void qux(int *nin, double *sin, double *tin, double *x)
{
int n = nin[0];
double s = sin[0];
double t = tin[0];
int i;
GetRNGstate();
for (i = 0; i < n; i++)
x[i] = rbeta(s, t);
PutRNGstate();
}
HHRESULT CGaussianMDP::sample_alpha
(
double par1,
double par2,
int n,
int k,
double &alpha
)
{
double b,odds,prob;
int ind;
HHRESULT hr = HH_OK;
b = rbeta(alpha+1,n);
odds = (par1+k-1)/(n*(par2-log(b)));
prob = odds/(odds+1);
ind = (int)rbinom(1,prob);
alpha = ind * rgamma2(par1+k, (par2-log(b))) +
(1-ind) * rgamma2(par1+k-1, (par2-log(b)));
return hr;
}
void newChain_kernel1(Chain *a){ /* kernel <<<1, 1>>> */
int n;
a->m = 1;
a->accD = 0;
a->tuneD = 400;
a->meanLogLik = 0;
a->logLikMean = 0;
a->dic = 0;
for(n = 0; n < a->N; ++n){
a->meanC[n] = 0;
a->c[n] = 0;
a->accC[n] = 0;
a->tuneC[n] = 1;
}
if(!a->constTau)
a->tau = sqrt(rgamma(a->aTau, a->bTau, 0));
if(!a->constPiAlp)
a->piAlp = rbeta(a->aAlp, a->bAlp);
if(!a->constPiDel)
a->piDel = rbeta(a->aDel, a->bDel);
if(!a->constD)
a->d = runiform(0, a->d0);
if(!a->constThePhi)
a->thePhi = rnormal(0, a->gamPhi);
if(!a->constTheAlp)
a->theAlp = rnormal(0, a->gamAlp);
if(!a->constTheDel)
a->theDel = rnormal(0, a->gamDel);
if(!a->constSigC)
a->sigC = runiform(0, a->sigC0);
if(!a->constSigPhi)
a->sigPhi = runiform(0, a->sigPhi0);
if(!a->constSigAlp)
a->sigAlp = runiform(0, a->sigAlp0);
if(!a->constSigDel)
a->sigDel = runiform(0, a->sigDel0);
a->s1 = 0;
a->s2 = 0;
for(n = 0; n < a->N; ++n){
a->Old[n] = 0;
a->New[n] = 0;
a->lOld[n] = 0;
a->lNew[n] = 0;
}
}
double BM::sim() const { return rbeta(a(),b());}
//extern "C"{
SEXP sampler(
/*prior params*/
double *a1, double *a2, /* prior for tau2*/
double *b1, double *b2, /* prior for sigma2 */
double *alphaW, double *betaW, /* prior for w */
double *v0, /* gamma */
double *varKsi, /*vector length qKsiUpdate!!*/
/*model dimensions*/
int *q, /*length of ksi*/
int *qKsiUpdate, /*length of updated ksi*/
int *p, /*length alpha*/
int *pPen, /*length penalized alpha/ tau2 / gamma*/
int *n, /* no. of obs.*/
int *d, /*vector (length p): group sizes*/
/*parameter vectors*/
double *beta,
double *alpha,
double *ksi,
double *tau2,
double *gamma,
double *sigma2,
double *w,
/* (precomputed) constants */
double *y,
double *X,
double *G,
double *scale,
double *offset,
/*info about updateBlocks*/
int *blocksAlpha,
int *indA1Alpha,
int *indA2Alpha,
int *blocksKsi,
int *indA1Ksi,
int *indA2Ksi,
/*MCMC parameters*/
int *pcts,
int *burnin,
int *thin,
int *totalLength,
int *verbose,
double *ksiDF,
int *scaleMode,
double *modeSwitching,
int *family,
double *acceptKsi,
double *acceptAlpha,
/*return matrices*/
double *betaMatR,
double *alphaMatR,
double *ksiMatR,
double *gammaMatR,
double *probV1MatR,
double *tau2MatR,
double *sigma2MatR,
double *wMatR,
double *likMatR,
double *logPostMatR
)
{
// ############################################### //
// ######## unwrap/initialize args ############### //
