Hi, As the bugs #275892 and #302873 are preventing r-noncran-lindsey to be build, and as they are open for a very long time, I am preparing an NMU of this package.
You will find attached the patch of this NMU. I'll upload it in 3 days unless you oppose. Bye, Aurelien -- .''`. Aurelien Jarno | GPG: 1024D/F1BCDB73 : :' : Debian GNU/Linux developer | Electrical Engineer `. `' [EMAIL PROTECTED] | [EMAIL PROTECTED] `- people.debian.org/~aurel32 | www.aurel32.net
diff -u r-noncran-lindsey-1.0.20041008/debian/changelog
r-noncran-lindsey-1.0.20041008/debian/changelog
--- r-noncran-lindsey-1.0.20041008/debian/changelog
+++ r-noncran-lindsey-1.0.20041008/debian/changelog
@@ -1,3 +1,12 @@
+r-noncran-lindsey (1.0.20041008-1.1) unstable; urgency=low
+
+ * NMU.
+ * Don't build-depends on libreadline4-dev (closes: bug#302873).
+ * Applied patch from Kåre Hviid to make build possible with gcc-3.4 and
+ gcc-4.0 (closes: bug#275892).
+
+ -- Aurelien Jarno <[EMAIL PROTECTED]> Sat, 20 Aug 2005 11:34:06 +0200
+
r-noncran-lindsey (1.0.20041008-1) unstable; urgency=low
* New upstream releases.
diff -u r-noncran-lindsey-1.0.20041008/debian/control
r-noncran-lindsey-1.0.20041008/debian/control
--- r-noncran-lindsey-1.0.20041008/debian/control
+++ r-noncran-lindsey-1.0.20041008/debian/control
@@ -3,7 +3,7 @@
Priority: optional
Maintainer: Chris Lawrence <[EMAIL PROTECTED]>
Standards-Version: 3.6.1
-Build-depends: r-base-dev (>> 2.0.0), debhelper (>> 4), libncurses5-dev,
libreadline4-dev, g77 [!m68k], f2c [m68k]
+Build-Depends: r-base-dev (>> 2.0.0), debhelper (>> 4), libncurses5-dev, g77
[!m68k], f2c [m68k]
Package: r-noncran-lindsey
Architecture: any
diff -u r-noncran-lindsey-1.0.20041008/debian/rules
r-noncran-lindsey-1.0.20041008/debian/rules
--- r-noncran-lindsey-1.0.20041008/debian/rules
+++ r-noncran-lindsey-1.0.20041008/debian/rules
@@ -46,7 +46,9 @@
dh_installdirs
(mkdir build; cd build; for lib in ../rmutil.tgz ../event.tgz
../growth.tgz ../repeated.tgz ../stable.tgz ../gnlm.tgz ../ordinal03.tgz; \
do tar -zxf $$lib; done; \
- cp ../fixed/*.c ./ordinal/src; cp ../fixed/Makefile ./ordinal/src; cp
../fixed/DESCRIPTION ./ordinal; \
+ cp ../fixed/dist.c ../fixed/lcr.c ./ordinal/src; cp ../fixed/Makefile
./ordinal/src; cp ../fixed/DESCRIPTION ./ordinal; \
+ cp ../fixed/ksurvb.c ../fixed/ksurvg.c ./event/src; \
+ cp ../fixed/kcountb.c ./repeated/src; \
R CMD INSTALL -l $(debRlib) --clean rmutil event growth repeated stable
gnlm ordinal)
rm -vf $(debRlib)/R.css
(find $(debRlib) -name 'COPYING' -print0 | xargs -0 rm -vf)
only in patch2:
unchanged:
--- r-noncran-lindsey-1.0.20041008.orig/fixed/ksurvg.c
+++ r-noncran-lindsey-1.0.20041008/fixed/ksurvg.c
@@ -0,0 +1,223 @@
+/*
+ * event : A Library of Special Functions for Event Histories
+ * Copyright (C) 1998, 1999, 2000, 2001 J.K. Lindsey
+ *
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 2 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software
+ * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
+ *
+ * SYNOPSIS
+ *
+ * void ksurvg(double p[],double y[],double x[],int cens[],int *nind,
+ * int nobs[],int *nbs,int *nccov,int *model,int *dist,int *density,
+ * int *dep,int *birth,int *tvc,double tvcov[],int *fit,double pred[],
+ * double rpred[],int *renewal,int *rf,double bb[],
+ * int *sf,double vv[],double *like)
+ *
+ * DESCRIPTION
+ *
+ * Function to compute the likelihood function for various distributions
+ * inserted in a gamma or Weibull distribution with serial dependence using
+ * Kalman-type update for event histories.
