;i++) { + epj[j] += prlim[i][i]*hf*eij[i][j][(int)age]; + } + epj[nlstate+1] +=epj[j]; + } + for(i=1, vepp=0.;i <=nlstate;i++) + for(j=1;j <=nlstate;j++) + vepp += vareij[i][j][(int)age]; + fprintf(ficrest," %.2f (%.2f)", epj[nlstate+1],hf*sqrt(vepp)); + for(j=1;j <=nlstate;j++){ + fprintf(ficrest," %.2f (%.2f)", epj[j],hf*sqrt(vareij[j][j][(int)age])); + } + fprintf(ficrest,"\n"); + } + fclose(ficrest); + fclose(ficpar); + free_vector(epj,1,nlstate+1); + + /*------- Variance limit prevalence------*/ + + varpl=matrix(1,nlstate,(int) bage, (int) fage); + oldm=oldms;savm=savms; + varprevlim(fileres, varpl, matcov, p, delti, nlstate, stepm, (int) bage, (int) fage, oldm, savm, prlim, ftolpl); + + + free_matrix(varpl,1,nlstate,(int) bage, (int)fage); + + free_ma3x(vareij,1,nlstate,1,nlstate,(int) bage, (int)fage); + free_ma3x(eij,1,nlstate,1,nlstate,(int) bage, (int)fage); + + + free_matrix(pmmij,1,nlstate+ndeath,1,nlstate+ndeath); + free_matrix(oldms, 1,nlstate+ndeath,1,nlstate+ndeath); + free_matrix(newms, 1,nlstate+ndeath,1,nlstate+ndeath); + free_matrix(savms, 1,nlstate+ndeath,1,nlstate+ndeath); + + free_matrix(matcov,1,npar,1,npar); + free_vector(delti,1,npar); + + free_ma3x(param,1,nlstate,1, nlstate+ndeath-1,1,ncov); + + printf("End of Imach\n"); + /* gettimeofday(&end_time, (struct timezone*)0);*/ /* after time */ + + /* printf("Total time was %d Sec. %d uSec.\n", end_time.tv_sec -start_time.tv_sec, end_time.tv_usec -start_time.tv_usec);*/ + /*printf("Total time was %d uSec.\n", total_usecs);*/ + /*------ End -----------*/ + + end: +#ifdef windows + chdir(pathcd); +#endif + system("gnuplot graph.gp"); + +#ifdef windows + while (z[0] != 'q') { + chdir(pathcd); + printf("\nType e to edit output files, c to start again, and q for exiting: "); + scanf("%s",z); + if (z[0] == 'c') system("./imach"); + else if (z[0] == 'e') { + chdir(path); + system("index.htm"); + } + else if (z[0] == 'q') exit(0); + } +#endif +} + diff --git a/src/imach.htm b/src/imach.htm new file mode 100644 index 0000000..eeb7597 --- /dev/null +++ b/src/imach.htm @@ -0,0 +1,1024 @@ + + + + + +Computing Health Expectancies using IMaCh + + + + +

+ +

Computing Health +Expectancies using IMaCh

+ +

(a Maximum +Likelihood Computer Program using Interpolation of Markov Chains)

+ +

INED and EUROREVES

+ +

March +2000

+ +

Authors of the +program: Nicolas Brouard, senior researcher at the Institut +National d'Etudes Démographiques (INED, Paris) in the "Mortality, +Health and Epidemiology" Research Unit

+ +

and Agnès +Lièvre
+

+ +

Contribution to the mathematics: C. R. +Heathcote (Australian +National University, Canberra).

+ +

Contact: Agnès Lièvre (lievre@ined.fr)

+ +

Introduction
The detailed statistical model (PDF + version),(ps version)
On what kind of data can it be used?
The data file
The parameter file
Running Imach
Output files and graphs
Exemple

+ +

Introduction

+ +

This program computes Healthy Life Expectancies from cross-longitudinal +data. Within the family of Health Expectancies (HE), +Disability-free life expectancy (DFLE) is probably the most +important index to monitor. In low mortality countries, there is +a fear that when mortality declines, the increase in DFLE is not +proportionate to the increase in total Life expectancy. This case +is called the Expansion of morbidity. Most of the data +collected today, in particular by the international REVES network on Health +expectancy, and most HE indices based on these data, are cross-sectional. +It means that the information collected comes from a single +cross-sectional survey: people from various ages (but mostly old +people) are surveyed on their health status at a single date. +Proportion of people disabled at each age, can then be measured +at that date. This age-specific prevalence curve is then used to +distinguish, within the stationary population (which, by +definition, is the life table estimated from the vital statistics +on mortality at the same date), the disable population from the +disability-free population. Life expectancy (LE) (or total +population divided by the yearly number of births or deaths of +this stationary population) is then decomposed into DFLE and DLE. +This method of computing HE is usually called the Sullivan method +(from the name of the author who first described it).

+ +

Age-specific proportions of people disable are very difficult +to forecast because each proportion corresponds to historical +conditions of the cohort and it is the result of the historical +flows from entering disability and recovering in the past until +today. The age-specific intensities (or incidence rates) of +entering disability or recovering a good health, are reflecting +actual conditions and therefore can be used at each age to +forecast the future of this cohort. For example if a country is +improving its technology of prosthesis, the incidence of +recovering the ability to walk will be higher at each (old) age, +but the prevalence of disability will only slightly reflect an +improve because the prevalence is mostly affected by the history +of the cohort and not by recent period effects. To measure the +period improvement we have to simulate the future of a cohort of +new-borns entering or leaving at each age the disability state or +dying according to the incidence rates measured today on +different cohorts. The proportion of people disabled at each age +in this simulated cohort will be much lower (using the exemple of +an improvement) that the proportions observed at each age in a +cross-sectional survey. This new prevalence curve introduced in a +life table will give a much more actual and realistic HE level +than the Sullivan method which mostly measured the History of +health conditions in this country.

+ +

Therefore, the main question is how to measure incidence rates +from cross-longitudinal surveys? This is the goal of the IMaCH +program. From your data and using IMaCH you can estimate period +HE and not only Sullivan's HE. Also the standard errors of the HE +are computed.

+ +

A cross-longitudinal survey consists in a first survey +("cross") where individuals from different ages are +interviewed on their health status or degree of disability. At +least a second wave of interviews ("longitudinal") +should measure each new individual health status. Health +expectancies are computed from the transitions observed between +waves and are computed for each degree of severity of disability +(number of life states). More degrees you consider, more time is +necessary to reach the Maximum Likelihood of the parameters +involved in the model. Considering only two states of disability +(disable and healthy) is generally enough but the computer +program works also with more health statuses.
+
+The simplest model is the multinomial logistic model where pij +is the probability to be observed in state j at the second +wave conditional to be observed in state i at the first +wave. Therefore a simple model is: log(pij/pii)= aij + +bij*age+ cij*sex, where 'age' is age and 'sex' +is a covariate. The advantage that this computer program claims, +comes from that if the delay between waves is not identical for +each individual, or if some individual missed an interview, the +information is not rounded or lost, but taken into account using +an interpolation or extrapolation. hPijx is the +probability to be observed in state i at age x+h +conditional to the observed state i at age x. The +delay 'h' can be split into an exact number (nh*stepm) +of unobserved intermediate states. This elementary transition (by +month or quarter trimester, semester or year) is modeled as a +multinomial logistic. The hPx matrix is simply the matrix +product of nh*stepm elementary matrices and the +contribution of each individual to the likelihood is simply hPijx. +
+

+ +

The program presented in this manual is a quite general +program named IMaCh (for Interpolated +MArkov CHain), designed to +analyse transition data from longitudinal surveys. The first step +is the parameters estimation of a transition probabilities model +between an initial status and a final status. From there, the +computer program produces some indicators such as observed and +stationary prevalence, life expectancies and their variances and +graphs. Our transition model consists in absorbing and +non-absorbing states with the possibility of return across the +non-absorbing states. The main advantage of this package, +compared to other programs for the analysis of transition data +(For example: Proc Catmod of SAS^®) is that the whole +individual information is used even if an interview is missing, a +status or a date is unknown or when the delay between waves is +not identical for each individual. The program can be executed +according to parameters: selection of a sub-sample, number of +absorbing and non-absorbing states, number of waves taken in +account (the user inputs the first and the last interview), a +tolerance level for the maximization function, the periodicity of +the transitions (we can compute annual, quaterly or monthly +transitions), covariates in the model. It works on Windows or on +Unix.
+

+ +

On what kind of data can +it be used?

