SEXP removeHets(SEXP founders_sexp, SEXP finals_sexp, SEXP hetData_sexp) { BEGIN_RCPP Rcpp::IntegerMatrix founders = Rcpp::as<Rcpp::IntegerMatrix>(founders_sexp); Rcpp::IntegerMatrix finals = Rcpp::as<Rcpp::IntegerMatrix>(finals_sexp); Rcpp::S4 hetData = Rcpp::as<Rcpp::S4>(hetData_sexp); Rcpp::List hetDataList = Rcpp::as<Rcpp::List>(hetData_sexp); int nMarkers = founders.ncol(), nFounders = founders.nrow(), nFinals = finals.nrow(); recodeDataStruct args; Rcpp::List recodedHetData(nMarkers); Rcpp::IntegerMatrix recodedFounders(founders.nrow(), founders.ncol()), recodedFinals(finals.nrow(), finals.ncol()); args.founders = founders; args.finals = finals; args.hetData = hetData; args.recodedHetData = recodedHetData; args.recodedFounders = recodedFounders; args.recodedFinals = recodedFinals; recodeFoundersFinalsHets(args); for(int i = 0; i < nMarkers; i++) { int maxAllele = 0; for(int j = 0; j < nFounders; j++) maxAllele = std::max(maxAllele, recodedFounders(j, i)); //Overwrite the recoded finals data for(int j = 0; j < nFinals; j++) { if(recodedFinals(j, i) > maxAllele) recodedFinals(j, i) = NA_INTEGER; else recodedFinals(j, i) = finals(j, i); } //overwrite the recoded het data Rcpp::IntegerMatrix newHetEntry(maxAllele+1, 3); Rcpp::IntegerMatrix oldHetEntry = Rcpp::as<Rcpp::IntegerMatrix>(hetDataList(i)); int counter = 0; for(int j = 0; j < oldHetEntry.nrow(); j++) { if(oldHetEntry(j, 0) == oldHetEntry(j, 1)) { newHetEntry(counter, 0) = newHetEntry(counter, 1) = newHetEntry(counter, 2) = oldHetEntry(j, 1); counter++; } } recodedHetData(i) = newHetEntry; } Rcpp::rownames(recodedFinals) = Rcpp::rownames(finals); Rcpp::colnames(recodedFinals) = Rcpp::colnames(finals); return Rcpp::List::create(Rcpp::Named("finals") = recodedFinals, Rcpp::Named("hetData") = recodedHetData); END_RCPP }
// [[Rcpp::export]] Rcpp::IntegerMatrix quantileNorm(Rcpp::IntegerMatrix mat, Rcpp::IntegerVector ref, int nthreads=1, int seed=13){ if (mat.nrow() != ref.length()) Rcpp::stop("incompatible arrays..."); if (!std::is_sorted(ref.begin(), ref.end())) Rcpp::stop("ref must be sorted"); int ncol = mat.ncol(); int nrow = mat.nrow(); //allocate new matrix Rcpp::IntegerMatrix res(nrow, ncol); Mat<int> oldmat = asMat(mat); Mat<int> newmat = asMat(res); Vec<int> ref2 = asVec(ref); //allocate a seed for each column std::seed_seq sseq{seed}; std::vector<std::uint32_t> seeds(ncol); sseq.generate(seeds.begin(), seeds.end()); #pragma omp parallel num_threads(nthreads) { std::vector<std::pair<int, int> > storage(nrow);//pairs <value, index> #pragma omp for for (int col = 0; col < ncol; ++col){ std::mt19937 gen(seeds[col]); qtlnorm(oldmat.getCol(col), ref2, newmat.getCol(col), storage, gen); } } res.attr("dimnames") = mat.attr("dimnames"); return res; }
static Rcpp::NumericMatrix x_tilde(Rcpp::NumericMatrix X, Rcpp::IntegerVector nk, Rcpp::IntegerMatrix groups, Rcpp::NumericVector d_cur, Rcpp::NumericMatrix eta_cur) { int K = nk.size(); int n_tot = X.nrow(); int p = X.ncol(); int L = groups.ncol(); Rcpp::NumericMatrix result(n_tot, p * K); int idx = 0; for (int k = 0; k < K; k++) { int n = nk[k]; for (int j = 0; j < p; j++) { //calculate sum for column j double sum = 0.0; for (int l = 0; l < L; l++) { if (elem(groups, j, l)) { sum += d_cur[l] * elem(eta_cur, l, k); } } //multiply column j in submatrix k of X with sum for (int i = 0; i < n; i++) { elem(result, idx + i, p * k + j) = elem(X, idx + i, j) * sum; } } idx += n; } return result; }
