Revision 9be3f5a0653739a591e2b30cc9e77900612dad9a authored by Wayne Zhang on 08 November 2011, 00:00:00 UTC, committed by Gabor Csardi on 08 November 2011, 00:00:00 UTC
1 parent 31a5c50
bcpglmm_tw.c
/************************************************************/
/* Function for the Markov Chain Monte Carlo algorithm */
/* in the Compound Poisson Generalized Linear Mixed */
/* Model using direct tweedie density evaluations */
/* Author: Wayne Zhang */
/* actuary_zhang@hotmail.com */
/************************************************************/
/**
* @file bcpglmm_tw.c
* @brief Function for implementing the MCMC algorithm
* in the Compound Poisson Generalized Linear Mixed Model using
* direct tweedie density evaluation
* @author Wayne Zhang
*/
#include "cplm.h"
#include "Matrix.h" /* for cholmod functions */
/** cholmod_common struct initialized */
extern cholmod_common c;
/*
* initiate cholmod and set the factorization form to be LL'
*/
/*
SEXP init(){
M_R_cholmod_start(&c);
c.final_ll = 1;
return R_NilValue ;
}
SEXP finish(){
M_cholmod_finish(&c);
return R_NilValue ;
}
*/
/************************************************/
/* Some utility functions */
/************************************************/
/**
* Compute the mean in cpglmm
*
* @param da a list object
*
*/
static void cpglmm_fitted(SEXP da){
int *dm = DIMS_ELT(da) ;
int nO = dm[nO_POS], nB = dm[nB_POS], i1 = 1 ;
double *offset= OFFSET_ELT(da), *X = X_ELT(da),
*link_power = LKP_ELT(da), *beta = BETA_ELT(da),
*eta = ETA_ELT(da), *mu = MU_ELT(da), one[] = {1,0};
CHM_DN ceta, u = AS_CHM_DN(getListElement(da,"u"));
CHM_SP Zt = Zt_ELT(da);
R_CheckStack();
// update eta
Memcpy(eta, offset, nO);
// eta := eta + X * beta
F77_CALL(dgemv)("N", &nO, &nB, one, X, &nO,
beta, &i1, one, eta, &i1);
ceta = N_AS_CHM_DN(eta, nO, 1);
R_CheckStack();
if (!M_cholmod_sdmult(Zt, 1 , one, one, u, ceta, &c))
error(_("cholmod_sdmult error returned"));
// update mu
cplm_mu_eta(mu, (double *) NULL, nO, eta, *link_power) ;
}
/**
* Set parameter to the k_th initial values provided in the inits slot
*
* @param da a list object
* @param k indicates the k_th set of initial values
*
*/
static void set_init(SEXP da, int k){
int *dm = DIMS_ELT(da) ;
int i, pos = 0, nB = dm[nB_POS], nU = dm[nU_POS],
nT = dm[nT_POS], *nc = NCOL_ELT(da);
SEXP inits = getListElement(da, "inits"),
Sig = getListElement(da, "Sigma");
double *Sigi, *init = REAL(VECTOR_ELT(inits,k));
Memcpy(BETA_ELT(da),init, nB) ;
PHI_ELT(da)[0] = init[nB] ;
P_ELT(da)[0] = init[nB+1];
Memcpy(U_ELT(da),init+nB+2, nU) ;
for (i=0;i<nT;i++){
Sigi = REAL(VECTOR_ELT(Sig,i)) ;
Memcpy(Sigi, init+nB+2+nU+pos, nc[i]*nc[i]) ;
pos += nc[i]*nc[i] ;
}
}
/**
* Set parameter to the ns_th column of the simulation results
*
* @param da a list object