// ############################################### //
int pIncluded=0, i=0, j=0, startPen = *p-*pPen, qKsiNoUpdate = *q - *qKsiUpdate,
save = 0, keep = *burnin, nrv =1, info=0,
nSamp=(*totalLength-*burnin)/(*thin), oneInt = 1, zeroInt = 0;
double *p1 =Calloc(*pPen, double);
double infV = 100000, oneV = 1.0, zeroV = 0.0, minusOneV =-1.0;
double *one=&oneV, *zero=&zeroV, *minusOne=&minusOneV, *inf=&infV, acceptance=0;
double invSigma2 = 1 / *sigma2, sqrtInvSigma2 = R_pow(invSigma2, 0.5);
double *penAlphaSq, *alphaLong, *varAlpha, *priorMeanAlpha, *modeAlpha, *offsetAlpha;;
penAlphaSq = Calloc(*pPen, double);
for(int i=*p-*pPen; i<*p; i++) penAlphaSq[i- *p + *pPen] = R_pow(alpha[i], 2.0);
alphaLong = Calloc(*q, double);
F77_CALL(dgemm)("N","N", q, &oneInt, p, one, G, q, alpha, p, zero, alphaLong, q);
varAlpha = Calloc(*p, double);
for(int i=0; i<startPen; i++) varAlpha[i] = *inf; /*unpenalized*/
for(int i=startPen; i<*p; i++) varAlpha[i] = tau2[i-startPen]*gamma[i-startPen]; /*penalized*/
priorMeanAlpha = Calloc(*p, double);
setToZero(priorMeanAlpha, *p);
modeAlpha = Calloc(*p, double);
F77_CALL(dcopy)(p, alpha, &oneInt, modeAlpha, &oneInt);
offsetAlpha = Calloc(*n, double);
F77_CALL(dcopy)(n, offset, &oneInt, offsetAlpha, &oneInt);
double *ksiUpdate, *priorMeanKsi, *modeKsi, *offsetKsi;
int safeQKsiUpdate = imax2(1, *qKsiUpdate);
//ksiUpdate contains the last qKsiUpdate elements in ksi
ksiUpdate = Calloc(safeQKsiUpdate, double);
F77_CALL(dcopy)(&safeQKsiUpdate, &ksi[*q-safeQKsiUpdate], &oneInt, ksiUpdate, &oneInt);
priorMeanKsi = Calloc(safeQKsiUpdate, double);
setToZero(priorMeanKsi, safeQKsiUpdate);
for(int i=0; i<*qKsiUpdate; i++) priorMeanKsi[i] = 1.0;
modeKsi = Calloc(safeQKsiUpdate, double);
setToZero(modeKsi, safeQKsiUpdate);
for(int i=0; i<*qKsiUpdate; i++) modeKsi[i] = ksi[i+qKsiNoUpdate];
// offsetKsi = offset + X_d=1*alpha : use lin.predictor of grps with ksi==1 as offset
offsetKsi = Calloc(*n, double);
F77_CALL(dcopy)(n, offset, &oneInt, offsetKsi, &oneInt);
if(qKsiNoUpdate < *q){
if(qKsiNoUpdate > 0){
F77_CALL(dgemm)("N","N", n, &oneInt, &qKsiNoUpdate, one, X, n, alpha, &qKsiNoUpdate, one, offsetKsi, n);
}
}
double *eta, *resid, rss, *XAlpha, *XKsiUpdate, *etaOffset;
eta = Calloc(*n, double);
F77_CALL(dgemm)("N","N", n, &oneInt, q, one, X, n, beta, q, zero, eta, n);
resid = Calloc(*n, double);
rss = 0;
for(int i=0; i<*n; i++) {
resid[i] = y[i]-eta[i] - offset[i];
rss += R_pow(resid[i], 2.0);
}
XAlpha = Calloc(*p * (*n), double);
updateXAlpha(XAlpha, X, G, ksi, q, qKsiUpdate, p, n);
XKsiUpdate = Calloc( *n * safeQKsiUpdate, double);
setToZero(XKsiUpdate, *n * safeQKsiUpdate);
if(qKsiNoUpdate < *q){
updateXKsi(XKsiUpdate, X, alphaLong, q, &qKsiNoUpdate, n);
}
etaOffset = Calloc(*n, double);
for(int i=0; i<*n; i++) etaOffset[i] = eta[i]+offset[i];
// ############################################################ //
// ######## set up blocks for blockwise updates ############### //