+ +

The minimum data required for a transition model is the +recording of a set of individuals interviewed at a first date and +interviewed again at least one another time. From the +observations of an individual, we obtain a follow-up over time of +the occurrence of a specific event. In this documentation, the +event is related to health status at older ages, but the program +can be applied on a lot of longitudinal studies in different +contexts. To build the data file explained into the next section, +you must have the month and year of each interview and the +corresponding health status. But in order to get age, date of +birth (month and year) is required (missing values is allowed for +month). Date of death (month and year) is an important +information also required if the individual is dead. Shorter +steps (i.e. a month) will more closely take into account the +survival time after the last interview.

+ +

The data file

+ +

In this example, 8,000 people have been interviewed in a +cross-longitudinal survey of 4 waves (1984, 1986, 1988, 1990). +Some people missed 1, 2 or 3 interviews. Health statuses are +healthy (1) and disable (2). The survey is not a real one. It is +a simulation of the American Longitudinal Survey on Aging. The +disability state is defined if the individual missed one of four +ADL (Activity of daily living, like bathing, eating, walking). +Therefore, even is the individuals interviewed in the sample are +virtual, the information brought with this sample is close to the +situation of the United States. Sex is not recorded is this +sample.

+ +

Each line of the data set (named data1.txt +in this first example) is an individual record which fields are:

+ +

Index number: positive number (field 1)
First covariate positive number (field 2)
Second covariate positive number (field 3)
Weight: positive number + (field 4) . In most surveys individuals are weighted + according to the stratification of the sample.
Date of birth: coded as mm/yyyy. Missing dates are + coded as 99/9999 (field 5)
Date of death: coded as mm/yyyy. Missing dates are + coded as 99/9999 (field 6)
Date of first interview: coded as mm/yyyy. Missing + dates are coded as 99/9999 (field 7)
Status at first interview: positive number. + Missing values ar coded -1. (field 8)
Date of second interview: coded as mm/yyyy. + Missing dates are coded as 99/9999 (field 9)
Status at second interview positive + number. Missing values ar coded -1. (field 10)
Date of third interview: coded as mm/yyyy. Missing + dates are coded as 99/9999 (field 11)
Status at third interview positive + number. Missing values ar coded -1. (field 12)
Date of fourth interview: coded as mm/yyyy. + Missing dates are coded as 99/9999 (field 13)
Status at fourth interview positive + number. Missing values are coded -1. (field 14)
etc

+ +

If your longitudinal survey do not include information about +weights or covariates, you must fill the column with a number +(e.g. 1) because a missing field is not allowed.

+ +

Your first example parameter file

+ +

#Imach version 0.63, February 2000, +INED-EUROREVES

+ +

This is a comment. Comments start with a '#'.

+ +

First uncommented line

+ +

title=1st_example datafile=data1.txt lastobs=8600 firstpass=1 lastpass=4

+ +

title= 1st_example is title of the run.
datafile=data1.txt is the name of the data set. + Our example is a six years follow-up survey. It consists + in a baseline followed by 3 reinterviews.
lastobs= 8600 the program is able to run on a + subsample where the last observation number is lastobs. + It can be set a bigger number than the real number of + observations (e.g. 100000). In this example, maximisation + will be done on the 8600 first records.
firstpass=1 , lastpass=4 In case of more + than two interviews in the survey, the program can be run + on selected transitions periods. firstpass=1 means the + first interview included in the calculation is the + baseline survey. lastpass=4 means that the information + brought by the 4th interview is taken into account.

+ +

Second uncommented +line

+ +

ftol=1.e-08 stepm=1 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0

+ +

ftol=1e-8 Convergence tolerance on the function + value in the maximisation of the likelihood. Choosing a + correct value for ftol is difficult. 1e-8 is a correct + value for a 32 bits computer.
stepm=1 Time unit in months for interpolation. + Examples:
- If stepm=1, the unit is a month
- If stepm=4, the unit is a trimester
- If stepm=12, the unit is a year
- If stepm=24, the unit is two years
- ...
+
ncov=2 Number of covariates to be add to the + model. The intercept and the age parameter are counting + for 2 covariates. For example, if you want to add gender + in the covariate vector you must write ncov=3 else + ncov=2.
nlstate=2 Number of non-absorbing (live) states. + Here we have two alive states: disability-free is coded 1 + and disability is coded 2.
ndeath=1 Number of absorbing states. The absorbing + state death is coded 3.
maxwav=4 Maximum number of waves. The program can + not include more than 4 interviews.
mle=1 Option for the + Maximisation Likelihood Estimation.
- If mle=1 the program does the maximisation and + the calculation of heath expectancies
- If mle=0 the program only does the calculation of + the health expectancies.
+
weight=0 Possibility to add weights.
- If weight=0 no weights are included
- If weight=1 the maximisation integrates the + weights which are in field 4
+

+ +

Guess values for optimization

+ +

You must write the initial guess values of the parameters for +optimization. The number of parameters, N depends on the +number of absorbing states and non-absorbing states and on the +number of covariates.
+N is given by the formula N=(nlstate + +ndeath-1)*nlstate*ncov .
+
+Thus in the simple case with 2 covariates (the model is log +(pij/pii) = aij + bij * age where intercept and age are the two +covariates), and 2 health degrees (1 for disability-free and 2 +for disability) and 1 absorbing state (3), you must enter 8 +initials values, a12, b12, a13, b13, a21, b21, a23, b23. You can +start with zeros as in this example, but if you have a more +precise set (for example from an earlier run) you can enter it +and it will speed up them
+Each of the four lines starts with indices "ij":
+
+ij aij bij

+ +

# Guess values of aij and bij in log (pij/pii) = aij + bij * age
+12 -14.155633  0.110794 
+13  -7.925360  0.032091 
+21  -1.890135 -0.029473 
+23  -6.234642  0.022315

+ +

or, to simplify:

+ +

12 0.0 0.0
+13 0.0 0.0
+21 0.0 0.0
+23 0.0 0.0

+ +

Guess values for computing variances

+ +

This is an output if mle=1. But it can be +used as an input to get the vairous output data files (Health +expectancies, stationary prevalence etc.) and figures without +rerunning the rather long maximisation phase (mle=0).

+ +

The scales are small values for the evaluation of numerical +derivatives. These derivatives are used to compute the hessian +matrix of the parameters, that is the inverse of the covariance +matrix, and the variances of health expectancies. Each line +consists in indices "ij" followed by the initial scales +(zero to simplify) associated with aij and bij.

+ +

If mle=1 you can enter zeros:

+ +

# Scales (for hessian or gradient estimation)
+12 0. 0. 
+13 0. 0. 
+21 0. 0. 
+23 0. 0.

+ +

If mle=0 you must enter a covariance matrix (usually + obtained from an earlier run).