static Rcpp::NumericMatrix next_beta(Rcpp::IntegerVector nk, Rcpp::IntegerMatrix groups, Rcpp::NumericMatrix alpha_new, Rcpp::NumericVector d_new, Rcpp::NumericMatrix eta_new) { int K = nk.size(); int p = groups.nrow(); int L = groups.ncol(); Rcpp::NumericMatrix result(p, K); for (int k = 0; k < K; k++) { for (int j = 0; j < p; j++) { double sum = 0.0; for (int l = 0; l < L; l++) { if (elem(groups, j, l)) { sum += d_new[l] * elem(eta_new, l, k); } } elem(result, j, k) = elem(alpha_new, j, k) * sum; } } return result; }
static Rcpp::NumericMatrix x_tilde_3(Rcpp::NumericMatrix X, Rcpp::IntegerVector nk, Rcpp::IntegerMatrix groups, Rcpp::NumericMatrix alpha_new, Rcpp::NumericVector d_new) { int K = nk.size(); int n_tot = X.nrow(); int p = X.ncol(); int L = groups.ncol(); Rcpp::NumericMatrix result(n_tot, L * K); int idx = 0; for (int k = 0; k < K; k++) { int n = nk[k]; for (int l = 0; l < L; l++) { for (int i = 0; i < n; i++) { double sum = 0.0; for (int j = 0; j < p; j++) { if (elem(groups, j, l)) { sum += elem(X, idx + i, j) * elem(alpha_new, j, k); } } elem(result, idx + i, L * k + l) = d_new[l] * sum; } } idx += n; } return result; }
static Rcpp::NumericMatrix x_tilde_2(Rcpp::NumericMatrix X, Rcpp::IntegerVector nk, Rcpp::IntegerMatrix groups, Rcpp::NumericMatrix alpha_new, Rcpp::NumericMatrix eta_cur) { int K = nk.size(); int n_tot = X.nrow(); int p = X.ncol(); int L = groups.ncol(); Rcpp::NumericMatrix result(n_tot, L); for (int l = 0; l < L; l++) { int k = -1; int n = 0; for (int i = 0; i < n_tot; i++) { if (i == n){ k +=1; n += nk[k]; } double sum = 0.0; for (int j = 0; j < p; j++) { if (elem(groups, j, l)) { sum += elem(X, i, j) * elem(alpha_new, j, k); } } elem(result, i, l) = elem(eta_cur, l, k) * sum; } } return result; }
SEXP exact(SEXP matrix_sexp, SEXP progress_sexp) { BEGIN_RCPP mpz_class sum = 0; mpz_class rowsumprod, rowsum; Rcpp::IntegerMatrix matrix = Rcpp::as<Rcpp::IntegerMatrix>(matrix_sexp); if(matrix.ncol() != matrix.nrow()) { throw std::runtime_error("Matrix must be square"); } bool progress = Rcpp::as<bool>(progress_sexp); Rcpp::Function txtProgressBar("txtProgressBar"), setTxtProgressBar("setTxtProgressBar"), close("close"); Rcpp::RObject barHandle; if(progress) { barHandle = txtProgressBar(Rcpp::Named("style") = 1, Rcpp::Named("min") = 0, Rcpp::Named("max") = 1000, Rcpp::Named("initial") = 0); } int dimension = matrix.nrow(); unsigned long long C = (1ULL << dimension); for (unsigned long long k = 1ULL; k < C; k++) { rowsumprod = 1; int setBits = 0; for (int m = 0; m < dimension; m++) { if((k & (1ULL << m)) != 0ULL) setBits++; } // loop columns of submatrix #k for (int m = 0; m < dimension; m++) { rowsum = 0; // loop rows and compute rowsum for (int p = 0; p < dimension; p++) { if((k & (1ULL << p)) != 0ULL) { rowsum += matrix(m, p); } } // update product of rowsums rowsumprod *= rowsum; if (rowsumprod == 0) break; } if(setBits % 2 == 0) sum += rowsumprod; else sum -= rowsumprod; if(progress && k % 1000ULL == 0) { setTxtProgressBar.topLevelExec(barHandle, (int)((double)k*1000.0 / (double)C)); } } if(progress) { close(barHandle); } if(dimension % 2 != 0) sum *= -1; return Rcpp::wrap(sum.str()); END_RCPP }
void CEnv::SetData(Rcpp::IntegerMatrix x_, Rcpp::IntegerMatrix mcz_) { int J = x_.nrow(); int n = x_.ncol(); int nZeroMC = mcz_.ncol(); if (mcz_.nrow() != J) { // no mcz nZeroMC = 0; } intvec x = Rcpp::as<intvec>(x_); intvec mcz = Rcpp::as<intvec>(mcz_); Rcpp::List something(x_.attr("levels")); intvec levels(something.size()); for (unsigned int i = 0; i < levels.size(); i++) { SEXP exp = something[i]; Rcpp::CharacterVector v(exp); levels[i] = v.size(); //Rprintf( "%d\n", levels[i]) ; } SetData(x, J, n, mcz, nZeroMC, levels); }