+ *
+ */
+
+#include <math.h>
+#include <stddef.h>
+#include "R.h"
+#include "Rmath.h"
+
+void ksurvg(double p[],double y[],double x[],int cens[],int *nind,int nobs[],
+ int *nbs,int *nccov,int *model,int *dist,int *density,int *dep,
+ int *birth,int *tvc,double tvcov[],int *fit,double pred[],
+ double rpred[],int *renewal,int *rf,double bb[],
+ int *sf,double vv[],double *like){
+ int i,j,k,nm,c;
+ double a,a1,b,H,delta,lambda,omega,om,beta,bt,l1,yy,kk,ly,plap,tmp,
+ intercept;
+
+ *like=0;
+ nm=0;
+ delta=exp(-p[*nccov+*birth+*tvc+1]);
+
if(*dep>0)omega=exp(p[*nccov+*birth+*tvc+2])/(1+exp(p[*nccov+*birth+*tvc+2]));
+ if(*model>1&&!*sf){
+ if(*model<5)lambda=exp(p[*nccov+*birth+*tvc+2+(*dep>0)]);
+ else lambda=exp(p[*nccov+*birth+*tvc+2+(*dep>0)]/2);}
+ if(*model==4)intercept=exp(p[*nccov+*birth+*tvc+3+(*dep>0)]);
+ for(i=0;i<*nind;i++){
+ a=b=delta;
+ if(!*rf){
+ beta=p[0];
+ for(k=0;k<*nccov;k++)beta+=p[k+1]*x[i+k**nind];
+ if(*model<4){
+ if(beta>40) beta=40;
+ if(beta<-40)beta=-40;
+ beta=exp(beta);}}
+ else if(!*tvc)bt=bb[i];
+ c=0;
+ yy=0;
+ for(j=0;j<nobs[i];j++){
+ if(*model>1&&*sf)lambda=vv[nm];
+ /* store value and check if ties to follow */
+ if(y[nm]>0){
+ if(*renewal)yy=y[nm];
+ else yy+=y[nm];}
+ c+=cens[nm];
+ if(j>=nobs[i]-1||y[nm+1]!=0){
+ if(*model>=5)ly=log(yy);
+ a1=a+c;
+ /* add in birth and time-varying covariates */
+ if(!*rf){
+ if(*tvc){
+ bt=0;
+ for(k=0;k<*tvc;k++)bt+=p[*nccov+*birth+k+1]*tvcov[nm+*nbs*k];
+ if(*model<4){
+ if(bt>40) bt=40;
+ if(bt<-40)bt=-40;
+ bt=exp(bt)*beta;}
+ else bt+=beta;}
+ else bt=beta;
+ if(*birth){
+ if(*model<4)bt*=pow(j+1.,p[*nccov+1]);
+ else bt+=p[*nccov+1]*log(j+1);}}
+ else if(*tvc)bt=bb[nm];
+ if(!*density){
+ /* intensity models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=yy/bt;
+ l1=1/bt;
+ break;
+ case 2: /* Weibull distribution */
+ H=pow(yy/bt,lambda);
+ l1=lambda*pow(yy/bt,lambda-1)/bt;
+ break;
+ case 3: /* gamma distribution */
+ H=-pgamma(yy,lambda,bt,0,1);
+ l1=dgamma(yy,lambda,bt,0)/(1-pgamma(yy,lambda,bt,1,0));
+ break;
+ case 4: /* generalized logistic distribution */
+ H=(yy+log(lambda+intercept*exp(-bt*yy))/bt)/lambda;
+ l1=1/(lambda+intercept*exp(-bt*yy));
+ break;
+ case 5: /* log normal distribution */
+ H=-pnorm(ly,bt,lambda,0,1);
+ l1=dnorm(ly,bt,lambda,0)/yy/(1-pnorm(ly,bt,lambda,1,0));
+ break;
+ case 6: /* log logistic distribution */
+ H=-plogis(ly,bt,lambda,0,1);
+ l1=dlogis(ly,bt,lambda,0)/yy/(1-plogis(ly,bt,lambda,1,0));
+ break;
+ case 7: /* log Cauchy distribution */
+ H=-pcauchy(ly,bt,lambda,0,1);
+ l1=dcauchy(ly,bt,lambda,0)/yy/(1-pcauchy(ly,bt,lambda,1,0));
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ H=-log(1-plap);
+ l1=tmp/(lambda*yy*(1-plap));
+ break;}}
+ else{
+ /* density models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=pexp(yy,bt,1,0);
+ l1=dexp(yy,bt,0);
+ break;
+ case 2: /* Weibull distribution */
+ H=pweibull(yy,lambda,bt,1,0);
+ l1=dweibull(yy,lambda,bt,0);
+ break;
+ case 3: /* gamma distribution */
+ H=pgamma(yy,lambda,bt,1,0);
+ l1=dgamma(yy,lambda,bt,0);
+ break;
+ case 4: /* generalized logistic distribution */
+
H=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt));
+
l1=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt)+1);
+ break;
+ case 5: /* log normal distribution */
+ H=pnorm(ly,bt,lambda,1,0);
+ l1=dnorm(ly,bt,lambda,0)/yy;
+ break;
+ case 6: /* log logistic distribution */
+ H=plogis(ly,bt,lambda,1,0);
+ l1=dlogis(ly,bt,lambda,0)/yy;
+ break;
+ case 7: /* log Cauchy distribution */
+ H=pcauchy(ly,bt,lambda,1,0);
+ l1=dcauchy(ly,bt,lambda,0)/yy;
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ H=ly<bt?tmp:1-tmp;
+ l1=tmp/lambda/yy;
+ break;}}
+ /* calculate likelihood */
+ if(l1<1e-20)l1=1e-20;
+ if(H<1e-20)H=1e-20;
+ if(H>1e20)H=1e20;
+ /* is this correct when c>1 ? */
+ if(*dist==2){
+ if(c)*like-=c*log(l1)-b*H-lgammafn(a)+log(b)+(a-1)*log(b*H);
+ /* if(c)*like-=c*log(l1)+log(dgamma(H,a,1/b));*/
+ else *like-=pgamma(H,a,1/b,0,1);}
+ else if(*dist==3){
+ if(c)*like-=c*log(l1)-pow(b*H,a)+(a-1)*log(b*H)+log(b)+log(a);
+ else *like+=pow(b*H,a);}
+ if(c>1)*like+=lgammafn(c+1);
+ if(*fit){
+ pred[nm-c+cens[nm]]=bt;
+ tmp=a/b;
+ if(!*density){
+ switch(*model){
+ case 1: rpred[nm-c+cens[nm]]=bt*tmp; break;
+ case 2: rpred[nm-c+cens[nm]]=bt*pow(tmp,1/lambda); break;
+ case 3: rpred[nm-c+cens[nm]]=qgamma(1-exp(-tmp),lambda,bt,1,0);
break;
+ case 5: rpred[nm-c+cens[nm]]=exp(qnorm(1-exp(-tmp),bt,lambda,1,0));
break;
+ case 6:
rpred[nm-c+cens[nm]]=exp(qlogis(1-exp(-tmp),bt,lambda,1,0)); break;
+ case 7:
rpred[nm-c+cens[nm]]=exp(qcauchy(1-exp(-tmp),bt,lambda,1,0)); break;
+ case 8:
rpred[nm-c+cens[nm]]=exp(bt+lambda*log(2*(ly<bt?exp(-tmp):1-exp(-tmp))));
break;}}
+ else{
+ switch(*model){
+ case 1: rpred[nm-c+cens[nm]]=qexp(tmp,bt,1,0); break;
+ case 2: rpred[nm-c+cens[nm]]=qweibull(tmp,lambda,bt,1,0); break;