+ +

Covariance matrix of parameters

+ +

Each line starts with indices "ijk" followed by the +covariances between aij and bij:

+ +

+   121 Var(a12) 
+   122 Cov(b12,a12)  Var(b12) 
+          ...
+   232 Cov(b23,a12)  Cov(b23,b12) ... Var (b23)

+ +

If mle=1 you can enter zeros.

+ +

# Covariance matrix
+121 0.
+122 0. 0.
+131 0. 0. 0. 
+132 0. 0. 0. 0. 
+211 0. 0. 0. 0. 0. 
+212 0. 0. 0. 0. 0. 0. 
+231 0. 0. 0. 0. 0. 0. 0. 
+232 0. 0. 0. 0. 0. 0. 0. 0.

+ +

If mle=0 you must enter a covariance matrix (usually + obtained from an earlier run).
+

+ +

last +uncommented line

+ +

agemin=70 agemax=100 bage=50 fage=100

+ +

Once we obtained the estimated parameters, the program is able +to calculated stationary prevalence, transitions probabilities +and life expectancies at any age. Choice of age ranges is useful +for extrapolation. In our data file, ages varies from age 70 to +102. Setting bage=50 and fage=100, makes the program computing +life expectancy from age bage to age fage. As we use a model, we +can compute life expectancy on a wider age range than the age +range from the data. But the model can be rather wrong on big +intervals.

+ +

Similarly, it is possible to get extrapolated stationary +prevalence by age raning from agemin to agemax.

+ +

agemin= Minimum age for calculation of the + stationary prevalence
agemax= Maximum age for calculation of the + stationary prevalence
bage= Minimum age for calculation of the health + expectancies
fage= Maximum ages for calculation of the health + expectancies

+ +

Running Imach +with this example

+ +

We assume that you entered your 1st_example +parameter file as explained above. To +run the program you should click on the imach.exe icon and enter +the name of the parameter file which is for example C:\usr\imach\mle\biaspar.txt +(you also can click on the biaspar.txt icon located in
+C:\usr\imach\mle and put it with +the mouse on the imach window).
+

+ +

The time to converge depends on the step unit that you used (1 +month is cpu consuming), on the number of cases, and on the +number of variables.

+ +

The program outputs many files. Most of them are files which +will be plotted for better understanding.

+ +

Output of the program +and graphs

+ +

Once the optimization is finished, some graphics can be made +with a grapher. We use Gnuplot which is an interactive plotting +program copyrighted but freely distributed. Imach outputs the +source of a gnuplot file, named 'graph.gp', which can be directly +input into gnuplot.
+When the running is finished, the user should enter a caracter +for plotting and output editing.

+ +

These caracters are:

+ +

'c' to start again the program from the beginning.
'g' to made graphics. The output graphs are in GIF format + and you have no control over which is produced. If you + want to modify the graphics or make another one, you + should modify the parameters in the file graph.gp + located in imach\bin. A gnuplot reference manual is + available here. +
'e' opens the index.htm file to edit the + output files and graphs.
'q' for exiting.

+ +

Results files
+
+- Observed prevalence in each state (and at first pass): +prbiaspar.txt
+

+ +

The first line is the title and displays each field of the +file. The first column is age. The fields 2 and 6 are the +proportion of individuals in states 1 and 2 respectively as +observed during the first exam. Others fields are the numbers of +people in states 1, 2 or more. The number of columns increases if +the number of states is higher than 2.
+The header of the file is

+ +

# Age Prev(1) N(1) N Age Prev(2) N(2) N
+70 1.00000 631 631 70 0.00000 0 631
+71 0.99681 625 627 71 0.00319 2 627 
+72 0.97125 1115 1148 72 0.02875 33 1148

+ +

# Age Prev(1) N(1) N Age Prev(2) N(2) N
+    70 0.95721 604 631 70 0.04279 27 631

+ +

It means that at age 70, the prevalence in state 1 is 1.000 +and in state 2 is 0.00 . At age 71 the number of individuals in +state 1 is 625 and in state 2 is 2, hence the total number of +people aged 71 is 625+2=627.
+

+ +

- Estimated parameters and +covariance matrix: rbiaspar.txt

+ +

This file contains all the maximisation results:

+ +

 Number of iterations=47
+ -2 log likelihood=46553.005854373667  
+ Estimated parameters: a12 = -12.691743 b12 = 0.095819 
+                       a13 = -7.815392   b13 = 0.031851 
+                       a21 = -1.809895 b21 = -0.030470 
+                       a23 = -7.838248  b23 = 0.039490  
+ Covariance matrix: Var(a12) = 1.03611e-001
+                    Var(b12) = 1.51173e-005
+                    Var(a13) = 1.08952e-001
+                    Var(b13) = 1.68520e-005  
+                    Var(a21) = 4.82801e-001
+                    Var(b21) = 6.86392e-005
+                    Var(a23) = 2.27587e-001
+                    Var(b23) = 3.04465e-005 
+

+ +

- Transition probabilities: +pijrbiaspar.txt

+ +

Here are the transitions probabilities Pij(x, x+nh) where nh +is a multiple of 2 years. The first column is the starting age x +(from age 50 to 100), the second is age (x+nh) and the others are +the transition probabilities p11, p12, p13, p21, p22, p23. For +example, line 5 of the file is:

+ +

 100 106 0.03286 0.23512 0.73202 0.02330 0.19210 0.78460

+ +

and this means:

+ +

p11(100,106)=0.03286
+p12(100,106)=0.23512
+p13(100,106)=0.73202
+p21(100,106)=0.02330
+p22(100,106)=0.19210 
+p22(100,106)=0.78460

+ +

- Stationary prevalence in each state: +plrbiaspar.txt

+ +

#Age 1-1 2-2 
+70 0.92274 0.07726 
+71 0.91420 0.08580 
+72 0.90481 0.09519 
+73 0.89453 0.10547

+ +

At age 70 the stationary prevalence is 0.92274 in state 1 and +0.07726 in state 2. This stationary prevalence differs from +observed prevalence. Here is the point. The observed prevalence +at age 70 results from the incidence of disability, incidence of +recovery and mortality which occurred in the past of the cohort. +Stationary prevalence results from a simulation with actual +incidences and mortality (estimated from this cross-longitudinal +survey). It is the best predictive value of the prevalence in the +future if "nothing changes in the future". This is +exactly what demographers do with a Life table. Life expectancy +is the expected mean time to survive if observed mortality rates +(incidence of mortality) "remains constant" in the +future.

+ +

- Standard deviation of +stationary prevalence: vplrbiaspar.txt

+ +

The stationary prevalence has to be compared with the observed +prevalence by age. But both are statistical estimates and +subjected to stochastic errors due to the size of the sample, the +design of the survey, and, for the stationary prevalence to the +model used and fitted. It is possible to compute the standard +deviation of the stationary prevalence at each age.

+ +

Observed and stationary +prevalence in state (2=disable) with the confident interval: +vbiaspar2.gif

+ +

+This graph exhibits the stationary prevalence in state (2) with +the confidence interval in red. The green curve is the observed +prevalence (or proportion of individuals in state (2)). Without +discussing the results (it is not the purpose here), we observe +that the green curve is rather below the stationary prevalence. +It suggests an increase of the disability prevalence in the +future.