// [[Rcpp::export]] Rcpp::IntegerVector colSummary(Rcpp::IntegerMatrix mat, std::string type, int nthreads=1){ //allocate final array Rcpp::IntegerVector ref(mat.ncol()); Mat<int> smat = asMat(mat); Vec<int> sref = asVec(ref); //compute function 'type' for each column if (type == "median"){ colSummary_loop(smat, sref, median, nthreads); } else if (type == "mean"){ colSummary_loop(smat, sref, mean, nthreads); } else if (type == "min"){ colSummary_loop(smat, sref, min, nthreads); } else Rcpp::stop("invalid type"); return ref; }
unsigned int GetTag(const Rcpp::IntegerMatrix& board, const int i, const int j) { // static constants const int nrow = board.nrow(); const int ncol = board.ncol(); // offset (assumes odd) -- integer divide intentional static const int offset = (sub_board_width/2); // data structure to hold the subboard's bits std::bitset<sub_board_width*sub_board_width> bits; unsigned int bit_index = (sub_board_width*sub_board_width)-1; const int k_start = i - offset; const int k_end = i + offset; const int l_start = j - offset; const int l_end = j + offset; // fill the bitset for (int k = k_start; k <= k_end; k++) { //std::cout << "k = " << k << std::endl; for (int l = l_start; l <= l_end; l++) { // check row boundaries if (k < 0 or k > (nrow-1)) { bits.set(bit_index, false); } // check col boundaries else if (l < 0 or l > (ncol-1)) { bits.set(bit_index, false); } else { bits.set(bit_index, static_cast<bool>(board(k,l))); } //std::cout << "\nbits(" << bit_index << ") = " << bits[bit_index] << std::endl; bit_index--; } } return static_cast<unsigned int>(bits.to_ulong()); }
// [[Rcpp::export]] Rcpp::IntegerVector getRef(Rcpp::IntegerMatrix mat, std::string type, int nthreads=1){ //allocate another matrix with transpose dimensions as mat int ncol = mat.ncol(); int nrow = mat.nrow(); if (ncol*nrow == 0) Rcpp::stop("empty input is invalid"); Rcpp::IntegerMatrix mem_smat(ncol, nrow); Mat<int> smat = asMat(mem_smat); Mat<int> omat = asMat(mat); //sort every column #pragma omp parallel for num_threads(nthreads) for (int col = 0; col < ncol; ++col){ Vec<int> ovec = omat.getCol(col); MatRow<int> svec = smat.getRow(col); sortCounts(ovec, svec); } return colSummary(mem_smat, type, nthreads); }
// [[Rcpp::export]] Rcpp::NumericMatrix CramersV_DF(Rcpp::IntegerMatrix dm, bool Bias_Cor) { int iCol = dm.ncol(); int i=0,j=0; Rcpp::NumericMatrix ResCV(iCol,iCol); for (i=0;i<iCol;i++){ for (j=i;j<iCol;j++){ if(i==j) {ResCV(i,j)=1;} else { ResCV(i,j) = CramersV_C((IntegerVector)dm(_,i),(IntegerVector)dm(_,j),(bool)Bias_Cor); ResCV(j,i) = ResCV(i,j); } } } Rcpp::List dimnms = Rcpp::List::create(VECTOR_ELT(dm.attr("dimnames"), 1), VECTOR_ELT(dm.attr("dimnames"), 1)); ResCV.attr("dimnames")=dimnms; return ResCV; }
/* This function specifically checks whether the observed data is consistent with the *pedigree*. It assumes that every observed value in the finals is already valid - That is, every observed value contained in the finals is also listed as a possibility in the hetData object */ void estimateRFCheckFunnels(Rcpp::IntegerMatrix finals, Rcpp::IntegerMatrix founders, Rcpp::List hetData, Rcpp::S4 pedigree, std::vector<int>& intercrossingGenerations, std::vector<std::string>& warnings, std::vector<std::string>& errors, std::vector<funnelType>& allFunnels, std::vector<funnelType>& lineFunnels) { Rcpp::CharacterVector