+ case 3: rpred[nm-c+cens[nm]]=qgamma(tmp,lambda,bt,1,0); break;
+ case 5: rpred[nm-c+cens[nm]]=exp(qnorm(tmp,bt,lambda,1,0)); break;
+ case 6: rpred[nm-c+cens[nm]]=exp(qlogis(tmp,bt,lambda,1,0)); break;
+ case 7: rpred[nm-c+cens[nm]]=exp(qcauchy(tmp,bt,lambda,1,0)); break;
+ case 8:
rpred[nm-c+cens[nm]]=exp(bt+lambda*log(2*(ly<bt?tmp:1-tmp))); break;}}}
+ /* update parameters */
+ switch(*dep){
+ case 1:
+ case 2: om=pow(omega,yy); a=om*a1+(1-om)*delta; break;
+ case 3:
+ case 4: a=omega*a1+(1-omega)*(delta-1); break;
+ case 5: a=a1; break;
+ case 6:
+ case 7: a=omega*a1; break;
+ default: ;
+ }
+ switch(*dep){
+ case 1: b=pow(b/(H*delta),om)*delta; break;
+ case 2: b=pow(H,-om)*delta; break;
+ case 3: b=pow(b/(H*delta),omega)*delta; break;
+ case 4: b=pow(H,-omega)*delta; break;
+ case 5:
+ case 7: b=pow(b/H,omega); break;
+ default: ;
+ }
+ c=0;}
+ nm++;}}
+ return;}
only in patch2:
unchanged:
--- r-noncran-lindsey-1.0.20041008.orig/fixed/ksurvb.c
+++ r-noncran-lindsey-1.0.20041008/fixed/ksurvb.c
@@ -0,0 +1,396 @@
+/*
+ * event : A Library of Special Functions for Event Histories
+ * Copyright (C) 1998, 1999, 2000, 2001 J.K. Lindsey
+ *
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 2 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software
+ * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
+ *
+ * SYNOPSIS
+ *
+ * void ksurvb(double p[],double y[],double x[],int cens[],int *nind,
+ * int nobs[],int *nbs,int *nccov,int *model,int *density,
+ * int *pfamily,int *dep,int *birth,int *tvc,double tvcov[],
+ * int *fit,double pred[],double rpred[],int *renewal,int *rf,
+ * double bb[],int *sf,double vv[],double *like)
+ * void frailb(double p[],double y[],double x[],int cens[],int *nind,
+ * int nobs[],int *nbs,int *nccov,int *model,int *density,int *dep,
+ * int *birth,int *tvc,double tvcov[],int *fit,double pred[],
+ * double rpred[],int *rf,double bb[],int *sf,double vv[],
+ * int *frser,double *like)
+ *
+ * DESCRIPTION
+ *
+ * Function to compute the likelihood function for various distributions
+ * inserted in a beta distribution with serial dependence or gamma frailties
+ * using Kalman-type update for event histories.
+ *
+ */
+
+#include <math.h>
+#include <stddef.h>
+#include "R.h"
+#include "Rmath.h"
+
+void ksurvb(double p[],double y[],double x[],int cens[],int *nind,
+ int nobs[],int *nbs,int *nccov,int *model,int *density,
+ int *pfamily,int *dep,int *birth,int *tvc,double tvcov[],int *fit,
+ double pred[],double rpred[],int *renewal,int *rf,double bb[],
+ int *sf,double vv[],double *like){
+ int i,j,k,nm,c;
+ double a,a1,b,b1,delta,lambda,omega,om,beta,bt,h,yy,kk,tmp,ly,plap,intercept,
+ family;
+
+ *like=0;
+ nm=0;
+ delta=exp(-p[*nccov+*birth+*tvc+1]);
+ if(*dep>0)
+ omega=exp(p[*nccov+*birth+*tvc+2])/(1+exp(p[*nccov+*birth+*tvc+2]));
+ if(*pfamily)family=p[*nccov+*birth+*tvc+2+(*dep>0)];
+ if(*model==4)intercept=exp(p[*nccov+*birth+*tvc+2+(*dep>0)+*pfamily]);;
+ if(*model>1&&!*sf){
+ if(*model<5)
+ lambda=exp(p[*nccov+*birth+*tvc+2+(*dep>0)+*pfamily+(*model==4)]);
+ else lambda=exp(p[*nccov+*birth+*tvc+2+(*dep>0)+*pfamily+(*model==4)]/2);}
+ for(i=0;i<*nind;i++){
+ a=b=delta;
+ if(!*rf){
+ beta=p[0];
+ for(k=0;k<*nccov;k++)beta+=p[k+1]*x[i+k**nind];
+ if(*model<4){
+ if(beta>40) beta=40;
+ if(beta<-40)beta=-40;
+ beta=exp(beta);}}
+ else if(!*tvc)bt=bb[i];
+ c=0;
+ yy=0;
+ for(j=0;j<nobs[i];j++){
+ if(*model>1&&*sf)lambda=vv[nm];
+ /* store value and check if ties to follow */
+ if(y[nm]>0){
+ if(*renewal)yy=y[nm];
+ else yy+=y[nm];}
+ c+=cens[nm];
+ if(j>=nobs[i]-1||y[nm+1]!=0){
+ /* if no ties follow, update the likelihood */
+ if(*model>=5)ly=log(yy);
+ a1=a+c;
+ b1=b;
+ /* add in birth and time-varying covariates */
+ if(!*rf){
+ if(*tvc){
+ bt=0;
+ for(k=0;k<*tvc;k++)bt+=p[*nccov+*birth+k+1]*tvcov[nm+*nbs*k];
+ if(*model<4){
+ if(bt>40) bt=40;
+ if(bt<-40)bt=-40;
+ bt=exp(bt)*beta;}
+ else bt+=beta;}
+ else bt=beta;
+ if(*birth){
+ if(*model<4)bt*=pow(j+1.,p[*nccov+1]);
+ else bt+=p[*nccov+1]*log(j+1);}}
+ else if(*tvc)bt=bb[nm];
+ if(!*density){
+ /* intensity models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ b1+=yy/bt;
+ h=-log(bt);
+ break;
+ case 2: /* Weibull distribution */
+ b1+=pow(yy/bt,lambda);
+ h=log(lambda/bt)+(lambda-1)*log(yy/bt);
+ break;
+ case 3: /* gamma distribution */
+ b1-=pgamma(yy,lambda,bt,0,1);
+ h=dgamma(yy,lambda,bt,1)-pgamma(yy,lambda,bt,0,1);
+ break;
+ case 4: /* generalized logistic distribution */
+ b1+=(yy+log(lambda+intercept*exp(-bt*yy))/bt)/lambda;
+ h=-log(lambda+intercept*exp(-bt*yy));
+ break;
+ case 5: /* log normal distribution */
+ b1-=pnorm(ly,bt,lambda,0,1);
+ h=dnorm(ly,bt,lambda,1)-ly-pnorm(ly,bt,lambda,0,1);
+ break;
+ case 6: /* log logistic distribution */
+ b1-=plogis(ly,bt,lambda,0,1);