// ############################################################ //
XBlockQR *AlphaBlocks = Calloc(*blocksAlpha, XBlockQR);
XBlockQR *KsiBlocks = Calloc(*blocksKsi, XBlockQR);
for(int i=0; i < *blocksAlpha; i++){
(AlphaBlocks[i]).indA1 = indA1Alpha[i];
(AlphaBlocks[i]).indA2 = indA2Alpha[i];
(AlphaBlocks[i]).qA = (AlphaBlocks[i]).indA2 - (AlphaBlocks[i]).indA1 + 1;
(AlphaBlocks[i]).qI = *p - (AlphaBlocks[i]).qA;
(AlphaBlocks[i]).qraux = Calloc((AlphaBlocks[i]).qA, double);
setToZero((AlphaBlocks[i]).qraux, (AlphaBlocks[i]).qA);
(AlphaBlocks[i]).work = Calloc((AlphaBlocks[i]).qA, double);
setToZero((AlphaBlocks[i]).work, (AlphaBlocks[i]).qA);
(AlphaBlocks[i]).pivots = Calloc((AlphaBlocks[i]).qA, int);
for(int j=0; j < (AlphaBlocks[i]).qA; j++) (AlphaBlocks[i]).pivots[j] = 0;
(AlphaBlocks[i]).coefI = Calloc((AlphaBlocks[i]).qI, double);
setToZero((AlphaBlocks[i]).coefI, (AlphaBlocks[i]).qI);
(AlphaBlocks[i]).Xa = Calloc(((AlphaBlocks[i]).qA + *n) * (AlphaBlocks[i]).qA, double);
setToZero((AlphaBlocks[i]).Xa, ((AlphaBlocks[i]).qA + *n) * (AlphaBlocks[i]).qA);
(AlphaBlocks[i]).Xi = Calloc(*n * (AlphaBlocks[i]).qI, double);
setToZero((AlphaBlocks[i]).Xi, *n * (AlphaBlocks[i]).qI );
(AlphaBlocks[i]).ya = Calloc(((AlphaBlocks[i]).qA + *n), double);
F77_CALL(dcopy)(n, y, &nrv, (AlphaBlocks[i]).ya, &nrv);
setToZero((AlphaBlocks[i]).ya + *n, (AlphaBlocks[i]).qA);
(AlphaBlocks[i]).m = Calloc((AlphaBlocks[i]).qA, double);
setToZero((AlphaBlocks[i]).m, (AlphaBlocks[i]).qA);
(AlphaBlocks[i]).err = Calloc((AlphaBlocks[i]).qA, double);
setToZero((AlphaBlocks[i]).err, (AlphaBlocks[i]).qA);
}
initializeBlocksQR(AlphaBlocks, XAlpha, *n, *blocksAlpha, *p, varAlpha, scale);
if(*qKsiUpdate > 0){
for(int i=0; i < *blocksKsi; i++){
(KsiBlocks[i]).indA1 = indA1Ksi[i];
(KsiBlocks[i]).indA2 = indA2Ksi[i];
(KsiBlocks[i]).qA = (KsiBlocks[i]).indA2 - (KsiBlocks[i]).indA1 + 1;
(KsiBlocks[i]).qI = *qKsiUpdate - (KsiBlocks[i]).qA;
(KsiBlocks[i]).qraux = Calloc((KsiBlocks[i]).qA, double);
setToZero((KsiBlocks[i]).qraux, (KsiBlocks[i]).qA);
(KsiBlocks[i]).work = Calloc((KsiBlocks[i]).qA, double);
setToZero((KsiBlocks[i]).work, (KsiBlocks[i]).qA);
(KsiBlocks[i]).pivots = Calloc((KsiBlocks[i]).qA, int);
for(int j=0; j < (KsiBlocks[i]).qA; j++) (KsiBlocks[i]).pivots[j] = 0;
(KsiBlocks[i]).coefI = Calloc((KsiBlocks[i]).qI, double);
setToZero((KsiBlocks[i]).coefI, (KsiBlocks[i]).qI);
(KsiBlocks[i]).Xa = Calloc(((KsiBlocks[i]).qA + *n) * (KsiBlocks[i]).qA, double);
setToZero((KsiBlocks[i]).Xa, ((KsiBlocks[i]).qA + *n) * (KsiBlocks[i]).qA);
(KsiBlocks[i]).Xi = Calloc(*n * (KsiBlocks[i]).qI, double);
setToZero((KsiBlocks[i]).Xi, *n * (KsiBlocks[i]).qI );
(KsiBlocks[i]).ya = Calloc(((KsiBlocks[i]).qA + *n), double);
F77_CALL(dcopy)(n, y, &nrv, (KsiBlocks[i]).ya, &nrv);
setToZero((KsiBlocks[i]).ya + *n, (KsiBlocks[i]).qA);
(KsiBlocks[i]).m = Calloc((KsiBlocks[i]).qA, double);