* @param ns indicates the ns_th column
* @param sims matrix to store simulations results
*
*/
static void set_sims(SEXP da, int ns, double **sims){
SEXP Sig = getListElement(da, "Sigma");
int *dm = DIMS_ELT(da) ;
int i, pos = 0, nB = dm[nB_POS], nU = dm[nU_POS],
nT = dm[nT_POS], *nc = NCOL_ELT(da);
double *Sigi ;
for (i=0;i<nB;i++)
sims[ns][i] = BETA_ELT(da)[i];
sims[ns][nB] = PHI_ELT(da)[0] ;
sims[ns][nB+1] = P_ELT(da)[0] ;
for (i=0;i<nU;i++)
sims[ns][nB+2+i] = U_ELT(da)[i];
for (i=0;i<nT;i++){
Sigi = REAL(VECTOR_ELT(Sig, i));
Memcpy(sims[ns]+nB+2+nU+pos,Sigi, nc[i]*nc[i]) ;
pos += nc[i]*nc[i] ;
}
}
/************************************************/
/* Function to compute full conditionals */
/************************************************/
/**
* posterior log density of the index parameter p
*
* @param x value of p at which the log density is to be calculated
* @param data a void struct, cocerced to SEXP internally
*
* @return log posterior density
*/
double bcpglmm_post_p_tw(double x, void *data){
SEXP da= data;
int *dm = DIMS_ELT(da) ;
double *Y = Y_ELT(da), *mu = MU_ELT(da), phi = PHI_ELT(da)[0] ;
return -0.5*dl2tweedie(dm[nO_POS], Y, mu, phi, x) ;
}
/**
* posterior log density of the index parameter phi
*
* @param x value of phi at which the log density is to be calculated
* @param data a void struct, cocerced to SEXP internally
*
* @return log posterior density
*/
static double bcpglmm_post_phi_tw(double x, void *data){
SEXP da = data ;
int *dm = DIMS_ELT(da) ;
double *Y = Y_ELT(da), *mu = MU_ELT(da), p = P_ELT(da)[0] ;
return -0.5*dl2tweedie(dm[nO_POS], Y, mu, x, p) ;
}
/**
* posterior log density of of the vector of beta
*
* @param x vector of values for beta
* @param data void struct that is coerced to SEXP
*
* @return log posterior density for beta
*/
static double bcpglmm_post_beta_tw(double *x, void *data){
SEXP da = data ;
int *dm = DIMS_ELT(da) ;
int nO = dm[nO_POS],
nP = dm[nP_POS],
nB = dm[nB_POS];
int i, kk, *ygt0 = YPO_ELT(da) ;
double ld=0, p= P_ELT(da)[0], phi = PHI_ELT(da)[0];
double p2=2-p, p1=p-1;
double *wts =PWT_ELT(da), *Y = Y_ELT(da), *mu = MU_ELT(da),
*pbeta_mean = PBM_ELT(da), *pbeta_var = PBV_ELT(da),
*beta= BETA_ELT(da), *beta_old = Alloca(nB, double) ;
R_CheckStack() ;
// update mu
Memcpy(beta_old, beta, nB) ;
Memcpy(beta, x, nB) ;
cpglmm_fitted(da) ;
Memcpy(beta, beta_old, nB) ;
// loglikelihood from data
for (i=0; i<nO; i++)
ld += pow(mu[i],p2) * wts[i];
ld /= (- phi*p2) ;
for (i=0; i<nP; i++){
kk = ygt0[i] ;
ld += - Y[kk]*pow(mu[kk],-p1)*wts[kk] /(phi*p1);
}
// prior info
for (i=0;i<nB;i++)
ld += -0.5*(x[i]-pbeta_mean[i])*(x[i]-pbeta_mean[i])/pbeta_var[i] ;
return ld ;
}
/**
* posterior log density of of the vector of u
*
* @param x vector of values for beta
* @param data void struct that is coerced to SEXP