+ h=dlogis(ly,bt,lambda,1)-ly-plogis(ly,bt,lambda,0,1);
+ break;
+ case 7: /* log Cauchy distribution */
+ b1-=pcauchy(ly,bt,lambda,0,1);
+ h=dcauchy(ly,bt,lambda,1)-ly-pcauchy(ly,bt,lambda,0,1);
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ b1-=log(1-plap);
+ h=log(tmp)-log(lambda*yy*(1-plap));
+ break;}}
+ else{
+ /* density models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ b1+=pexp(yy,bt,1,0);
+ h=dexp(yy,bt,1);
+ break;
+ case 2: /* Weibull distribution */
+ b1+=pweibull(yy,lambda,bt,1,0);
+ h=dweibull(yy,lambda,bt,1);
+ break;
+ case 3: /* gamma distribution */
+ b1+=pgamma(yy,lambda,bt,1,0);
+ h=dgamma(yy,lambda,bt,1);
+ break;
+ case 4: /* generalized logistic distribution */
+
b1+=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt));
+
h=-yy/lambda+(1/(lambda*bt)+1)*log((lambda+intercept)/(lambda+intercept*exp(-bt*yy)));
+ break;
+ case 5: /* log normal distribution */
+ b1+=pnorm(ly,bt,lambda,1,0);
+ h=dnorm(ly,bt,lambda,1)-ly;
+ break;
+ case 6: /* log logistic distribution */
+ b1+=plogis(ly,bt,lambda,1,0);
+ h=dlogis(ly,bt,lambda,1)-ly;
+ break;
+ case 7: /* log Cauchy distribution */
+ b1+=pcauchy(ly,bt,lambda,1,0);
+ h=dcauchy(ly,bt,lambda,1)-ly;
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ b1+=ly<bt?tmp:1-tmp;
+ h=log(tmp/lambda/yy);
+ break;}}
+ /* calculate likelihood */
+ *like-=(c>1?lgammafn(a1)-lgammafn(a)-lgammafn(c+1):c*log(a))+c*h;
+ /* is this correct for c>1? - gives gamma when family -> 0 */
+ /* c*(family-1)*log(b1) does not work */
+
if(*pfamily)*like-=(c>0)*(family-c)*log(b1)-a*(pow(b1,family)-pow(b,family))/family;
+ else *like-=a*log(b)-a1*log(b1);
+ /* calculate fitted values */
+ if(*fit){
+ pred[nm-c+cens[nm]]=bt;
+ tmp=b/a;
+ if(!*density){
+ switch(*model){
+ case 1: rpred[nm-c+cens[nm]]=bt*tmp; break;
+ case 2: rpred[nm-c+cens[nm]]=bt*pow(tmp,1/lambda); break;
+ case 3: rpred[nm-c+cens[nm]]=qgamma(1-exp(-tmp),lambda,bt,1,0);
break;
+ case 5: rpred[nm-c+cens[nm]]=exp(qnorm(1-exp(-tmp),bt,lambda,1,0));
break;
+ case 6:
rpred[nm-c+cens[nm]]=exp(qlogis(1-exp(-tmp),bt,lambda,1,0)); break;
+ case 7:
rpred[nm-c+cens[nm]]=exp(qcauchy(1-exp(-tmp),bt,lambda,1,0)); break;
+ case 8:
rpred[nm-c+cens[nm]]=exp(bt+lambda*log(2*(ly<bt?exp(-tmp):1-exp(-tmp))));
break;}}
+ else{
+ switch(*model){
+ case 1: rpred[nm-c+cens[nm]]=qexp(tmp,bt,1,0); break;
+ case 2: rpred[nm-c+cens[nm]]=qweibull(tmp,lambda,bt,1,0); break;
+ case 3: rpred[nm-c+cens[nm]]=qgamma(tmp,lambda,bt,1,0); break;
+ case 5: rpred[nm-c+cens[nm]]=exp(qnorm(tmp,bt,lambda,1,0)); break;
+ case 6: rpred[nm-c+cens[nm]]=exp(qlogis(tmp,bt,lambda,1,0)); break;
+ case 7: rpred[nm-c+cens[nm]]=exp(qcauchy(tmp,bt,lambda,1,0)); break;
+ case 8:
rpred[nm-c+cens[nm]]=exp(bt+lambda*log(2*(ly<bt?tmp:1-tmp))); break;}}}
+ /* update parameters */
+ switch(*dep){
+ case 1:
+ case 2: om=pow(omega,yy); a=om*a1+(1-om)*delta; break;
+ case 3:
+ case 4: a=omega*a1+(1-omega)*delta; break;
+ case 5: a=a1; break;
+ case 6:
+ case 7: a=omega*a1; break;
+ default: ;
+ }
+ switch(*dep){
+ case 1: b=om*(b1-b)+delta; break;
+ case 2: b=om*b1+(1-om)*delta; break;
+ case 3: b=omega*(b1-b)+delta; break;
+ case 4: b=omega*b1+(1-omega)*delta; break;
+ case 5:
+ case 7: b=omega*b1; break;
+ default: ;
+ }
+ c=0;}
+ nm++;}}
+ return;}
+
+void frailb(double p[],double y[],double x[],int cens[],int *nind,int nobs[],
+ int *nbs,int *nccov,int *model,int *density,int *dep,int *birth,
+ int *tvc,double tvcov[],int *fit,double pred[],double rpred[],
+ int *rf,double bb[],int *sf,double vv[],int *frser,
+ double *like){
+ int i,j,k,nm,ns,c,nn;
+ double b1,delta,lambda,beta,bt,l1,yy,kk,nb,ly,plap,tmp,intercept,H,btp,res;
+
+ *like=0;
+ nm=0;
+ delta=exp(p[*nccov+*birth+*tvc+1]);
+ if(*model>1&&!*sf){
+ if(*model<5)lambda=exp(p[*nccov+*birth+*tvc+*frser+2]);
+ else lambda=exp(p[*nccov+*birth+*tvc+*frser+2]/2);}
+ if(*model==4)intercept=exp(p[*nccov+*birth+*tvc+*frser+3]);
+ for(nn=i=0;i<*nind;i++)nn+=nobs[i];
+ for(i=0;i<*nind;i++){
+ if(!*rf){
+ beta=p[0];
+ for(k=0;k<*nccov;k++)beta+=p[k+1]*x[i+k**nind];
+ if(*model<4){
+ if(beta>40) beta=40;
+ if(beta<-40)beta=-40;
+ beta=exp(beta);}}
+ else if(!*tvc)bt=bb[i];
+ ns=b1=0;
+ if(!*dep)nb=1;
+ else {
+ nb=0;
+ for(j=0;j<nobs[i];j++)nb+=y[nm+j];
+ nb/=beta;}
+ res=0;
+ for(c=0,j=0;j<nobs[i];j++){
+ if(*model>1&&*sf)lambda=vv[nm];
+ ns+=cens[nm];
+ /* store value and check if ties to follow */
+ if(y[nm]>0)yy=y[nm];
+ c+=cens[nm];
+ if(j>=nobs[i]-1||y[nm+1]!=0){
+ if(*model>=5)ly=log(yy);
+ l1=log(1+delta*j/nb);
+ /* add in birth and time-varying covariates */
+ if(!*rf){
+ if(*tvc){
+ bt=0;
+ for(k=0;k<*tvc;k++)bt+=p[*nccov+*birth+k+1]*tvcov[nm+*nbs*k];
+ if(*model<4)bt=exp(bt)*beta;
+ else bt+=beta;}
+ else bt=beta;
+ if(*birth){
+ if(*model<4)bt*=pow(j+1.,p[*nccov+1]);
+ else bt+=p[*nccov+1]*log(j+1);}}
+ else if(*tvc)bt=bb[nm];