pedigreeLineNames = Rcpp::as<Rcpp::CharacterVector>(pedigree.slot("lineNames")); //We make a copy of the pedigree line names and sort it (otherwise the std::find relating to pedigreeLineNames is prohibitive) std::vector<pedigreeLineStruct> sortedLineNames; sortPedigreeLineNames(pedigreeLineNames, sortedLineNames); Rcpp::IntegerVector mother = Rcpp::as<Rcpp::IntegerVector>(pedigree.slot("mother")); Rcpp::IntegerVector father = Rcpp::as<Rcpp::IntegerVector>(pedigree.slot("father")); bool warnImproperFunnels = Rcpp::as<bool>(pedigree.slot("warnImproperFunnels")); Rcpp::CharacterVector finalNames = Rcpp::as<Rcpp::CharacterVector>(Rcpp::as<Rcpp::List>(finals.attr("dimnames"))[0]); Rcpp::CharacterVector markerNames = Rcpp::as<Rcpp::CharacterVector>(Rcpp::as<Rcpp::List>(finals.attr("dimnames"))[1]); int nFinals = finals.nrow(), nFounders = founders.nrow(), nMarkers = finals.ncol(); if(nFounders != 2 && nFounders != 4 && nFounders != 8 && nFounders != 16) { throw std::runtime_error("Number of founders must be 2, 4, 8, or 16"); } xMajorMatrix<int> foundersToMarkerAlleles(nFounders, nFounders, nMarkers, -1); for(int markerCounter = 0; markerCounter < nMarkers; markerCounter++) { Rcpp::IntegerMatrix currentMarkerHetData = hetData(markerCounter); for(int founderCounter1 = 0; founderCounter1 < nFounders; founderCounter1++) { for(int founderCounter2 = 0; founderCounter2 < nFounders; founderCounter2++) { int markerAllele1 = founders(founderCounter1, markerCounter); int markerAllele2 = founders(founderCounter2, markerCounter); for(int hetDataRowCounter = 0; hetDataRowCounter < currentMarkerHetData.nrow(); hetDataRowCounter++) { if(markerAllele1 == currentMarkerHetData(hetDataRowCounter, 0) && markerAllele2 == currentMarkerHetData(hetDataRowCounter, 1)) { foundersToMarkerAlleles(founderCounter1, founderCounter2, markerCounter) = currentMarkerHetData(hetDataRowCounter, 2); } } } } } std::vector<long> individualsToCheckFunnels; for(long finalCounter = 0; finalCounter < nFinals; finalCounter++) { individualsToCheckFunnels.clear(); std::string currentLineName = Rcpp::as<std::string>(finalNames(finalCounter)); std::vector<pedigreeLineStruct>::iterator findLineName = std::lower_bound(sortedLineNames.begin(), sortedLineNames.end(), pedigreeLineStruct(currentLineName, -1)); if(findLineName == sortedLineNames.end() || findLineName->lineName != currentLineName) { std::stringstream ss; ss << "Unable to find line number " << finalCounter << " named " << finalNames(finalCounter) << " in pedigree"; throw std::runtime_error(ss.str().c_str()); } int pedigreeRow = findLineName->index; //This vector lists all the founders that are ancestors of the current line. This may comprise any number - E.g. if we have an AIC line descended from funnels 1,2,1,2 and 2,3,2,3 then this vector is going it contain 1,2,3 std::vector<int> representedFounders; if(intercrossingGenerations[finalCounter] == 0) { individualsToCheckFunnels.push_back(pedigreeRow); } else { try { getAICParentLines(mother, father, pedigreeRow, intercrossingGenerations[finalCounter], individualsToCheckFunnels); } catch(...) { std::stringstream ss; ss << "Error while attempting to trace intercrossing lines for line " << finalNames(finalCounter); errors.push_back(ss.str()); goto nextLine; } } //Now we know the lines for which we need to check the funnels from the pedigree (note: We don't necessarily have genotype data for all of these, it's purely a pedigree check) //Fixed length arrays to