*
* @return log posterior density for u
*/
static double bcpglmm_post_u_tw(double *x, void *data){
SEXP da = data,
Sig = getListElement(da,"Sigma") ;
int *dm = DIMS_ELT(da) ;
int nO = dm[nO_POS], nP = dm[nP_POS],
nU = dm[nU_POS], nT = dm[nT_POS];
int i, j, k, kk,
*ygt0 = YPO_ELT(da), *Gp = Gp_ELT(da),
*nc = NCOL_ELT(da), *nlev= NLEV_ELT(da);
int mc = imax(nc, nT);
double ld=0, p= P_ELT(da)[0], phi = PHI_ELT(da)[0];
double p2=2-p, p1=p-1, *Sigi, *wts =PWT_ELT(da),
*Y = Y_ELT(da), *u=U_ELT(da), *mu = MU_ELT(da),
*xv = Alloca(mc, double), *iv = Alloca(mc*mc, double),
*u_old = Alloca(nU, double);
R_CheckStack() ;
// update mu
Memcpy(u_old, u, nU) ;
Memcpy(u, x, nU);
cpglmm_fitted(da) ;
Memcpy(u, u_old, nU) ;
// loglikelihood from data
for (i=0; i<nO; i++)
ld += pow(mu[i],p2) * wts[i];
ld /= (- phi*p2) ;
for (i=0; i<nP; i++){
kk = ygt0[i] ;
ld += - Y[kk]*pow(mu[kk],-p1)*wts[kk] /(phi*p1);
}
// prior info
for (i=0;i<nT;i++){
Sigi = REAL(VECTOR_ELT(Sig, i));
solve_po(nc[i],Sigi, iv) ;
for (j=0;j<nlev[i];j++){
for (k=0;k<nc[i];k++){
kk = Gp[i]+ j + k*nlev[i] ;
xv[k]= x[kk] ; // u vector when nc >1
}
ld += dmvnorm(nc[i], xv, (double*) NULL, iv);
}
}
return ld ;
}
/************************************************/
/* Main function to fit compound Poisson */
/* GLMM using Monte Carlo Markov Chains */
/************************************************/
/**
* implement MCMC for compound Poisson GLMM using tweedie density evaluation
*
* @param da a list object
* @param nR report interval
* @param nit number iterations
* @param nbn number of burn-in
* @param nth thinning rate
* @param sims a 2d array to store simulation results
* @param acc_pct a vector of length 4 to store acceptance percentage
*
*/
static void bcpglmm_mcmc_tw(SEXP da, int nR, int nit, int nbn, int nth,
double **sims, double *acc_pct){
SEXP Sig = getListElement(da,"Sigma"),
pSig = getListElement(da, "pSigma"), pSigi ;
int *dm = DIMS_ELT(da) ;
int nB = dm[nB_POS], nT = dm[nT_POS], nU = dm[nU_POS];
int i, j, iter, ns, pos, acc=0, *Gp = Gp_ELT(da),
*nc = NCOL_ELT(da), *nlev= NLEV_ELT(da), accept[]={0,0,0,0};
int mc = imax(nc, nT);
// bound for p and phi
double xl_p = BDP_ELT(da)[0], xr_p =BDP_ELT(da)[1],
xr_phi = BDPHI_ELT(da)[0];
// proposal covariance matrix etc..
double *mh_beta_var = EBV_ELT(da), *mh_u_var = EUV_ELT(da),
mh_p_var = EPV_ELT(da)[0], mh_phi_var = EPHIV_ELT(da)[0],
*beta= BETA_ELT(da), *u = U_ELT(da), *p = P_ELT(da),
*phi= PHI_ELT(da),
*beta_sim = Alloca(nB, double), *beta_m = Alloca(nB, double),
*u_sim=Alloca(nU, double), *u_m = Alloca(nU, double),
*scl = Alloca(mc*mc, double), *scl2 = Alloca(mc*mc, double);
double xtemp, su, *Sigi, p_sd = sqrt(mh_p_var), phi_sd = sqrt(mh_phi_var);
R_CheckStack() ;
// update eta and mu
cpglmm_fitted(da) ;