+ /* if AR, add discounted previous residual */
+ btp=bt;
+ if(*frser&&j>0){
+ if(*model<4)
+ bt=exp(log(bt)+exp(p[*nccov+*birth+*tvc+2]*y[nm])*res);
+ else bt+=exp(p[*nccov+*birth+*tvc+2]*y[nm])*res;}
+ if(!*density){
+ /* intensity models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=yy/bt;
+ l1+=-log(bt);
+ break;
+ case 2: /* Weibull distribution */
+ H=pow(yy/bt,lambda);
+ l1+=log(lambda/bt)+(lambda-1)*log(yy/bt);
+ break;
+ case 3: /* gamma distribution */
+ H=-pgamma(yy,lambda,bt,0,1);
+ l1+=dgamma(yy,lambda,bt,1)-pgamma(yy,lambda,bt,0,1);
+ break;
+ case 4: /* generalized logistic distribution */
+ H=(yy+log(lambda+intercept*exp(-bt*yy))/bt)/lambda;
+ l1+=-log(lambda+intercept*exp(-bt*yy));
+ break;
+ case 5: /* log normal distribution */
+ H=-pnorm(ly,bt,lambda,0,1);
+ l1+=dnorm(ly,bt,lambda,1)-ly-pnorm(ly,bt,lambda,0,1);
+ break;
+ case 6: /* log logistic distribution */
+ H=-plogis(ly,bt,lambda,0,1);
+ l1+=dlogis(ly,bt,lambda,1)/-ly-plogis(ly,bt,lambda,0,1);
+ break;
+ case 7: /* log Cauchy distribution */
+ H=-pcauchy(ly,bt,lambda,0,1);
+ l1+=dcauchy(ly,bt,lambda,1)-ly-pcauchy(ly,bt,lambda,0,1);
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ H=-log(1-plap);
+ l1+=tmp/(lambda*yy*(1-plap));
+ break;}}
+ else{
+ /* density models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=pexp(yy,bt,1,0);
+ l1+=dexp(yy,bt,1);
+ break;
+ case 2: /* Weibull distribution */
+ H=pweibull(yy,lambda,bt,1,0);
+ l1+=dweibull(yy,lambda,bt,1);
+ break;
+ case 3: /* gamma distribution */
+ H=pgamma(yy,lambda,bt,1,0);
+ l1+=dgamma(yy,lambda,bt,1);
+ break;
+ case 4: /* generalized logistic distribution */
+
H=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt));
+
l1+=-yy/lambda+log((lambda+intercept)/(lambda+intercept*exp(-bt*yy)))/((lambda*bt)+1);
+ break;
+ case 5: /* log normal distribution */
+ H=pnorm(ly,bt,lambda,1,0);
+ l1+=dnorm(ly,bt,lambda,1)-ly;
+ break;
+ case 6: /* log logistic distribution */
+ H=plogis(ly,bt,lambda,1,0);
+ l1+=dlogis(ly,bt,lambda,1)-ly;
+ break;
+ case 7: /* log Cauchy distribution */
+ H=pcauchy(ly,bt,lambda,1,0);
+ l1+=dcauchy(ly,bt,lambda,1)-ly;
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ H=ly<bt?tmp:1-tmp;
+ l1+=log(tmp/lambda/yy);
+ break;}}
+ /* calculate likelihood */
+ *like-=c*l1;
+ if(c>1)*like+=lgammafn(c+1);
+ if(*frser)res=(*model>6?ly:yy)-btp;
+ if(*fit){
+ pred[nm-c+cens[nm]]=btp;
+ tmp=(b1+nb/(nn*delta))/(nb/(nn*delta)+ns);
+ if(!*density){
+ switch(*model){
+ case 1: rpred[nm-c+cens[nm]]=bt*tmp; break;
+ case 2: rpred[nm-c+cens[nm]]=bt*pow(tmp,1/lambda); break;
+ case 3: rpred[nm]=qgamma(1-exp(-tmp),lambda,bt,1,0); break;
+ case 5: rpred[nm-c+cens[nm]]=exp(qnorm(1-exp(-tmp),bt,lambda,1,0));
+ break;
+ case 6: rpred[nm-c+cens[nm]]=exp(qlogis(1-exp(-tmp),bt,lambda,1,0));
+ break;
+ case 7:
rpred[nm-c+cens[nm]]=exp(qcauchy(1-exp(-tmp),bt,lambda,1,0));
+ break;
+ case 8:
rpred[nm-c+cens[nm]]=exp(bt+lambda*log(2*(ly<bt?exp(-tmp):1-exp(-tmp))));
+ break;}}
+ else{
+ switch(*model){
+ case 1: rpred[nm-c+cens[nm]]=qexp(tmp,bt,1,0); break;
+ case 2: rpred[nm-c+cens[nm]]=qweibull(tmp,lambda,bt,1,0); break;
+ case 3: rpred[nm-c+cens[nm]]=qgamma(tmp,lambda,bt,1,0); break;
+ case 5: rpred[nm-c+cens[nm]]=exp(qnorm(tmp,bt,lambda,1,0)); break;
+ case 6: rpred[nm-c+cens[nm]]=exp(qlogis(tmp,bt,lambda,1,0)); break;
+ case 7: rpred[nm-c+cens[nm]]=exp(qcauchy(tmp,bt,lambda,1,0));
+ break;
+ case 8:
rpred[nm-c+cens[nm]]=exp(bt+lambda*log(2*(ly<bt?tmp:1-tmp)));
+ break;}}}
+ b1+=H;
+ c=0;}
+ nm++;}
+ *like+=(nb/delta+ns)*log(1+delta*b1/nb);}
+ return;}
only in patch2:
unchanged:
--- r-noncran-lindsey-1.0.20041008.orig/fixed/kcountb.c
+++ r-noncran-lindsey-1.0.20041008/fixed/kcountb.c
@@ -0,0 +1,443 @@
+/*
+ * repeated : A Library of Repeated Measurements Models
+ * Copyright (C) 1998, 1999, 2000, 2001 J.K. Lindsey
+ *
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 2 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software
+ * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
+ *
+ * SYNOPSIS
+ *
+ * void kcountb(double p[],double y[],double *origin,int c[],double x[],
+ * int *nind,int nobs[],int *nbs,int *nccov,int *model,
+ * int *density,int *pfamily,int *dep,int *birth,int *tvc,double tvcov[],
+ * int *fit,double pred[],double rpred[],int *rf, double bbb[],
+ * int *sf, double vv[], double *like)
+ * void countfb(double p[],double y[],int c[], double x[],int *nind,
+ * int nobs[],int *nbs,int *nccov,int *model,int *density,
+ * int *tvc,double tvcov[],int *fit,double pred[],double rpred[],int *rf,
+ * double bbb[],int *sf,double vv[],int *frser,double *like)
+ *
+ * DESCRIPTION
+ *
+ * Functions to compute the likelihood function for various distributions
+ * inserted in a beta distribution with serial or frailty dependence using
+ * Kalman-type update for longitudinal count data.