store funnels. If we have less than 16 founders then part of this is garbage and we don't use that bit.... funnelType funnel, copiedFunnel; for(std::vector<long>::iterator i = individualsToCheckFunnels.begin(); i != individualsToCheckFunnels.end(); i++) { try { getFunnel(*i, mother, father, &(funnel.val[0]), nFounders); } catch(...) { std::stringstream ss; ss << "Attempting to trace pedigree for line " << finalNames(finalCounter) << ": Unable to get funnel for line " << pedigreeLineNames(*i); errors.push_back(ss.str()); goto nextLine; } //insert these founders into the vector containing all the represented founders representedFounders.insert(representedFounders.end(), &(funnel.val[0]), &(funnel.val[0]) + nFounders); //Copy the funnel memcpy(&copiedFunnel, &funnel, sizeof(funnelType)); std::sort(&(copiedFunnel.val[0]), &(copiedFunnel.val[0]) + nFounders); if(std::unique(&(copiedFunnel.val[0]), &(copiedFunnel.val[0]) + nFounders) != &(copiedFunnel.val[0]) + nFounders) { //If we have intercrossing generations then having repeated founders is an error. Otherwise if warnImproperFunnels is true it's still a warning. if(intercrossingGenerations[finalCounter] != 0 || warnImproperFunnels) { std::stringstream ss; ss << "Funnel for line " << pedigreeLineNames(*i) << " contained founders {" << funnel.val[0]; if(nFounders == 2) { ss << ", " << funnel.val[1] << "}"; } else if(nFounders == 4) { ss << ", " << funnel.val[1] << ", " << funnel.val[2] << ", " << funnel.val[3] << "}"; } else if(nFounders == 8) { ss << ", " << funnel.val[1] << ", " << funnel.val[2] << ", " << funnel.val[3] << ", " << funnel.val[4] << ", " << funnel.val[5] << ", " << funnel.val[6] << ", " << funnel.val[7]<< "}"; } else if (nFounders == 16) { ss << ", " << funnel.val[1] << ", " << funnel.val[2] << ", " << funnel.val[3] << ", " << funnel.val[4] << ", " << funnel.val[5] << ", " << funnel.val[6] << ", " << funnel.val[7] << ", " << funnel.val[8] << ", " << funnel.val[9] << ", " << funnel.val[10] << ", " << funnel.val[11] << ", " << funnel.val[12] << ", " << funnel.val[13] << ", " << funnel.val[14] << ", " << funnel.val[15] << "}"; } //In this case it's an error if(intercrossingGenerations[finalCounter] != 0) { ss << ". Repeated founders are only allowed with zero generations of intercrossing"; errors.push_back(ss.str()); } //But if we have zero intercrossing generations then it's only a warning else { ss << ". Did you intend to use all " << nFounders << " founders?"; warnings.push_back(ss.str()); } } } allFunnels.push_back(funnel); } //remove duplicates in representedFounders std::sort(representedFounders.begin(), representedFounders.end()); representedFounders.erase(std::unique(representedFounders.begin(), representedFounders.end()), representedFounders.end()); //Try and check for inconsistent generations of selfing for(std::vector<int>::iterator i = representedFounders.begin(); i != representedFounders.end(); i++) { if(*i > nFounders) { std::stringstream ss; ss << "Error in pedigree for line number " << finalCounter << " named " << finalNames(finalCounter) << ". Inconsistent number of generations of intercrossing"; errors.push_back(ss.str()); goto nextLine; } } //Not having all the founders in the input funnels is more serious if it causes the observed marker data to be impossible. So check for this. for(int markerCounter = 0; markerCounter < nMarkers; markerCounter++) { bool okMarker = false; //If observed value is an NA then than's ok, continue int value = finals(finalCounter, markerCounter); if(value == NA_INTEGER) continue; for(std::vector<int>::iterator founderIterator1 = representedFounders.begin(); founderIterator1 != representedFounders.end(); founderIterator1++) { for(std::vector<int>::iterator founderIterator2 = representedFounders.begin(); founderIterator2 != representedFounders.end(); founderIterator2++) { //Note that founderIterator comes from representedFounders, which comes from getFunnel - Which returns values starting at 1, not 0. So we have to subtract one. if(finals(finalCounter, markerCounter) == foundersToMarkerAlleles((*founderIterator1)-1, (*founderIterator2)-1, markerCounter)) { okMarker = true; break; } } } if(!okMarker) { std::stringstream ss; ss << "Data for marker " << markerNames(markerCounter) << " is impossible for individual " << finalNames(finalCounter) << " with given pedigree"; errors.push_back(ss.str()); if(errors.size() > 1000) return; goto nextLine; } } //In this case individualsToCheckFunnels contains one element => getFunnel was only called once => we can reuse the funnel variable if(intercrossingGenerations[finalCounter] == 0) { orderFunnel(&(funnel.val[0]), nFounders); lineFunnels.push_back(funnel); } else { //Add a dummy value in lineFunnel for(int i = 0; i < 16; i++) funnel.val[i] = 0; lineFunnels.push_back(funnel); } nextLine: ; } }
// Returns an n by 1 column that represents the likelihoods of // an estimated univariate MS-AR model for each k where 1 \leq k \leq n where // beta is switching. // Note that even if beta is non-switching, setting beta as a s by M matrix with // repeated column of the original beta will give you the likelihood for // MS-AR model with non-switching beta. // TODO: Implement the version where z_dependent/z_independent exist(lagged variables should be implemented for MSM models) // [[Rcpp::export]] SEXP LikelihoodsMSMAR (Rcpp::NumericVector y_rcpp, Rcpp::NumericMatrix y_lagged_rcpp, Rcpp::NumericMatrix z_dependent_rcpp, Rcpp::NumericMatrix z_independent_rcpp, Rcpp::NumericMatrix z_dependent_lagged_rcpp, Rcpp::NumericMatrix z_independent_lagged_rcpp, Rcpp::NumericMatrix transition_probs_rcpp, Rcpp::NumericVector initial_dist_extended_rcpp, Rcpp::NumericMatrix beta_rcpp, Rcpp::NumericVector mu_rcpp, Rcpp::NumericVector sigma_rcpp, Rcpp::NumericMatrix gamma_dependent_rcpp, Rcpp::NumericVector gamma_independent_rcpp, Rcpp::IntegerMatrix state_conversion_mat_rcpp ) { int n = y_rcpp.size(); arma::colvec y(y_rcpp.begin(), y_rcpp.size(), false); arma::mat y_lagged(y_lagged_rcpp.begin(), y_lagged_rcpp.nrow(), y_lagged_rcpp.ncol(), false); arma::mat z_dependent(z_dependent_rcpp.begin(), z_dependent_rcpp.nrow(), z_dependent_rcpp.ncol(), false); arma::mat z_dependent_lagged(z_dependent_lagged_rcpp.begin(), z_dependent_lagged_rcpp.nrow(), z_dependent_lagged_rcpp.ncol(), false); arma::mat z_independent(z_independent_rcpp.begin(), z_independent_rcpp.nrow(), z_independent_rcpp.ncol(), false); arma::mat z_independent_lagged(z_independent_lagged_rcpp.begin(), z_independent_lagged_rcpp.nrow(), z_independent_lagged_rcpp.ncol(), false); arma::mat transition_probs(transition_probs_rcpp.begin(), transition_probs_rcpp.nrow(), transition_probs_rcpp.ncol(), false); arma::colvec initial_dist_extended(initial_dist_extended_rcpp.begin(), initial_dist_extended_rcpp.size(), false); arma::mat beta(beta_rcpp.begin(), beta_rcpp.nrow(), beta_rcpp.ncol(), false); arma::colvec mu(mu_rcpp.begin(), mu_rcpp.size(), false); arma::colvec sigma(sigma_rcpp.begin(), sigma_rcpp.size(), false); arma::mat