//' Update eigen values, vectors, and inverse matrices for irms
//'
//' @param tpm_prods array of transition probability matrix products
//' @param tpms array of transition probability matrices
//' @param pop_mat population level bookkeeping matrix
//' @param obstimes vector of observation times
//'
//' @return Updated eigenvalues, eigenvectors, and inverse matrices
// [[Rcpp::export]]
void tpmProdSeqs(Rcpp::NumericVector& tpm_prods, Rcpp::NumericVector& tpms, const Rcpp::IntegerVector obs_time_inds) {
// Get indices
int n_obs = obs_time_inds.size();
Rcpp::IntegerVector tpmDims = tpms.attr("dim");
// Ensure obs_time_inds starts at 0
Rcpp::IntegerVector obs_inds = obs_time_inds - 1;
// Set array pointers
arma::cube prod_arr(tpm_prods.begin(), tpmDims[0], tpmDims[1], tpmDims[2], false);
arma::cube tpm_arr(tpms.begin(), tpmDims[0], tpmDims[1], tpmDims[2], false);
// Generate tpm products and store them in the appropriate spots in the tpm product array
for(int j = 0; j < (n_obs - 1); ++j) {
prod_arr.slice(obs_inds[j+1] - 1) = tpm_arr.slice(obs_inds[j+1] - 1);
for(int k = (obs_inds[j+1] - 2); k > (obs_inds[j] - 1); --k) {
prod_arr.slice(k) = tpm_arr.slice(k) * prod_arr.slice(k + 1);
}
}
}
// Bellman recursion using row rearrangement
//[[Rcpp::export]]
Rcpp::List Bellman(const arma::mat& grid,
Rcpp::NumericVector reward_,
const arma::cube& scrap,
Rcpp::NumericVector control_,
const arma::cube& disturb,
const arma::vec& weight) {
// Passing R objects to C++
const std::size_t n_grid = grid.n_rows;
const std::size_t n_dim = grid.n_cols;
const arma::ivec r_dims = reward_.attr("dim");
const std::size_t n_pos = r_dims(3);
const std::size_t n_action = r_dims(2);
const std::size_t n_dec = r_dims(4) + 1;
const arma::cube
reward(reward_.begin(), n_grid, n_dim * n_action * n_pos, n_dec - 1, false);
const arma::ivec c_dims = control_.attr("dim");
arma::cube control2;
arma::imat control;
bool full_control;
if (c_dims.n_elem == 3) {
full_control = false;
arma::cube temp_control2(control_.begin(), n_pos, n_action, n_pos, false);
control2 = temp_control2;
} else {
full_control = true;
arma::mat temp_control(control_.begin(), n_pos, n_action, false);
control = arma::conv_to<arma::imat>::from(temp_control);
}
const std::size_t n_disturb = disturb.n_slices;
// Bellman recursion
arma::cube value(n_grid, n_dim * n_pos, n_dec);
arma::cube cont(n_grid, n_dim * n_pos, n_dec - 1, arma::fill::zeros);
arma::mat d_value(n_grid, n_dim);
Rcpp::Rcout << "At dec: " << n_dec - 1 << "...";
for (std::size_t pp = 0; pp < n_pos; pp++) {
value.slice(n_dec - 1).cols(n_dim * pp, n_dim * (pp + 1) - 1) =
scrap.slice(pp);
}
for (int tt = (n_dec - 2); tt >= 0; tt--) {
Rcpp::Rcout << tt;
// Approximating the continuation value
for (std::size_t pp = 0; pp < n_pos; pp++) {
cont.slice(tt).cols(n_dim * pp, n_dim * (pp + 1) - 1) =
Expected(grid,
value.slice(tt + 1).cols(pp * n_dim, n_dim * (pp + 1) - 1),
disturb, weight);
}
Rcpp::Rcout << "..";
// Optimise value function
if (full_control) {
BellmanOptimal(grid, control, value, reward, cont, tt);
} else {
BellmanOptimal2(grid, control2, value, reward, cont, tt);
}
Rcpp::Rcout << ".";
}
return Rcpp::List::create(Rcpp::Named("value") = value,
Rcpp::Named("expected") = cont);
}
// kernel Dist function on a Grid
// [[Rcpp::export]]
Rcpp::NumericVector
KdeDist(const Rcpp::NumericMatrix & X
, const Rcpp::NumericMatrix & Grid
, const double h
, const Rcpp::NumericVector & weight
, const bool printProgress
) {
const unsigned sampleNum = X.nrow();
const unsigned dimension = Grid.ncol();
const unsigned gridNum = Grid.nrow();
// first = sum K_h(X_i, X_j), second = K_h(x, x), third = sum K_h(x, X_i)
std::vector< double > firstValue;
const double second = 1.0;
std::vector< double > thirdValue;
double firstmean;
Rcpp::NumericVector kdeDistValue(gridNum);
int counter = 0, percentageFloor = 0;
int totalCount = sampleNum + gridNum;
if (printProgress) {
printProgressFrame(Rprintf);
}
firstValue = computeKernel< std::vector< double > >(
X, X, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
if (dimension <= 1) {
thirdValue = computeKernel< std::vector< double > >(
X, Grid, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
}
else {
thirdValue = computeGaussOuter< std::vector< double > >(
X, Grid, h, weight, printProgress, Rprintf, counter, totalCount,
percentageFloor);
}
if (weight.size() == 1) {
firstmean = std::accumulate(firstValue.begin(), firstValue.end(), 0.0) / sampleNum;
}
else {
firstmean = std::inner_product(
firstValue.begin(), firstValue.end(), weight.begin(), 0.0) /
std::accumulate(weight.begin(), weight.end(), 0.0);