+ +

Convergence to the +stationary prevalence of disability: pbiaspar1.gif
+

+ +

This graph plots the conditional transition probabilities from +an initial state (1=healthy in red at the bottom, or 2=disable in +green on top) at age x to the final state 2=disable at +age x+h. Conditional means at the condition to be alive +at age x+h which is hP12x + hP22x. The +curves hP12x/(hP12x + hP22x) and hP22x/(hP12x ++ hP22x) converge with h, to the stationary +prevalence of disability. In order to get the stationary +prevalence at age 70 we should start the process at an earlier +age, i.e.50. If the disability state is defined by severe +disability criteria with only a few chance to recover, then the +incidence of recovery is low and the time to convergence is +probably longer. But we don't have experience yet.

+ +

- Life expectancies by age +and initial health status: erbiaspar.txt

+ +

# Health expectancies 
+# Age 1-1 1-2 2-1 2-2 
+70 10.7297 2.7809 6.3440 5.9813 
+71 10.3078 2.8233 5.9295 5.9959 
+72 9.8927 2.8643 5.5305 6.0033 
+73 9.4848 2.9036 5.1474 6.0035

+ +

For example 70 10.7297 2.7809 6.3440 5.9813 means:
+e11=10.7297 e12=2.7809 e21=6.3440 e22=5.9813

+ +

For example, life expectancy of a healthy individual at age 70 +is 10.73 in the healthy state and 2.78 in the disability state +(=13.51 years). If he was disable at age 70, his life expectancy +will be shorter, 6.34 in the healthy state and 5.98 in the +disability state (=12.32 years). The total life expectancy is a +weighted mean of both, 13.51 and 12.32; weight is the proportion +of people disabled at age 70. In order to get a pure period index +(i.e. based only on incidences) we use the computed or +stationary prevalence at age 70 (i.e. computed from +incidences at earlier ages) instead of the observed prevalence +(for example at first exam) (see +below).

+ +

- Variances of life +expectancies by age and initial health status: vrbiaspar.txt

+ +

For example, the covariances of life expectancies Cov(ei,ej) +at age 50 are (line 3)

+ +

   Cov(e1,e1)=0.4667  Cov(e1,e2)=0.0605=Cov(e2,e1)  Cov(e2,e2)=0.0183

+ +

- Health +expectancies +with standard errors in parentheses: trbiaspar.txt

+ +

#Total LEs with variances: e.. (std) e.1 (std) e.2 (std)

+ +

70 13.42 (0.18) 10.39 (0.15) 3.03 (0.10)70 13.81 (0.18) 11.28 (0.14) 2.53 (0.09)

+ +

Thus, at age 70 the total life expectancy, e..=13.42 years is +the weighted mean of e1.=13.51 and e2.=12.32 by the stationary +prevalence at age 70 which are 0.92274 in state 1 and 0.07726 in +state 2, respectively (the sum is equal to one). e.1=10.39 is the +Disability-free life expectancy at age 70 (it is again a weighted +mean of e11 and e21). e.2=3.03 is also the life expectancy at age +70 to be spent in the disability state.

+ +

Total life expectancy by +age and health expectancies in states (1=healthy) and (2=disable): +ebiaspar.gif

+ +

This figure represents the health expectancies and the total +life expectancy with the confident interval in dashed curve.

+ +

Standard deviations (obtained from the information matrix of +the model) of these quantities are very useful. +Cross-longitudinal surveys are costly and do not involve huge +samples, generally a few thousands; therefore it is very +important to have an idea of the standard deviation of our +estimates. It has been a big challenge to compute the Health +Expectancy standard deviations. Don't be confuse: life expectancy +is, as any expected value, the mean of a distribution; but here +we are not computing the standard deviation of the distribution, +but the standard deviation of the estimate of the mean.

+ +

Our health expectancies estimates vary according to the sample +size (and the standard deviations give confidence intervals of +the estimate) but also according to the model fitted. Let us +explain it in more details.

+ +

Choosing a model means ar least two kind of choices. First we +have to decide the number of disability states. Second we have to +design, within the logit model family, the model: variables, +covariables, confonding factors etc. to be included.

+ +

More disability states we have, better is our demographical +approach of the disability process, but smaller are the number of +transitions between each state and higher is the noise in the +measurement. We do not have enough experiments of the various +models to summarize the advantages and disadvantages, but it is +important to say that even if we had huge and unbiased samples, +the total life expectancy computed from a cross-longitudinal +survey, varies with the number of states. If we define only two +states, alive or dead, we find the usual life expectancy where it +is assumed that at each age, people are at the same risk to die. +If we are differentiating the alive state into healthy and +disable, and as the mortality from the disability state is higher +than the mortality from the healthy state, we are introducing +heterogeneity in the risk of dying. The total mortality at each +age is the weighted mean of the mortality in each state by the +prevalence in each state. Therefore if the proportion of people +at each age and in each state is different from the stationary +equilibrium, there is no reason to find the same total mortality +at a particular age. Life expectancy, even if it is a very useful +tool, has a very strong hypothesis of homogeneity of the +population. Our main purpose is not to measure differential +mortality but to measure the expected time in a healthy or +disability state in order to maximise the former and minimize the +latter. But the differential in mortality complexifies the +measurement.

+ +

Incidences of disability or recovery are not affected by the +number of states if these states are independant. But incidences +estimates are dependant on the specification of the model. More +covariates we added in the logit model better is the model, but +some covariates are not well measured, some are confounding +factors like in any statistical model. The procedure to "fit +the best model' is similar to logistic regression which itself is +similar to regression analysis. We haven't yet been sofar because +we also have a severe limitation which is the speed of the +convergence. On a Pentium III, 500 MHz, even the simplest model, +estimated by month on 8,000 people may take 4 hours to converge. +Also, the program is not yet a statistical package, which permits +a simple writing of the variables and the model to take into +account in the maximisation. The actual program allows only to +add simple variables without covariations, like age+sex but +without age+sex+ age*sex . This can be done from the source code +(you have to change three lines in the source code) but will +never be general enough. But what is to remember, is that +incidences or probability of change from one state to another is +affected by the variables specified into the model.

+ +

Also, the age range of the people interviewed has a link with +the age range of the life expectancy which can be estimated by +extrapolation. If your sample ranges from age 70 to 95, you can +clearly estimate a life expectancy at age 70 and trust your +confidence interval which is mostly based on your sample size, +but if you want to estimate the life expectancy at age 50, you +should rely in your model, but fitting a logistic model on a age +range of 70-95 and estimating probabilties of transition out of +this age range, say at age 50 is very dangerous. At least you +should remember that the confidence interval given by the +standard deviation of the health expectancies, are under the +strong assumption that your model is the 'true model', which is +probably not the case.

+ +

- Copy of the parameter +file: orbiaspar.txt

+ +

This copy of the parameter file can be useful to re-run the +program while saving the old output files.

+ +

Trying an example

+ +

Since you know how to run the program, it is time to test it +on your own computer. Try for example on a parameter file named imachpar.txt which is a +copy of mypar.txt +included in the subdirectory of imach, mytry. Edit it to change the name of +the data file to ..\data\mydata.txt +if you don't want to copy it on the same directory. The file mydata.txt is a smaller file of 3,000 +people but still with 4 waves.

+ +

Click on the imach.exe icon to open a window. Answer to the +question:'Enter the parameter file name:'

+ + + + + +

IMACH, Version 0.63

Enter + the parameter file name: ..\mytry\imachpar.txt

+ +

Most of the data files or image files generated, will use the +'imachpar' string into their name. The running time is about 2-3 +minutes on a Pentium III. If the execution worked correctly, the +outputs files are created in the current directory, and should be +the same as the mypar files initially included in the directory mytry.