gamma_dependent(gamma_dependent_rcpp.begin(), gamma_dependent_rcpp.nrow(), gamma_dependent_rcpp.ncol(), false); arma::colvec gamma_independent(gamma_independent_rcpp.begin(), gamma_independent_rcpp.size(), false); arma::imat state_conversion_mat(state_conversion_mat_rcpp.begin(), state_conversion_mat_rcpp.nrow(), state_conversion_mat_rcpp.ncol(), false); arma::mat transition_probs_extended = GetExtendedTransitionProbs( transition_probs, state_conversion_mat); arma::mat transition_probs_extended_t = transition_probs_extended.t(); arma::colvec likelihoods(n, arma::fill::zeros); int M_extended = transition_probs_extended_t.n_rows; int M = gamma_dependent.n_cols; int s = beta.n_rows; int M_extended_block = IntPower(M, s); arma::mat* xi_k_t = new arma::mat(M_extended, n, arma::fill::zeros); // make a transpose first for col operations. // partition blocks int p = gamma_dependent.n_rows; int q = gamma_independent.size(); arma::mat* z_dependent_lagged_blocks = new arma::mat[s]; arma::mat* z_independent_lagged_blocks = new arma::mat[s]; for (int i = 0; i < s; i++) { int z_dependent_block_first = i * p; int z_independent_block_first = i * q; z_dependent_lagged_blocks[i] = z_dependent_lagged.cols(z_dependent_block_first, z_dependent_block_first + p - 1); z_independent_lagged_blocks[i] = z_independent_lagged.cols(z_independent_block_first, z_independent_block_first + q - 1); } for (int k = 0; k < n; k++) { // initial setting; keep track of minimum value and its index // to divide everything by the min. value in order to prevent // possible numerical errors when computing posterior probs. int min_index = -1; double min_value = std::numeric_limits<double>::infinity(); double* ratios = new double[M_extended]; double row_sum = 0; arma::colvec xi_past; if (k > 0) xi_past = transition_probs_extended_t * exp(xi_k_t->col(k-1)); else xi_past = initial_dist_extended; xi_past /= arma::sum(xi_past); for (int j_M = 0; j_M < M; j_M++) { for (int j_extra = 0; j_extra < M_extended_block; j_extra++) { int j = j_M * M_extended_block + j_extra; arma::colvec xi_k_t_jk = y.row(k); // arma::colvec xi_k_t_jk = y.row(k) - // z_dependent.row(k) * gamma_dependent.col(j_M) - // z_independent.row(k) * gamma_independent - mu(j_M); for (int lag = 0; lag < s; lag++) { int lagged_index = state_conversion_mat.at((lag + 1), j); xi_k_t_jk -= beta.at(lag, j_M) * (y_lagged.at(k, lag) - mu(lagged_index)); xi_k_t_jk -= beta.at(lag, j_M) * (y_lagged.at(k, lag) - z_dependent_lagged_blocks[lag].row(k) * gamma_dependent.col(lagged_index) - z_independent_lagged_blocks[lag].row(k) * gamma_independent - mu(lagged_index)); } xi_k_t->at(j,k) = xi_k_t_jk(0); // explicit gluing xi_k_t->at(j,k) *= xi_k_t->at(j,k); xi_k_t->at(j,k) = xi_k_t->at(j,k) / (2 * (sigma(j_M) * sigma(j_M))); if (min_value > xi_k_t->at(j,k)) { min_value = xi_k_t->at(j,k); min_index = j; } // SQRT2PI only matters in calculation of eta; // you can remove it in the final log-likelihood. ratios[j] = xi_past(j) / sigma(j_M); } } for (int j = 0; j < M_extended; j++) { if (j == min_index) row_sum += 1.0; else row_sum += (ratios[j] / ratios[min_index]) * exp(min_value - xi_k_t->at(j,k)); xi_k_t->at(j,k) += log(ratios[j]); } likelihoods(k) = log(row_sum) - min_value + log(ratios[min_index]) - LOG2PI_OVERTWO; delete[] ratios; // clear memory } arma::exp(xi_k_t->cols(1,4)).t().print(); // clear memory for blocks delete[] z_dependent_lagged_blocks; delete[] z_independent_lagged_blocks; delete xi_k_t; return (wrap(likelihoods)); }