+ *
+ */
+
+#include <math.h>
+#include <stddef.h>
+#include "R.h"
+#include "Rmath.h"
+
+static double dpvfp2(int y, double d, double s, double s1, double f);
+
+void kcountb(double p[],double y[],double *origin,int c[],double x[],
+ int *nind,int nobs[],int *nbs,int *nccov,int *model,
+ int *density,int *pfamily,int *dep,int *birth,int *tvc,double tvcov[],
+ int *fit,double pred[],double rpred[],int *rf, double bbb[],
+ int *sf, double vv[], double *like){
+ int i,j,j0,k,nm;
+ double a,a1,b,b1,bb,bb0,sc,delta,lambda,omega,om,beta,bt,H,yy,yy0,
+ kk,tmp,ly,ly0,plap,intercept,family;
+
+ *like=0;
+ nm=0;
+ delta=exp(-p[*nccov+*birth+*tvc+1]);
+
if(*dep>0)omega=exp(p[*nccov+*birth+*tvc+2])/(1+exp(p[*nccov+*birth+*tvc+2]));
+ if(*pfamily)family=p[*nccov+*birth+*tvc+2+(*dep>0)];
+ if(*model==4)intercept=exp(p[*nccov+*birth+*tvc+2+(*dep>0)+*pfamily]);
+ if(*model>1&&!*sf){
+ if(*model<5)
+ lambda=exp(p[*nccov+*birth+*tvc+2+(*dep>0)+*pfamily+(*model==4)]);
+ else lambda=exp(p[*nccov+*birth+*tvc+2+(*dep>0)+*pfamily+(*model==4)]/2);}
+ for(i=0;i<*nind;i++){
+ a=b=delta;
+ sc=bb0=ly0=0;
+ yy0=*origin;
+ if(!*rf){
+ beta=p[0];
+ for(k=0;k<*nccov;k++)beta+=p[k+1]*x[i+k**nind];
+ if(*model<4){
+ if(beta>40) beta=40;
+ if(beta<-40)beta=-40;
+ beta=exp(beta);}}
+ else if(!*tvc)bt=bbb[i];
+ j0=origin==0||*tvc?0:-1;
+ for(j=j0;j<nobs[i];j++){
+ yy=*origin+(j>-1?y[nm]:0);
+ if(*model>=5)ly=log(yy);
+ if(*model>1&&*sf)lambda=vv[nm];
+ a1=a+(j>-1?c[nm]:0);
+ b1=b;
+ /* add in birth and time-varying covariates */
+ if(!*rf){
+ if(*tvc){
+ bt=0;
+ for(k=0;k<*tvc;k++)bt+=p[*nccov+*birth+k+1]*tvcov[nm+*nbs*k];
+ if(*model<4){
+ if(bt>40) bt=40;
+ if(bt<-40)bt=-40;
+ bt=exp(bt)*beta;}
+ else bt+=beta;}
+ else bt=beta;
+ if(j>-1&&*birth){
+ sc+=c[nm];
+ if(*model<5)bt*=pow(sc,p[*nccov+1]);
+ else bt+=p[*nccov+1]*log(sc);}}
+ else if(*tvc)bt=bbb[nm];
+ if(!*tvc){
+ if(!*density){
+ /* intensity models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=yy/bt; break;
+ case 2: /* Weibull distribution */
+ H=pow(yy/bt,lambda); break;
+ case 3: /* gamma distribution */
+ H=-pgamma(yy,lambda,bt,0,1); break;
+ case 4: /* generalized logistic distribution */
+ H=(yy+log(lambda+intercept*exp(-bt*yy))/bt)/lambda; break;
+ case 5: /* log normal distribution */
+ H=-pnorm(ly,bt,lambda,0,1); break;
+ case 6: /* log logistic distribution */
+ H=-plogis(ly,bt,lambda,0,1); break;
+ case 7: /* log Cauchy distribution */
+ H=-pcauchy(ly,bt,lambda,0,1); break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ H=-log(1-plap);
+ break;}}
+ else{
+ /* density models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=pexp(yy,bt,1,0); break;
+ case 2: /* Weibull distribution */
+ H=pweibull(yy,lambda,bt,1,0); break;
+ case 3: /* gamma distribution */
+ H=pgamma(yy,lambda,bt,1,0); break;
+ case 4: /* generalized logistic distribution */
+
H=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt));
+ break;
+ case 5: /* log normal distribution */
+ H=pnorm(ly,bt,lambda,1,0); break;
+ case 6: /* log logistic distribution */
+ H=plogis(ly,bt,lambda,1,0); break;
+ case 7: /* log Cauchy distribution */
+ H=pcauchy(ly,bt,lambda,1,0); break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ H=ly<bt?tmp:1-tmp;
+ break;}}
+ bb=H;
+ H-=bb0;
+ bb0=bb;}
+ else {
+ /* if there are time-varying covariates, finesse the problem
+ by integrating over the interval, but with covariate values
+ from the end of the interval */
+ if(!*density){
+ /* intensity models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=(yy-yy0)/bt; break;
+ case 2: /* Weibull distribution */
+ H=pow(yy/bt,lambda)-pow(yy0/bt,lambda); break;
+ case 3: /* gamma distribution */
+ H=-pgamma(yy,lambda,bt,0,1)+pgamma(yy0,lambda,bt,0,1); break;
+ case 4: /* generalized logistic distribution */
+
H=(yy-yy0+(log(lambda+intercept*exp(-bt*yy))-log(lambda+intercept*exp(-bt*yy0)))/bt)/lambda;
+ break;
+ case 5: /* log normal distribution */
+ H=-pnorm(ly,bt,lambda,0,1)+(yy0>0?pnorm(ly0,bt,lambda,0,1):0);
+ break;
+ case 6: /* log logistic distribution */
+ H=-plogis(ly,bt,lambda,0,1)+(yy0>0?plogis(ly0,bt,lambda,0,1):0);
+ break;
+ case 7: /* log Cauchy distribution */