GetRNGstate() ;
for (iter=0;iter<nit;iter++){
if (nR>0 && (iter+1)%nR==0)
Rprintf("Iteration: %d \n ", iter+1) ;
R_CheckUserInterrupt() ;
// M-H update of p using truncated normal
acc = metrop_tnorm_rw(*p, p_sd, xl_p, xr_p, &xtemp,
bcpglmm_post_p_tw, (void *) da);
*p = xtemp ;
accept[0] += acc ;
R_CheckUserInterrupt() ;
//Metropolis-Hasting block update of beta
Memcpy(beta_m, beta, nB); // need this because of the copying step in post_beta
acc = metrop_mvnorm_rw(nB, beta_m, mh_beta_var,
beta_sim, bcpglmm_post_beta_tw, (void *)da) ;
Memcpy(beta, beta_sim, nB) ;
accept[1] += acc ;
cpglmm_fitted(da);
R_CheckUserInterrupt() ;
//Metropolis-Hasting block update of u
Memcpy(u_m, u, nU);
acc = metrop_mvnorm_rw(nU, u_m, mh_u_var,
u_sim, bcpglmm_post_u_tw, (void *)da) ;
Memcpy(u, u_sim, nU) ;
accept[2] += acc ;
cpglmm_fitted(da) ;
R_CheckUserInterrupt() ;
// M-H update of phi using truncated normal
acc = metrop_tnorm_rw(*phi, phi_sd, 0, xr_phi, &xtemp,
bcpglmm_post_phi_tw, (void *) da);
*phi = xtemp ;
accept[3] += acc ;
R_CheckUserInterrupt() ;
// direct simulation of Sigma due to conjugacy
for (i=0;i<nT;i++){
Sigi = REAL(VECTOR_ELT(Sig, i));
pSigi = VECTOR_ELT(pSig, i) ;
if (nc[i]==1){
// simulate from bounded inverse-Gamma
su = norm(u+Gp[i], nlev[i]) ;
Sigi[0] = 1 / rgamma(0.5*nlev[i]+IGSHP_ELT(pSigi)[0],
1/(su*su*0.5+IGSCL_ELT(pSigi)[0])) ;
}
else {
// simulate from inverse-Wishart
mult_xtx(nlev[i],nc[i], u+Gp[i], scl) ; // t(x) * (x)
pos = 0;
for (j=0;j<nc[i]*nc[i];j++)
scl[j] += IWSCL_ELT(pSigi)[j] ;
solve_po(nc[i], scl, scl2) ;
rwishart(nc[i], (double) nlev[i]+ IWDF_ELT(pSigi)[0], scl2, scl) ;
solve_po(nc[i], scl, Sigi) ;
}
}
// print out acceptance rate if necessary
if (nR>0 && (iter+1)%nR==0){
Rprintf(_("Acceptance rate: beta(%4.2f%%), u(%4.2f%%), phi(%4.2f%%), p(%4.2f%%),\n"),
accept[1]*1.0/(iter+1)*100, accept[2]*1.0/(iter+1)*100,
accept[3]*1.0/(iter+1)*100, accept[0]*1.0/(iter+1)*100 );
}
// store results
if (iter>=nbn && (iter+1-nbn)%nth==0 ){
ns = (iter+1-nbn)/nth -1;
set_sims(da, ns, sims) ;
}
}
PutRNGstate() ;
// compute acceptance percentage
for (i=0;i<4;i++)
acc_pct[i] = accept[i]*1.0/nit ;
}
/**
* tune the proposal covariance matrix
*
* @param da an input list object
* @param acc_pct a vector to store the acceptance rate
*
*/
static void bcpglmm_tune_tw(SEXP da, double *acc_pct){
int *dm = DIMS_ELT(da) ;
int nB = dm[nB_POS], nA = dm[nA_POS],
nU = dm[nU_POS], nR = dm[rpt_POS],
tn = dm[tnit_POS], ntn = dm[ntn_POS];
int i, j, k;
double **sims, tnw = REAL(getListElement(da,"tune.weight"))[0];
if (nR>0)
Rprintf("Tuning phase...\n");
int etn = ceil(tn *1.0/ntn) ; // # iters per tuning loop
sims = dmatrix(etn,nA) ;
// store samples and sample covariance
double *p_sims = dvect(etn), *phi_sims = dvect(etn),