setToZero((KsiBlocks[i]).m, (KsiBlocks[i]).qA);
(KsiBlocks[i]).err = Calloc((KsiBlocks[i]).qA, double);
setToZero((KsiBlocks[i]).err, (KsiBlocks[i]).qA);
}
initializeBlocksQR(KsiBlocks, XKsiUpdate, *n, *blocksKsi, *qKsiUpdate, varKsi, scale);
}
// ############################################### //
// ######## start sampling ############### //
// ############################################### //
#ifdef Win32
R_FlushConsole();
#endif
/* sampling */
GetRNGstate();
for(i = 0; i < *totalLength; i++)
{
debugMsg("\n###########################################\n\n");
//update alpha
{
//update varAlpha
for(j=startPen; j<*p; j++) varAlpha[j] = tau2[j-startPen] * gamma[j-startPen];
//update alpha
updateCoefQR(y, XAlpha, AlphaBlocks,
*blocksAlpha,
alpha,
varAlpha, *p,
scale,
*n, nrv, oneInt, info, *minusOne, *zero, *one, 1, priorMeanAlpha,
*family, modeAlpha, eta, acceptAlpha, offsetAlpha, *modeSwitching, zeroInt);
}
//update ksi
if(qKsiNoUpdate < *q){
//update alphaLong = G %*% alpha
F77_CALL(dgemm)("N","N", q, &oneInt, p, one, G, q, alpha, p, zero, alphaLong, q);
//update design for ksi
updateXKsi(XKsiUpdate, X, alphaLong, q, &qKsiNoUpdate, n);
//update offsetKsi
if(qKsiNoUpdate > 0){
F77_CALL(dcopy)(n, offset, &oneInt, offsetKsi, &oneInt);
F77_CALL(dgemm)("N","N", n, &oneInt, &qKsiNoUpdate, one, X, n, alpha, &qKsiNoUpdate, one, offsetKsi, n);
}
for(j = 0; j < *qKsiUpdate; j++){
priorMeanKsi[j] = sign( 1/(1 + exp(-2*ksiUpdate[j]/varKsi[j])) - runif(0,1) );
}
if(*ksiDF>0){
updateVarKsi(ksiUpdate, varKsi, ksiDF, priorMeanKsi, qKsiNoUpdate, *q);
}
updateCoefQR(y, XKsiUpdate, KsiBlocks,
*blocksKsi,
ksiUpdate, varKsi, *qKsiUpdate,
scale,
*n, nrv, oneInt, info, *minusOne, *zero, *one, 1, priorMeanKsi,
*family, modeKsi, eta, acceptKsi, offsetKsi, *modeSwitching, *scaleMode);
//write back to ksi
F77_CALL(dcopy)(qKsiUpdate, ksiUpdate, &oneInt, &ksi[*q-*qKsiUpdate], &oneInt);
//rescale ksi, alpha & put back in ksiUpdate
if(*scaleMode > 0){
rescaleKsiAlpha(ksi, alpha, varKsi, tau2, G, d, *p, *q, qKsiNoUpdate, *pPen, *scaleMode, modeAlpha, modeKsi, *family);
F77_CALL(dcopy)(qKsiUpdate, &ksi[*q-*qKsiUpdate], &oneInt, ksiUpdate, &oneInt);
}
//update XAlpha
updateXAlpha(XAlpha, X, G, ksi, q, qKsiUpdate, p, n);
//update alphaLong = G %*% alpha
F77_CALL(dgemm)("N","N", q, &oneInt, p, one, G, q, alpha, p, zero, alphaLong, q);
} else {
F77_CALL(dcopy)(q, alpha, &oneInt, alphaLong, &oneInt);
}
for(int i = *p-*pPen; i < *p; i++) penAlphaSq[i - *p + *pPen] = R_pow(alpha[i], 2.0);
updateTau(penAlphaSq, gamma, tau2, *a1, *a2, *pPen);
updateP1Gamma(penAlphaSq, tau2, p1, gamma, *v0, *w, *pPen);
pIncluded = 0;
for(j=0; j<*p - startPen; j++) pIncluded += (gamma[j] == 1.0);
*w = rbeta( *alphaW + pIncluded, *betaW + *p - pIncluded );
// update beta
for(j = 0; j < *q; j++){
beta[j] = alphaLong[j]*ksi[j];
}
//update eta, eta+offset
F77_CALL(dgemm)("N", "N", n, &oneInt, q, one, X, n, beta, q, zero, eta, n);