+ H=-pcauchy(ly,bt,lambda,0,1)+(yy0>0?pcauchy(ly0,bt,lambda,0,1):0);
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ H=-log(1-plap);
+ if(yy0>0){
+ tmp=exp(-fabs(ly0-bt)/lambda)/2;
+ plap=ly0<bt?tmp:1-tmp;
+ H+=log(1-plap);}
+ break;}}
+ else{
+ /* density models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=pexp(yy-yy0,bt,1,0); break;
+ case 2: /* Weibull distribution */
+ H=pweibull(yy,lambda,bt,1,0)-pweibull(yy0,lambda,bt,1,0); break;
+ case 3: /* gamma distribution */
+ H=pgamma(yy,lambda,bt,1,0)-pgamma(yy0,lambda,bt,1,0); break;
+ case 4: /* generalized logistic distribution */
+
H=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt))-exp(-yy0/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy0)),1/(lambda*bt));
+ break;
+ case 5: /* log normal distribution */
+ H=pnorm(ly,bt,lambda,1,0)-(yy0>0?pnorm(ly0,bt,lambda,1,0):0); break;
+ case 6: /* log logistic distribution */
+ H=plogis(ly,bt,lambda,1,0)-(yy0>0?plogis(ly0,bt,lambda,1,0):0);
+ break;
+ case 7: /* log Cauchy distribution */
+ H=pcauchy(ly,bt,lambda,1,0)-(yy0>0?pcauchy(ly0,bt,lambda,1,0):0);
+ break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ H=ly<bt?tmp:1-tmp;
+ if(yy0>0)H-=exp(-fabs(ly0-bt)/lambda)/2;
+ break;}}
+ ly0=ly;}
+ if(j>-1){
+ b1+=H;
+ /* calculate likelihood */
+ if(*pfamily)*like-=dpvfp2(c[nm],a,b,b1,family);
+ else *like-=lgammafn(a1)-lgammafn(a)+a*log(b)-a1*log(b1);
+ if(c[nm]>0)*like-=c[nm]*log(H);
+ if(c[nm]>1)*like+=lgammafn(c[nm]+1);
+ /* calculate fitted values */
+ if(*fit){
+ if(!*density){
+ switch(*model){
+ case 1: pred[nm]=1/bt; break;
+ case 2: pred[nm]=lambda*pow(yy/bt,lambda-1)/bt; break;
+ case 3: pred[nm]=dgamma(yy,lambda,bt,0)/(1-pgamma(yy,lambda,bt,1,0));
break;
+ case 4: pred[nm]=1/(lambda+intercept*exp(-bt*yy)); break;
+ case 5:
pred[nm]=dnorm(ly,bt,lambda,0)/(y[nm]*(1-pnorm(ly,bt,lambda,1,0)));
+ break;
+ case 6:
+
pred[nm]=dlogis(ly,bt,lambda,0)/(y[nm]*(1-plogis(ly,bt,lambda,1,0)));
+ break;
+ case 7:
+
pred[nm]=dcauchy(ly,bt,lambda,0)/(y[nm]*(1-pcauchy(ly,bt,lambda,1,0)));
+ break;
+ case 8:
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ pred[nm]=tmp/(lambda*y[nm]*(1-plap));
+ break;}}
+ else{
+ switch(*model){
+ case 1: pred[nm]=dexp(yy,bt,0); break;
+ case 2: pred[nm]=dweibull(yy,lambda,bt,0); break;
+ case 3: pred[nm]=dgamma(yy,lambda,bt,0); break;
+ case 4:
+
pred[nm]=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt)+1);
+ break;
+ case 5: pred[nm]=dnorm(ly,bt,lambda,0)/y[nm]; break;
+ case 6: pred[nm]=dlogis(ly,bt,lambda,0)/y[nm]; break;
+ case 7: pred[nm]=dcauchy(ly,bt,lambda,0)/y[nm]; break;
+ case 8:
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ pred[nm]=tmp/(lambda*y[nm]);
+ break;}}
+ rpred[nm]=j==0?pred[nm]:a*H/b;}
+ /* update parameters */
+ switch(*dep){
+ case 1: a=omega*a1+(1-omega)*delta; break;
+ case 2:
+ case 6:
+ om=pow(omega,yy-yy0);
+ a=om*a1+(1-om)*delta;
+ break;
+ case 3: a=a1; break;
+ case 4:
+ case 5: a=omega*a1; break;
+ default: ;
+ }
+ switch(*dep){
+ case 1: b=omega*b1+(1-omega)*delta; break;
+ case 2: b=om*b1+(1-om)*delta; break;
+ case 3:
+ case 5: b=omega*b1; break;
+ case 6: b=om*(b1-b)+delta; break;
+ default: ;
+ }
+ nm++;}
+ yy0=yy;}}
+ return;}
+
+void countfb(double p[],double y[],int c[], double x[],int *nind,
+ int nobs[],int *nbs,int *nccov,int *model,int *density,
+ int *tvc,double tvcov[],int *fit,double pred[],double rpred[],int *rf,
+ double bbb[], int *sf, double vv[], int *frser, double *like){
+ int i,j,k,nm;
+ double a,b,bb,bb0,delta,lambda,omega,om,beta,bt,H,yy,kk,tmp,ly,
+ plap,intercept,res;
+
+ *like=0;
+ nm=0;
+ delta=exp(p[*nccov+*tvc+1]);
+ if(*model>1&&!*sf){
+ if(*model<5)lambda=exp(p[*nccov+*tvc+*frser+2]);
+ else lambda=exp(p[*nccov+*tvc+*frser+2]/2);}
+ if(*model==4)intercept=exp(p[*nccov+*tvc+*frser+3]);
+ for(i=0;i<*nind;i++){
+ a=delta;
+ b=bb0=0;
+ if(!*rf){
+ beta=p[0];
+ for(k=0;k<*nccov;k++)beta+=p[k+1]*x[i+k**nind];
+ if(*model<4){
+ if(beta>40) beta=40;
+ if(beta<-40)beta=-40;
+ beta=exp(beta);}}
+ else if(!*tvc)bt=bbb[i];
+ res=0;
+ for(j=0;j<nobs[i];j++){
+ yy=y[nm];
+ if(*model>=5)ly=log(yy);
+ if(*model>1&&*sf)lambda=vv[nm];
+ a+=c[nm];
+ /* add in time-varying covariates */
+ if(!*rf){