+ +

Output on the screen The output screen looks like this Log file
+#
+
+title=MLE datafile=..\data\mydata.txt lastobs=3000 firstpass=1 lastpass=3
+ftol=1.000000e-008 stepm=24 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0

Total number of individuals= 2965, Agemin = 70.00, Agemax= 100.92
+
+Warning, no any valid information for:126 line=126
+Warning, no any valid information for:2307 line=2307
+Delay (in months) between two waves Min=21 Max=51 Mean=24.495826
+These lines give some warnings on the data file and also some raw statistics on frequencies of transitions.
+Age 70 1.=230 loss[1]=3.5% 2.=16 loss[2]=12.5% 1.=222 prev[1]=94.1% 2.=14
+ prev[2]=5.9% 1-1=8 11=200 12=7 13=15 2-1=2 21=6 22=7 23=1
+Age 102 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0

+ +

Maximisation with the Powell algorithm. 8 directions are + given corresponding to the 8 parameters. this can be + rather long to get convergence.
+
+ Powell iter=1 -2*LL=11531.405658264877 1 0.000000000000 2 + 0.000000000000 3
+ 0.000000000000 4 0.000000000000 5 0.000000000000 6 + 0.000000000000 7
+ 0.000000000000 8 0.000000000000
+ 1..........2.................3..........4.................5.........
+ 6................7........8...............
+ Powell iter=23 -2*LL=6744.954108371555 1 -12.967632334283 +
+ 2 0.135136681033 3 -7.402109728262 4 0.067844593326
+ 5 -0.673601538129 6 -0.006615504377 7 -5.051341616718
+ 8 0.051272038506
+ 1..............2...........3..............4...........
+ 5..........6................7...........8.........
+ #Number of iterations = 23, -2 Log likelihood = + 6744.954042573691
+ # Parameters
+ 12 -12.966061 0.135117
+ 13 -7.401109 0.067831
+ 21 -0.672648 -0.006627
+ 23 -5.051297 0.051271
+

Calculation of the hessian matrix. Wait...
+12345678.12.13.14.15.16.17.18.23.24.25.26.27.28.34.35.36.37.38.45.46.47.48.56.57.58.67.68.78
+
+Inverting the hessian to get the covariance matrix. Wait...
+
+#Hessian matrix#
+3.344e+002 2.708e+004 -4.586e+001 -3.806e+003 -1.577e+000 -1.313e+002 3.914e-001 3.166e+001 
+2.708e+004 2.204e+006 -3.805e+003 -3.174e+005 -1.303e+002 -1.091e+004 2.967e+001 2.399e+003 
+-4.586e+001 -3.805e+003 4.044e+002 3.197e+004 2.431e-002 1.995e+000 1.783e-001 1.486e+001 
+-3.806e+003 -3.174e+005 3.197e+004 2.541e+006 2.436e+000 2.051e+002 1.483e+001 1.244e+003 
+-1.577e+000 -1.303e+002 2.431e-002 2.436e+000 1.093e+002 8.979e+003 -3.402e+001 -2.843e+003 
+-1.313e+002 -1.091e+004 1.995e+000 2.051e+002 8.979e+003 7.420e+005 -2.842e+003 -2.388e+005 
+3.914e-001 2.967e+001 1.783e-001 1.483e+001 -3.402e+001 -2.842e+003 1.494e+002 1.251e+004 
+3.166e+001 2.399e+003 1.486e+001 1.244e+003 -2.843e+003 -2.388e+005 1.251e+004 1.053e+006 
+# Scales
+12 1.00000e-004 1.00000e-006
+13 1.00000e-004 1.00000e-006
+21 1.00000e-003 1.00000e-005
+23 1.00000e-004 1.00000e-005
+# Covariance
+  1 5.90661e-001
+  2 -7.26732e-003 8.98810e-005
+  3 8.80177e-002 -1.12706e-003 5.15824e-001
+  4 -1.13082e-003 1.45267e-005 -6.50070e-003 8.23270e-005
+  5 9.31265e-003 -1.16106e-004 6.00210e-004 -8.04151e-006 1.75753e+000
+  6 -1.15664e-004 1.44850e-006 -7.79995e-006 1.04770e-007 -2.12929e-002 2.59422e-004
+  7 1.35103e-003 -1.75392e-005 -6.38237e-004 7.85424e-006 4.02601e-001 -4.86776e-003 1.32682e+000
+  8 -1.82421e-005 2.35811e-007 7.75503e-006 -9.58687e-008 -4.86589e-003 5.91641e-005 -1.57767e-002 1.88622e-004
+# agemin agemax for lifexpectancy, bage fage (if mle==0 ie no data nor Max likelihood).
+
+
+agemin=70 agemax=100 bage=50 fage=100
+Computing prevalence limit: result on file 'plrmypar.txt' 
+Computing pij: result on file 'pijrmypar.txt' 
+Computing Health Expectancies: result on file 'ermypar.txt' 
+Computing Variance-covariance of DFLEs: file 'vrmypar.txt' 
+Computing Total LEs with variances: file 'trmypar.txt' 
+Computing Variance-covariance of Prevalence limit: file 'vplrmypar.txt' 
+End of Imach
+

+ +

Once the running is finished, the program +requires a caracter:

+ + + + + +

Type g for plotting (available + if mle=1), e to edit output files, c to start again,

and + q for exiting:

+ +

First you should enter g to +make the figures and then you can edit all the results by typing e. +

+ +

Outputs files
+ - index.htm, this file is the master file on which you + should click first.
+ - Observed prevalence in each state: mypar.txt
+ - Estimated parameters and the covariance matrix: rmypar.txt
+ - Stationary prevalence in each state: plrmypar.txt
+ - Transition probabilities: pijrmypar.txt
+ - Copy of the parameter file: ormypar.txt
+ - Life expectancies by age and initial health status: ermypar.txt
+ - Variances of life expectancies by age and initial + health status: vrmypar.txt +
+ - Health expectancies with their variances: trmypar.txt
+ - Standard deviation of stationary prevalence: vplrmypar.txt
+
+
Graphs
+
+ -Observed and stationary + prevalence in state (1) with the confident interval
+ -Observed and stationary + prevalence in state (2) with the confident interval
+ -Health life expectancies + by age and initial health state (1)
+ -Health life expectancies + by age and initial health state (2)
+ -Total life expectancy by + age and health expectancies in states (1) and (2).

+ +

This software have been partly granted by Euro-REVES, a concerted +action from the European Union. It will be copyrighted +identically to a GNU software product, i.e. program and software +can be distributed freely for non commercial use. Sources are not +widely distributed today. You can get them by asking us with a +simple justification (name, email, institute) mailto:brouard@ined.fr and mailto:lievre@ined.fr .

+ +

Latest version (0.63 of 16 march 2000) can be accessed at http://euroreves.ined.fr/imach
+