*beta_sims = dvect(etn*nB), *u_sims = dvect(etn*nU);
double sam_p_var, sam_phi_var,
*sam_beta_var = dvect(nB*nB), *sam_u_var = dvect(nU*nU);
// proposal covariance matrix
double *mh_beta_var = EBV_ELT(da), *mh_u_var = EUV_ELT(da),
*mh_p_var = EPV_ELT(da), *mh_phi_var = EPHIV_ELT(da);
for (k=0;k<ntn;k++) {
// run mcmc
bcpglmm_mcmc_tw(da, 0, etn, 0, 1, sims, acc_pct);
// convert to long vector
for (i=0;i<etn;i++){
p_sims[i] = sims[i][nB+1] ;
phi_sims[i] = sims[i][nB] ;
for (j=0;j<nB;j++)
beta_sims[i+j*etn] = sims[i][j] ;
for (j=0;j<nU;j++)
u_sims[i+j*etn] = sims[i][nB+2+j] ;
}
// adjust proposal variance for p and phi
cov(etn,1,p_sims, &sam_p_var) ;
cov(etn,1,phi_sims, &sam_phi_var) ;
if (acc_pct[0]<0.4 || acc_pct[0] > 0.6)
*mh_p_var = tnw * (*mh_p_var) + (1-tnw) * sam_p_var ;
if (acc_pct[3]<0.4 || acc_pct[3] > 0.6)
*mh_phi_var = tnw * (*mh_phi_var) + (1-tnw) * sam_phi_var ;
// adjust vcov for beta and u
cov(etn, nB, beta_sims, sam_beta_var) ;
cov(etn, nU, u_sims, sam_u_var) ;
if (acc_pct[1]<0.15 || acc_pct[1] > 0.35){
for (i=0;i<nB*nB;i++)
mh_beta_var[i] = tnw * mh_beta_var[i] + (1-tnw) * sam_beta_var[i];
}
if (acc_pct[2]<0.15 || acc_pct[2] > 0.35){
for (i=0;i<nU*nU;i++)
mh_u_var[i] = tnw * mh_u_var[i] + (1-tnw) * sam_u_var[i];
}
}
if (nR>0){
Rprintf("Acceptance rate in the last tuning phase: beta(%4.2f%%), u(%4.2f%%), phi(%4.2f%%), p(%4.2f%%)\n",
acc_pct[1]*100, acc_pct[2]*100, acc_pct[3]*100, acc_pct[0]*100);
Rprintf("-----------------------------------------\n");
}
}
/**
* implement MCMC for compound Poisson GLMM using direct density evaluation
*
* @param da an input list object
*
* @return the simulated values
*
*/
SEXP bcpglmm_gibbs_tw (SEXP da){
// get dimensions
int *dm = DIMS_ELT(da) ;
int nA = dm[nA_POS],
nit = dm[itr_POS], nbn = dm[bun_POS],
nth = dm[thn_POS], nS = dm[kp_POS],
nR = dm[rpt_POS], tn = dm[tnit_POS];
int i, j, k;
double acc_pct[]={0,0,0,0},**sims;
SEXP ans, ans_tmp;
// tune the scale parameter for M-H update
if (tn)
bcpglmm_tune_tw(da, acc_pct) ;
// run Markov chains
PROTECT(ans=allocVector(VECSXP,dm[chn_POS])) ;
if (nR>0){
Rprintf("Markov Chain Monte Carlo starts...\n");
Rprintf("-----------------------------------------\n");
}
// simulations
sims = dmatrix(nS,nA) ;
for (k=0;k<dm[chn_POS];k++){
if (nR>0)
Rprintf("Start Markov chain %d\n", k+1);
// re-initialize
set_init(da, k) ;
bcpglmm_mcmc_tw(da, nR, nit, nbn, nth, sims, acc_pct);
//return result
PROTECT(ans_tmp=allocMatrix(REALSXP, nS, nA));
for (j=0;j<nA;j++){
for (i=0;i<nS;i++)
REAL(ans_tmp)[i+nS*j]= sims[i][j] ;
}
SET_VECTOR_ELT(ans, k, ans_tmp);
UNPROTECT(1) ;
if (nR>0)
Rprintf("-----------------------------------------\n");
}
UNPROTECT(1) ;
if (nR>0)
Rprintf("Markov Chain Monte Carlo ends!\n");
return ans ;
}
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