+ if(*tvc){
+ bt=0;
+ for(k=0;k<*tvc;k++)bt+=p[*nccov+k+1]*tvcov[nm+*nbs*k];
+ if(*model<4)bt=exp(bt)*beta;
+ else bt+=beta;}
+ else bt=beta;}
+ else if(*tvc)bt=bbb[nm];
+ /* if AR, add discounted previous residual */
+ if(*frser&&j>0){
+ if(*model<4)bt=exp(log(bt)+exp(p[*nccov+*tvc+2]*(y[nm]-y[nm-1]))*res);
+ else bt+=exp(p[*nccov+*tvc+2]*(y[nm]-y[nm-1]))*res;}
+ if(!*density){
+ /* intensity models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=yy/bt; break;
+ case 2: /* Weibull distribution */
+ H=pow(yy/bt,lambda); break;
+ case 3: /* gamma distribution */
+ H=-pgamma(yy,lambda,bt,0,1); break;
+ case 4: /* generalized logistic distribution */
+ H=(yy+log(lambda+intercept*exp(-bt*yy))/bt)/lambda; break;
+ case 5: /* log normal distribution */
+ H=-pnorm(ly,bt,lambda,0,1); break;
+ case 6: /* log logistic distribution */
+ H=-plogis(ly,bt,lambda,0,1); break;
+ case 7: /* log Cauchy distribution */
+ H=-pcauchy(ly,bt,lambda,0,1); break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ H=-log(1-plap);
+ break;}}
+ else{
+ /* density models */
+ switch(*model){
+ case 1: /* exponential distribution */
+ H=pexp(yy,bt,1,0); break;
+ case 2: /* Weibull distribution */
+ H=pweibull(yy,lambda,bt,1,0); break;
+ case 3: /* gamma distribution */
+ H=pgamma(yy,lambda,bt,1,0); break;
+ case 4: /* generalized logistic distribution */
+
H=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt));
+ break;
+ case 5: /* log normal distribution */
+ H=pnorm(ly,bt,lambda,1,0); break;
+ case 6: /* log logistic distribution */
+ H=plogis(ly,bt,lambda,1,0); break;
+ case 7: /* log Cauchy distribution */
+ H=pcauchy(ly,bt,lambda,1,0); break;
+ case 8: /* log Laplace distribution */
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ H=ly<bt?tmp:1-tmp;
+ break;}}
+ bb=H;
+ H-=bb0;
+ bb0=bb;
+ b+=H;
+ /* if(*tvc)b+=H;
+ else if(j==nobs[i]-1)b=bb; */
+ /* calculate likelihood */
+ if(c[nm]>0)*like-=c[nm]*log(H)-lgammafn(c[nm]+1);
+ /* calculate fitted values */
+ if(*fit||*frser){
+ if(!*density){
+ switch(*model){
+ case 1: pred[nm]=1/bt; break;
+ case 2: pred[nm]=lambda*pow(yy/bt,lambda-1)/bt; break;
+ case 3: pred[nm]=dgamma(yy,lambda,bt,0)/(1-pgamma(yy,lambda,bt,1,0));
break;
+ case 4: pred[nm]=1/(lambda+intercept*exp(-bt*yy)); break;
+ case 5:
pred[nm]=dnorm(ly,bt,lambda,0)/(y[nm]*(1-pnorm(ly,bt,lambda,1,0)));
+ break;
+ case 6:
+
pred[nm]=dlogis(ly,bt,lambda,0)/(y[nm]*(1-plogis(ly,bt,lambda,1,0)));
+ break;
+ case 7:
+
pred[nm]=dcauchy(ly,bt,lambda,0)/(y[nm]*(1-pcauchy(ly,bt,lambda,1,0)));
+ break;
+ case 8:
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ plap=ly<bt?tmp:1-tmp;
+ pred[nm]=tmp/(lambda*y[nm]*(1-plap));
+ break;}}
+ else{
+ switch(*model){
+ case 1: pred[nm]=dexp(yy,bt,0); break;
+ case 2: pred[nm]=dweibull(yy,lambda,bt,0); break;
+ case 3: pred[nm]=dgamma(yy,lambda,bt,0); break;
+ case 4:
+
pred[nm]=exp(-yy/lambda)*pow((lambda+intercept)/(lambda+intercept*exp(-bt*yy)),1/(lambda*bt)+1);
+ break;
+ case 5: pred[nm]=dnorm(ly,bt,lambda,0)/y[nm]; break;
+ case 6: pred[nm]=dlogis(ly,bt,lambda,0)/y[nm]; break;
+ case 7: pred[nm]=dcauchy(ly,bt,lambda,0)/y[nm]; break;
+ case 8:
+ tmp=exp(-fabs(ly-bt)/lambda)/2;
+ pred[nm]=tmp/(lambda*y[nm]);
+ break;}}
+ rpred[nm]=a*H/(delta+b);
+ if(*frser)res=(*model>=4?ly:y[nm])-pred[nm];}
+ nm++;}
+ *like-=lgammafn(a)-a*log(delta+b);}
+ *like-=*nind*(-lgammafn(delta)+delta*log(delta));
+ return;}
+
+/* power variance function Poisson */
+
+static double pvfc2(int y, double d, double s1, double f){
+ int i,j;
+ double r,*c,tmp1,tmp2,tmp3;
+ c=(double*)R_alloc((size_t)(y*y),sizeof(double));
+ tmp1=gammafn(1.-f);
+ tmp2=log(d);
+ tmp3=log(s1);
+ for(i=0;i<y;i++){
+ c[i*(y+1)]=1;
+ if(i>0){
+ c[i*y]=gammafn(i+1-f)/tmp1;
+ if(i>1)
+ for(j=1;j<i;j++)c[y*i+j]=c[y*(i-1)+j-1]+c[y*(i-1)+j]*(i-(j+1)*f);}}
+ r=0.;
+ for(i=1;i<=y;i++){
+ r+=c[y*(y-1)+i-1]*exp(i*tmp2+(i*f-y)*tmp3);}
+ return(log(r));}
+
+static double dpvfp2(int y, double d, double s, double s1, double f){
+ double res;
+ if(f==0.)res=dnbinom(y,d,s/s1,1);
+ else {
+ res=-d*(pow(s1,f)-pow(s,f))/f;
+ if(y>0)res+=pvfc2(y,d,s1,f);}
+ return(res);}
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