+ + diff --git a/src/imachrun.log b/src/imachrun.log new file mode 100644 index 0000000..de91cc4 --- /dev/null +++ b/src/imachrun.log @@ -0,0 +1,176 @@ +C:\usr\biais\imach\mytry>c:\usr\biais\imach\bin\imach.exe mypar.txt +# MLE 1984-1990 + +# + +title=MLE datafile=..\data\mydata.txt lastobs=3000 firstpass=1 lastpass=3 +ftol=1.000000e-008 stepm=24 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0 +# Parameters nlstate*nlstate*ncov a12*1 + b12 * age + ... + +11 0.000000 0.000000 +12 0.000000 0.000000 +21 0.000000 0.000000 +22 0.000000 0.000000 +# Scales (for hessian or gradient estimation) + +11 0.000000e+000 0.000000e+000 +12 0.000000e+000 0.000000e+000 +21 0.000000e+000 0.000000e+000 +22 0.000000e+000 0.000000e+000 +#covariance matrix# + +121 0.00000e+000 +122 0.00000e+000 0.00000e+000 +131 0.00000e+000 0.00000e+000 0.00000e+000 +132 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 +211 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 +212 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 +231 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 +232 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 0.00000e+000 + +Total number of individuals= 2965, Agemin = 70.00, Agemax= 100.92 + +Warning, no any valid information for:126 line=126 +Warning, no any valid information for:2307 line=2307 +Delay (in months) between two waves Min=21 Max=51 Mean=24.495826 + + Age 70 1.=230 loss[1]=3.5% 2.=16 loss[2]=12.5% 1.=222 prev[1]=94.1% 2.=14 prev[2]=5.9% 1-1=8 11=200 12=7 13=15 2-1=2 21=6 22=7 23=1 +Age 71 1.=235 loss[1]=4.7% 2.=7 loss[2]=14.3% 1.=224 prev[1]=97.4% 2.=6 prev[2]=2.6% 1-1=11 11=196 12=6 13=22 2-1=1 21=1 22=4 23=1 33=6 +Age 72 1.=417 loss[1]=5.8% 2.=24 loss[2]=4.2% 1.=393 prev[1]=94.5% 2.=23 prev[2]=5.5% 1-1=24 11=347 12=18 13=28 2-1=1 21=6 22=14 23=3 33=20 +Age 73 1.=395 loss[1]=3.3% 2.=21 loss[2]=4.8% 1.=382 prev[1]=95.0% 2.=20 prev[2]=5.0% 1-1=13 11=327 12=15 13=40 2-1=1 21=4 22=12 23=4 33=17 +Age 74 1.=364 loss[1]=6.3% 2.=37 loss[2]=5.4% 1.=341 prev[1]=90.7% 2.=35 prev[2]=9.3% 1-1=23 11=307 12=11 13=23 2-1=2 21=6 22=23 23=6 3-1=2 33=20 +Age 75 1.=357 loss[1]=6.4% 2.=23 loss[2]=4.3% 1.=334 prev[1]=93.8% 2.=22 prev[2]=6.2% 1-1=23 11=286 12=18 13=30 2-1=1 21=4 22=13 23=5 3-1=1 33=21 +Age 76 1.=315 loss[1]=4.4% 2.=29 loss[2]=3.4% 1.=301 prev[1]=91.5% 2.=28 prev[2]=8.5% 1-1=14 11=260 12=20 13=21 2-1=1 21=5 22=18 23=5 3-1=2 33=15 +Age 77 1.=285 loss[1]=3.5% 2.=46 loss[2]=0.0% 1.=275 prev[1]=85.7% 2.=46 prev[2]=14.3% 1-1=10 11=224 12=19 13=32 21=6 22=30 23=10 3-1=1 33=10 +Age 78 1.=277 loss[1]=3.2% 2.=41 loss[2]=2.4% 1.=268 prev[1]=87.0% 2.=40 prev[2]=13.0% 1-1=9 11=216 12=28 13=24 2-1=1 21=4 22=28 23=8 3-1=1 33=22 +Age 79 1.=235 loss[1]=4.7% 2.=47 loss[2]=8.5% 1.=224 prev[1]=83.9% 2.=43 prev[2]=16.1% 1-1=11 11=184 12=20 13=20 2-1=4 21=5 22=28 23=10 33=12 +Age 80 1.=226 loss[1]=5.8% 2.=40 loss[2]=5.0% 1.=213 prev[1]=84.9% 2.=38 prev[2]=15.1% 1-1=13 11=182 12=16 13=15 2-1=2 21=7 22=25 23=6 33=12 +Age 81 1.=204 loss[1]=7.4% 2.=40 loss[2]=7.5% 1.=189 prev[1]=83.6% 2.=37 prev[2]=16.4% 1-1=15 11=144 12=25 13=20 2-1=3 21=4 22=16 23=17 3-1=2 33=11 +Age 82 1.=183 loss[1]=2.7% 2.=40 loss[2]=2.5% 1.=178 prev[1]=82.0% 2.=39 prev[2]=18.0% 1-1=5 11=139 12=21 13=18 2-1=1 21=7 22=19 23=13 3-1=1 33=15 +Age 83 1.=149 loss[1]=3.4% 2.=35 loss[2]=2.9% 1.=144 prev[1]=80.9% 2.=34 prev[2]=19.1% 1-1=5 11=109 12=15 13=20 2-1=1 21=6 22=20 23=8 3-1=1 33=12 +Age 84 1.=133 loss[1]=3.0% 2.=53 loss[2]=9.4% 1.=129 prev[1]=72.9% 2.=48 prev[2]=27.1% 1-1=4 11=97 12=18 13=14 2-1=5 21=10 22=23 23=15 3-1=2 33=14 +Age 85 1.=109 loss[1]=2.8% 2.=34 loss[2]=0.0% 1.=106 prev[1]=75.7% 2.=34 prev[2]=24.3% 1-1=3 11=69 12=17 13=20 21=7 22=19 23=8 3-1=2 33=15 +Age 86 1.=95 loss[1]=1.1% 2.=43 loss[2]=2.3% 1.=94 prev[1]=69.1% 2.=42 prev[2]=30.9% 1-1=1 11=59 12=17 13=18 2-1=1 21=5 22=22 23=15 3-1=2 33=9 +Age 87 1.=78 loss[1]=2.6% 2.=44 loss[2]=6.8% 1.=76 prev[1]=65.0% 2.=41 prev[2]=35.0% 1-1=2 11=52 12=14 13=10 2-1=3 21=9 22=20 23=12 33=15 +Age 88 1.=67 loss[1]=0.0% 2.=32 loss[2]=3.1% 1.=67 prev[1]=68.4% 2.=31 prev[2]=31.6% 11=37 12=19 13=11 2-1=1 21=5 22=20 23=6 33=9 +Age 89 1.=55 loss[1]=5.5% 2.=31 loss[2]=3.2% 1.=52 prev[1]=63.4% 2.=30 prev[2]=36.6% 1-1=3 11=26 12=14 13=12 2-1=1 21=8 22=17 23=5 3-1=1 33=9 +Age 90 1.=44 loss[1]=11.4% 2.=27 loss[2]=3.7% 1.=39 prev[1]=60.0% 2.=26 prev[2]=40.0% 1-1=5 11=22 12=9 13=8 2-1=1 21=2 22=14 23=10 3-1=1 33=9 +Age 91 1.=31 loss[1]=3.2% 2.=23 loss[2]=13.0% 1.=30 prev[1]=60.0% 2.=20 prev[2]=40.0% 1-1=1 11=17 12=7 13=6 2-1=3 21=1 22=11 23=8 33=3 +Age 92 1.=23 loss[1]=0.0% 2.=21 loss[2]=4.8% 1.=23 prev[1]=53.5% 2.=20 prev[2]=46.5% 11=9 12=8 13=6 2-1=1 21=1 22=13 23=6 33=8 +Age 93 1.=8 loss[1]=12.5% 2.=19 loss[2]=5.3% 1.=7 prev[1]=28.0% 2.=18 prev[2]=72.0% 1-1=1 11=4 12=3 2-1=1 21=1 22=11 23=6 3-1=1 33=9 +Age 94 1.=11 loss[1]=9.1% 2.=14 loss[2]=0.0% 1.=10 prev[1]=41.7% 2.=14 prev[2]=58.3% 1-1=1 11=3 12=6 13=1 21=2 22=7 23=5 33=5 +Age 95 1.=4 loss[1]=0.0% 2.=6 loss[2]=16.7% 1.=4 prev[1]=44.4% 2.=5 prev[2]=55.6% 11=2 12=1 13=1 2-1=1 21=2 23=3 33=2 +Age 96 1.=4 loss[1]=25.0% 2.=6 loss[2]=0.0% 1.=3 prev[1]=33.3% 2.=6 prev[2]=66.7% 1-1=1 11=1 12=1 13=1 21=2 22=2 23=2 33=2 +Age 97 1.=0 loss[1]=NaNQ% 2.=1 loss[2]=0.0% 1.=0 prev[1]=0.0% 2.=1 prev[2]=100.0% 23=1 +Age 98 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 prev[2]=NaNQ% +Age 99 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 prev[2]=NaNQ% +Age 100 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 prev[2]=NaNQ% +Age 101 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 prev[2]=NaNQ% +Age 102 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 prev[2]=NaNQ% -1-1=18 -11=188 -12=30 -13=38 +Total 1.=4534 loss[1]=4.5% 2.=800 loss[2]=4.9% 1.=4328 prev[1]=85.0% 2.=761 prev[2]=15.0% -1-1=18 -11=188 -12=30 -13=38 1-1=206 11=3519 12=373 13=436 2-1=39 21=126 22=436 23=199 3-1=20 33=302 +Powell + +Powell iter=1 -2*LL=11531.405658264877 1 0.000000000000 2 0.000000000000 3 0.000000000000 4 0.000000000000 5 0.000000000000 6 0.000000000000 7 0.000000000000 8 0.000000000000 +1..........2.................3..........4.................5.........6................7........8............... +Powell iter=2 -2*LL=7189.342479571923 1 -1.618034000000 2 0.000243057112 3 -1.618034000000 4 -0.004756278029 5 -0.914196809953 6 0.000204141812 7 -0.703784678359 8 0.000288223851 +1........2.................3.........4..................5.........6................7.........8........................ +Powell iter=3 -2*LL=6999.869835328977 1 -2.331359020254 2 0.000958881180 3 -1.729014288043 4 -0.004451102909 5 -1.234885928233 6 0.000031918936 7 -0.813603344320 8 0.000604615223 +1...............2.................3.........4..................5.........6................7.........8.............. +Powell iter=4 -2*LL=6996.402685207441 1 -2.478603679819 2 0.002320473825 3 -1.774318202733 4 -0.003932303071 5 -1.267360655852 6 -0.000258419142 7 -0.872906870738 8 0.000688621829 +1...............2.......3.........4..................5.........6................7.........8.............. +Powell iter=5 -2*LL=6991.472623520970 1 -2.754862801334 2 0.004818289656 3 -1.909104599415 4 -0.002278897004 5 -1.199219023235 6 -0.001178497892 7 -1.022698614160 8 0.002118851608 +1...............2......3.........4..................5.........6................7.........8............. +Powell iter=6 -2*LL=6962.896830060558 1 -4.241651416576 2 0.021100764612 3 -2.782715905961 4 0.008746142517 5 -0.661703877114 6 -0.007293414952 7 -2.002149669075 8 0.013161578084 +1...............2........3.........4.................5.........6................7.........8.............. +Powell iter=7 -2*LL=6856.515462890279 1 -9.104604463847 2 0.080963849064 3 -5.984275969066 4 0.049613285500 5 1.350545085198 6 -0.029870618347 7 -5.621492754966 8 0.056727766830 +1...............2......3.........4.................5.........6................7.........8....... +Powell iter=8 -2*LL=6807.299976649243 1 -9.679671626302 2 0.090172151940 3 -6.501900056622 4 0.056165638832 5 1.617555411708 6 -0.033345210651 7 -6.285041940821 8 0.065876908728 +1...............2.......3.........4.................5.........6................7.........8.............. +Powell iter=9 -2*LL=6765.223979608776 1 -10.944967760181 2 0.110078352295 3 -7.599918978249 4 0.070265582553 5 2.119367547003 6 -0.040632636562 7 -7.697984528675 8 0.082717269362 +1...............2........3...........4.................5.........6................7..........8........... +Powell iter=10 -2*LL=6764.942925283532 1 -10.995045251042 2 0.110672992239 3 -7.631384326082 4 0.070639289631 5 2.107918420012 6 -0.040631357619 7 -7.717680431349 8 0.082842084637 +1...............2........3.........4.................5.........6................7..........8.................. +Powell iter=11 -2*LL=6758.126928754780 1 -12.649915944424 2 0.130679851428 3 -8.532294367391 4 0.081933260496 5 1.269832277292 6 -0.031153280246 7 -7.541104999513 8 0.080670456845 +1........2........3.........4.................5.........6................7.........8....... +Powell iter=12 -2*LL=6751.739055814885 1 -12.595143083360 2 0.130107012348 3 -8.389513656808 4 0.080162752729 5 0.471603387369 6 -0.021009924396 7 -6.696076157144 8 0.070744440420 +1........2........3.........4.................5.........6................7.........8............. +Powell iter=13 -2*LL=6746.696865848237 1 -12.517990378064 2 0.129617715142 3 -8.146403583528 4 0.077181406366 5 -1.054592628099 6 -0.001907385381 7 -5.139371065631 8 0.052382230359 +1........2...........3.........4.................5.........6................7.........8........ +Powell iter=14 -2*LL=6746.679040245447 1 -12.521586079436 2 0.129677462699 3 -8.144304958850 4 0.077159986974 5 -1.065387233819 6 -0.001856671774 7 -5.138397462232 8 0.052326966712 +1.................2........3.........4.................5.........6................7........8.............. +Powell iter=15 -2*LL=6746.584135522181 1 -12.394946753976 2 0.128077576911 3 -8.009745762465 4 0.075460831413 5 -1.045986257644 6 -0.002324513261 7 -5.086087706239 8 0.051646596279 +1........2........3.........4.........5..........6................7.........8............ +Powell iter=16 -2*LL=6746.409688111119 1 -12.337298733327 2 0.127349601718 3 -7.867238995448 4 0.073740146981 5 -1.028502684303 6 -0.002611889339 7 -5.005904152361 8 0.050670578982 +1........2........3.........4........5.......6................7.........8.......... +Powell iter=17 -2*LL=6745.813273626674 1 -12.426249555087 2 0.128447083617 3 -7.474039244737 4 0.068916832631 5 -0.999114433414 6 -0.003020647652 7 -4.804607433487 8 0.048253597224 +1........2........3.........4........5.....6................7.........8...... +Powell iter=18 -2*LL=6745.117193568432 1 -12.807569088249 2 0.133202806969 3 -7.467620316340 4 0.068773114112 5 -0.898426027976 6 -0.004024137111 7 -4.893865605332 8 0.049367546860 +1........2........3.........4........5......6................7.........8....... +Powell iter=19 -2*LL=6744.983460451949 1 -12.894722466826 2 0.134251952454 3 -7.428738535044 4 0.068222705074 5 -0.767927408730 6 -0.005529901364 7 -4.985241944126 8 0.050472191885 +1........2........3.........4........5.......6................7.........8........ +Powell iter=20 -2*LL=6744.959681724528 1 -12.935337614690 2 0.134745057934 3 -7.413886221380 4 0.068011967304 5 -0.715509808093 6 -0.006132925395 7 -5.022473778604 8 0.050922685102 +1............2.........3........4........5.......6................7..........8........ +Powell iter=21 -2*LL=6744.955290044597 1 -12.952671908339 2 0.134955278220 3 -7.407513503114 4 0.067921460677 5 -0.693105554462 6 -0.006390806673 7 -5.038208232247 8 0.051113073984 +1...............2..............3.............4...........5.......6................7............8........ +Powell iter=22 -2*LL=6744.954431665786 1 -12.960110073059 2 0.135045475315 3 -7.404811455032 4 0.067883051952 5 -0.683487247450 6 -0.006501558124 7 -5.044834063352 8 0.051193262301 +1...............2............3...........4...............5.......6.........................7.............8......................... +Powell iter=23 -2*LL=6744.954108371555 1 -12.967632334283 2 0.135136681033 3 -7.402109728262 4 0.067844593326 5 -0.673601538129 6 -0.006615504377 7 -5.051341616718 8 0.051272038506 +1..............2...........3..............4...........5..........6................7...........8......... +#Number of iterations = 23, -2 Log likelihood = 6744.954042573691 +# Parameters +12 -12.966061 0.135117 +13 -7.401109 0.067831 +21 -0.672648 -0.006627 +23 -5.051297 0.051271 + +Calculation of the hessian matrix. Wait... +12345678.12.13.14.15.16.17.18.23.24.25.26.27.28.34.35.36.37.38.45.46.47.48.56.57.58.67.68.78 + +Inverting the hessian to get the covariance matrix. Wait... + +#Hessian matrix# +3.344e+002 2.708e+004 -4.586e+001 -3.806e+003 -1.577e+000 -1.313e+002 3.914e-001 3.166e+001 +2.708e+004 2.204e+006 -3.805e+003 -3.174e+005 -1.303e+002 -1.091e+004 2.967e+001 2.399e+003 +-4.586e+001 -3.805e+003 4.044e+002 3.197e+004 2.431e-002 1.995e+000 1.783e-001 1.486e+001 +-3.806e+003 -3.174e+005 3.197e+004 2.541e+006 2.436e+000 2.051e+002 1.483e+001 1.244e+003 +-1.577e+000 -1.303e+002 2.431e-002 2.436e+000 1.093e+002 8.979e+003 -3.402e+001 -2.843e+003 +-1.313e+002 -1.091e+004 1.995e+000 2.051e+002 8.979e+003 7.420e+005 -2.842e+003 -2.388e+005 +3.914e-001 2.967e+001 1.783e-001 1.483e+001 -3.402e+001 -2.842e+003 1.494e+002 1.251e+004 +3.166e+001 2.399e+003 1.486e+001 1.244e+003 -2.843e+003 -2.388e+005 1.251e+004 1.053e+006 +# Scales +12 1.00000e-004 1.00000e-006 +13 1.00000e-004 1.00000e-006 +21 1.00000e-003 1.00000e-005 +23 1.00000e-004 1.00000e-005 +# Covariance + 1 5.90661e-001 + 2 -7.26732e-003 8.98810e-005 + 3 8.80177e-002 -1.12706e-003 5.15824e-001 + 4 -1.13082e-003 1.45267e-005 -6.50070e-003 8.23270e-005 + 5 9.31265e-003 -1.16106e-004 6.00210e-004 -8.04151e-006 1.75753e+000 + 6 -1.15664e-004 1.44850e-006 -7.79995e-006 1.04770e-007 -2.12929e-002 2.59422e-004 + 7 1.35103e-003 -1.75392e-005 -6.38237e-004 7.85424e-006 4.02601e-001 -4.86776e-003 1.32682e+000 + 8 -1.82421e-005 2.35811e-007 7.75503e-006 -9.58687e-008 -4.86589e-003 5.91641e-005 -1.57767e-002 1.88622e-004 +# agemin agemax for lifexpectancy, bage fage (if mle==0 ie no data nor Max likelihood). + +# Fin de la lecture + +agemin=70 agemax=100 bage=50 fage=100 +Computing prevalence limit: result on file 'plrmypar.txt' +Computing pij: result on file 'pijrmypar.txt' +Computing Health Expectancies: result on file 'ermypar.txt' +Computing Variance-covariance of DFLEs: file 'vrmypar.txt' +Computing Total LEs with variances: file 'trmypar.txt' +Computing Variance-covariance of Prevalence limit: file 'vplrmypar.txt' +End of Imach + +C:\usr\biais\imach\mytry>pause +Appuyez sur une touche pour continuer . . . + + +C:\usr\biais\imach\mytry>ECHO Press a key to get graphs +Press a key to get graphs + +C:\usr\biais\imach\mytry>c:\usr\biais\imach\bin\wgnuplot.exe.lnk graph.gp + +C:\usr\biais\imach\mytry>pause \ No newline at end of file diff --git a/src/index.htm b/src/index.htm new file mode 100644 index 0000000..a6bca7d --- /dev/null +++ b/src/index.htm @@ -0,0 +1,19 @@ +