for(int i=0; i<*n; i++) etaOffset[i] = eta[i] + offset[i];
//update sigma_eps
if(*family == 0){
//resid = y - eta - offset
F77_CALL(dcopy)(n, y, &nrv, resid, &nrv); //resid <- y
F77_CALL(daxpy)(n, minusOne, etaOffset, &nrv, resid, &nrv); //resid <- resid - eta - offset
//rss = resid'resid
rss = F77_CALL(ddot)(n, resid, &oneInt, resid, &oneInt);
//update sigma2
invSigma2 = rgamma(*n/2 + *b1, 1/(rss/2 + *b2));
sqrtInvSigma2 = R_pow(invSigma2, 0.5);
scale[0] = sqrtInvSigma2;
*sigma2 = 1 / invSigma2;
}
if(i >= *burnin){
/* report progress */
if(*verbose){
for(j=0; j<9; j++){
if(i == pcts[j]){
Rprintf(".");
#ifdef Win32
R_FlushConsole();
#endif
break;
}
}
}
/* save samples*/
if(i == keep){
for(j = 0; j < *q; j++){
(betaMatR)[save + j*nSamp] = beta[j];
(ksiMatR)[save + j*nSamp] = ksi[j];
}
for(j=0; j < *p; j++){
(alphaMatR)[save + j*nSamp] = alpha[j];
}
for(j=0; j < *pPen; j++){
(tau2MatR)[save + j*nSamp] = tau2[j];
(gammaMatR)[save + j*nSamp] = gamma[j];
(probV1MatR)[save + j*nSamp] = p1[j];
}
(wMatR)[save] = *w;
(sigma2MatR)[save] = *sigma2;
likMatR[save] = logLik(y, etaOffset, *family, scale, *n);
(logPostMatR)[save] = updateLogPost(y, alpha, varAlpha,
ksi, varKsi, scale, *b1, *b2, gamma, *w, *alphaW, *betaW,
tau2, *a1, *a2, *n, *q, *p, *pPen, pIncluded, qKsiNoUpdate, priorMeanKsi, *family, likMatR[save]);
keep = keep + *thin;
save ++;
R_CheckUserInterrupt();
}
} else {
if(*verbose){
if(i == (*burnin-1)){
Rprintf("b");
#ifdef Win32
R_FlushConsole();
#endif
}
}
}
} /* end for i*/
PutRNGstate();
if(*verbose) Rprintf(".");
if(*family > 0) {
acceptance = 0.0;
for(j=0; j<*blocksAlpha; j++) acceptance += acceptAlpha[j];
acceptance = 0.0;
if(qKsiNoUpdate < *q){
for(j=0; j<*blocksKsi; j++) acceptance += acceptKsi[j];
}
}
Free(etaOffset); Free(XKsiUpdate); Free(XAlpha); Free(resid); Free(eta);
Free(offsetKsi); Free(modeKsi); Free(priorMeanKsi); Free(ksiUpdate);
Free(offsetAlpha);
Free(modeAlpha);
Free(priorMeanAlpha);
Free(varAlpha);
Free(alphaLong);
Free(penAlphaSq);
freeXBlockQR(AlphaBlocks, *blocksAlpha);
if(qKsiNoUpdate < *q) freeXBlockQR(KsiBlocks, *blocksKsi);
Free(p1);
return(R_NilValue);
}/* end sampler ()*/
Type objective_function<Type>::operator() ()
{
DATA_STRING(distr);
DATA_INTEGER(n);
Type ans = 0;
if (distr == "norm") {
PARAMETER(mu);
PARAMETER(sd);
vector<Type> x = rnorm(n, mu, sd);
ans -= dnorm(x, mu, sd, true).sum();
}
else if (distr == "gamma") {
PARAMETER(shape);
PARAMETER(scale);
vector<Type> x = rgamma(n, shape, scale);
ans -= dgamma(x, shape, scale, true).sum();
}
else if (distr == "pois") {
PARAMETER(lambda);
vector<Type> x = rpois(n, lambda);
ans -= dpois(x, lambda, true).sum();
}
else if (distr == "compois") {
PARAMETER(mode);
PARAMETER(nu);
vector<Type> x = rcompois(n, mode, nu);
ans -= dcompois(x, mode, nu, true).sum();
}
else if (distr == "compois2") {
PARAMETER(mean);
PARAMETER(nu);
vector<Type> x = rcompois2(n, mean, nu);