Outputs files

+ + - Observed prevalence in each state: prbiaspar.txt
+ +- Estimated parameters and the covariance matrix: rbiaspar.txt
+ - Stationary prevalence in each state: plrbiaspar.txt
+ - Transition probabilities: pijrbiaspar.txt
+ - Copy of the parameter file: orbiaspar.txt
+ - Life expectancies by age and initial health status: erbiaspar.txt
+ - Variances of life expectancies by age and initial health status: vrbiaspar.txt
+ - Health expectancies with their variances: trbiaspar.txt
+ - Standard deviation of stationary prevalences: vplrbiaspar.txt
Graphs

- Prevalence of disability: pbiaspar1.gif
+
- Observed and stationary prevalence (with confident +interval) in state (1): vbiaspar1.gif
+
- Observed and stationary prevalence (with confident +interval) in state (2): vbiaspar2.gif
+
- Total life expectancy by age and + health expectancies in states (1) and (2): ebiaspar.gif
+

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Computing Health +Expectancies using IMaCh

(a Maximum +Likelihood Computer Program using Interpolation of Markov Chains)

INED and EUROREVES

Authors of the program: Nicolas +Brouard, senior researcher at +the Institut +National d'Etudes Démographiques +(INED, Paris) in the "Mortality, Health and Epidemiology +Research Unit"

and Agnès Lièvre

Contribution to the mathematics: C. R. +Heathcote (Australian +National University, Canberra).

Contact: Agnès Lièvre (lievre@ined.fr)

Installation

Computing Health +Expectancies using IMaCh

(a Maximum +Likelihood Computer Program using Interpolation of Markov Chains)

INED and EUROREVES

Contribution to the mathematics: C. R. +Heathcote (Australian +National University, Canberra).

Contact: Agnès Lièvre (lievre@ined.fr)

Introduction

On what kind of data can +it be used?

The data file

Your first example parameter file

#Imach version 0.63, February 2000, +INED-EUROREVES

First uncommented line

Second uncommented +line

Guess values for optimization

Guess values for computing variances

Covariance matrix of parameters

last +uncommented line

Running Imach +with this example

Output of the program +and graphs

Results files + +- Observed prevalence in each state (and at first pass): +prbiaspar.txt +

- Estimated parameters and +covariance matrix: rbiaspar.txt

- Transition probabilities: +pijrbiaspar.txt

- Stationary prevalence in each state: +plrbiaspar.txt

- Standard deviation of +stationary prevalence: vplrbiaspar.txt

Observed and stationary +prevalence in state (2=disable) with the confident interval: +vbiaspar2.gif

Convergence to the +stationary prevalence of disability: pbiaspar1.gif +

- Life expectancies by age +and initial health status: erbiaspar.txt

- Variances of life +expectancies by age and initial health status: vrbiaspar.txt

- Health +expectancies +with standard errors in parentheses: trbiaspar.txt

Total life expectancy by +age and health expectancies in states (1=healthy) and (2=disable): +ebiaspar.gif

- Copy of the parameter +file: orbiaspar.txt

Trying an example

Results files
+
+- Observed prevalence in each state (and at first pass): +prbiaspar.txt
+

Convergence to the +stationary prevalence of disability: pbiaspar1.gif
+