ans -= dcompois2(x, mean, nu, true).sum();
}
else if (distr == "nbinom") {
PARAMETER(size);
PARAMETER(prob);
vector<Type> x = rnbinom(n, size, prob);
ans -= dnbinom(x, size, prob, true).sum();
}
else if (distr == "nbinom2") {
PARAMETER(mu);
PARAMETER(var);
vector<Type> x = rnbinom2(n, mu, var);
ans -= dnbinom2(x, mu, var, true).sum();
}
else if (distr == "exp") {
PARAMETER(rate);
vector<Type> x = rexp(n, rate);
ans -= dexp(x, rate, true).sum();
}
else if (distr == "beta") {
PARAMETER(shape1);
PARAMETER(shape2);
vector<Type> x = rbeta(n, shape1, shape2);
ans -= dbeta(x, shape1, shape2, true).sum();
}
else if (distr == "f") {
PARAMETER(df1);
PARAMETER(df2);
vector<Type> x = rf(n, df1, df2);
ans -= df(x, df1, df2, true).sum();
}
else if (distr == "logis") {
PARAMETER(location);
PARAMETER(scale);
vector<Type> x = rlogis(n, location, scale);
ans -= dlogis(x, location, scale, true).sum();
}
else if (distr == "t") {
PARAMETER(df);
vector<Type> x = rt(n, df);
ans -= dt(x, df, true).sum();
}
else if (distr == "weibull") {
PARAMETER(shape);
PARAMETER(scale);
vector<Type> x = rweibull(n, shape, scale);
ans -= dweibull(x, shape, scale, true).sum();
}
else if (distr == "AR1") {
PARAMETER(phi);
vector<Type> x(n);
density::AR1(phi).simulate(x);
ans += density::AR1(phi)(x);
}
else if (distr == "ARk") {
PARAMETER_VECTOR(phi);
vector<Type> x(n);
density::ARk(phi).simulate(x);
ans += density::ARk(phi)(x);
}
else if (distr == "MVNORM") {
PARAMETER(phi);
matrix<Type> Sigma(5,5);
for(int i=0; i<Sigma.rows(); i++)
for(int j=0; j<Sigma.rows(); j++)
Sigma(i,j) = exp( -phi * abs(i - j) );
density::MVNORM_t<Type> nldens = density::MVNORM(Sigma);
for(int i = 0; i<n; i++) {
vector<Type> x = nldens.simulate();
ans += nldens(x);
}
}
else if (distr == "SEPARABLE") {
PARAMETER(phi1);
PARAMETER_VECTOR(phi2);
array<Type> x(100, 200);
SEPARABLE( density::ARk(phi2), density::AR1(phi1) ).simulate(x);
ans += SEPARABLE( density::ARk(phi2), density::AR1(phi1) )(x);
}
else if (distr == "GMRF") {
PARAMETER(delta);
matrix<Type> Q0(5, 5);
Q0 <<
1,-1, 0, 0, 0,
-1, 2,-1, 0, 0,
0,-1, 2,-1, 0,
0, 0,-1, 2,-1,
0, 0, 0,-1, 1;
Q0.diagonal().array() += delta;
Eigen::SparseMatrix<Type> Q = asSparseMatrix(Q0);
vector<Type> x(5);
for(int i = 0; i<n; i++) {
density::GMRF(Q).simulate(x);
ans += density::GMRF(Q)(x);
}
}
else if (distr == "SEPARABLE_NESTED") {
PARAMETER(phi1);
PARAMETER(phi2);
PARAMETER(delta);
matrix<Type> Q0(5, 5);
Q0 <<
1,-1, 0, 0, 0,
-1, 2,-1, 0, 0,
0,-1, 2,-1, 0,
0, 0,-1, 2,-1,
0, 0, 0,-1, 1;
Q0.diagonal().array() += delta;
Eigen::SparseMatrix<Type> Q = asSparseMatrix(Q0);
array<Type> x(5, 6, 7);
for(int i = 0; i<n; i++) {
SEPARABLE(density::AR1(phi2),
SEPARABLE(density::AR1(phi1),
density::GMRF(Q) ) ).simulate(x);
ans += SEPARABLE(density::AR1(phi2),
SEPARABLE(density::AR1(phi1),
density::GMRF(Q) ) )(x);
}
}
else error( ("Invalid distribution '" + distr + "'").c_str() );
return ans;
}