}
for (unsigned gridIdx = 0; gridIdx < gridNum; ++gridIdx) {
kdeDistValue[gridIdx] = std::sqrt(firstmean + second - 2 * thirdValue[gridIdx]);
}
if (printProgress) {
Rprintf("\n");
}
return kdeDistValue;
}
// [[Rcpp::export(.cpp_convolve)]]
Rcpp::NumericVector cpp_convolve(Rcpp::NumericVector xa, Rcpp::NumericVector xb) {
int n_xa = xa.size(), n_xb = xb.size();
Rcpp::NumericVector xab(n_xa + n_xb - 1);
typedef Rcpp::NumericVector::iterator vec_iterator;
vec_iterator ia = xa.begin(), ib = xb.begin();
vec_iterator iab = xab.begin();
for (int i = 0; i < n_xa; i++)
for (int j = 0; j < n_xb; j++)
iab[i + j] += ia[i] * ib[j];
return xab;
}
// [[Rcpp::export]]
Rcpp::List worstcase_l1(Rcpp::NumericVector z, Rcpp::NumericVector q, double t){
// resulting probability
craam::numvec p;
// resulting objective value
double objective;
craam::numvec vz(z.begin(), z.end()), vq(q.begin(), q.end());
std::tie(p,objective) = craam::worstcase_l1(vz,vq,t);
Rcpp::List result;
result["p"] = Rcpp::NumericVector(p.cbegin(), p.cend());
result["obj"] = objective;
return result;
}
// Returns the linear predictor from the full step
Rcpp::NumericVector
densePred::gamma(const Rcpp::NumericVector& xwts,
const Rcpp::NumericVector& wtres)
throw (std::runtime_error) {
int n = d_X.nrow();
if (xwts.size() != n || wtres.size() != n)
throw
std::runtime_error("length(xwts) or length(wtres) != nrow(X)");
arma::vec a_xwts = arma::vec(xwts.begin(), n, false, true),
a_wtres = arma::vec(wtres.begin(), n, false, true);
arma::vec tmp = solve(diagmat(a_xwts) * a_X, a_wtres);
std::copy(tmp.begin(), tmp.end(), a_delta.begin());
d_coef = d_coef0 + d_delta;
return NumericVector(wrap(a_X * a_coef));
}
// [[Rcpp::export]]
Rcpp::NumericVector randomSample(Rcpp::NumericVector a, int n) {
// clone a into b to leave a alone
Rcpp::NumericVector b(n);
__gnu_cxx::random_sample(a.begin(), a.end(),
b.begin(), b.end(), randWrapper);
return b;
}
Rcpp::NumericVector
glmFamily::devResid(Rcpp::NumericVector const &mu,
Rcpp::NumericVector const &weights,
Rcpp::NumericVector const &y) const {
if (devRes.count(d_family)) {
double (*f)(double, double, double) = devRes[d_family];
int nobs = mu.size();
NumericVector ans(nobs);
double *mm = mu.begin(), *ww = weights.begin(),
*yy = y.begin(), *aa = ans.begin();
for (int i = 0; i < nobs; i++)
aa[i] = f(yy[i], mm[i], ww[i]);
return ans;
}
return d_devRes(y, mu, weights);
}
//' rcpp_neutral_hotspots_ntests
//'
//' Performs repeated neutral tests to yield average distributions of both
//' hotspot values and spatial autocorrelation statistics.
//'
//' @param nbs An \code{spdep} \code{nb} object listing all neighbours of each
//' point.
//' @param wts Weighting factors for each neighbour; must have same length as
//' nbs.
//' @param nbsi List of matrices as returned from \code{get_nbsi}. each element
//' of which contains the i-th nearest neighbour to each point.
//' @param alpha Strength of spatial autocorrelation
//' @param sd0 Standard deviation of truncated normal distribution used to model
//' environmental variation (with mean of 1)
//' @param nt Number of successive layers of temporal and spatial autocorrelation
//' used to generate final modelled values
//' @param ntests Number of tests used to obtain average values
//' @param ac_type Character string specifying type of aucorrelation
//' (\code{moran}, \code{geary}, or code{getis-ord}).
//'
//' @return A matrix of dimension (size, 2), with first column containing
//' sorted and re-scaled hotspot values, and second column containing sorted and
//' re-scaled spatial autocorrelation statistics.
//'
// [[Rcpp::export]]
Rcpp::NumericMatrix rcpp_neutral_hotspots_ntests (Rcpp::List nbs,
Rcpp::List wts, Rcpp::List nbsi, double alpha, double sd0, int niters,
std::string ac_type, bool log_scale, int ntests)
{
const int size = nbs.size ();
Rcpp::NumericMatrix hs1;
Rcpp::NumericVector z (size), z1 (size), ac (size), ac1 (size);
std::fill (ac.begin (), ac.end (), 0.0);
std::fill (z.begin (), z.end (), 0.0);
for (int n=0; n<ntests; n++)
{
hs1 = rcpp_neutral_hotspots (nbs, wts, nbsi, alpha, sd0, log_scale,
niters, ac_type);
z += hs1 (Rcpp::_, 0);
ac += hs1 (Rcpp::_, 1);
}
Rcpp::NumericMatrix result (size, 2);
result (Rcpp::_, 0) = z / (double) ntests;
result (Rcpp::_, 1) = ac / (double) ntests;
Rcpp::colnames (result) = Rcpp::CharacterVector::create ("z", "ac");
return result;
}
void eigs_sym_shift_c(
mat_op op, int n, int k, double sigma,
const spectra_opts *opts, void *data,
int *nconv, int *niter, int *nops,
double *evals, double *evecs, int *info
)
{
BEGIN_RCPP
CRealShift cmat_op(op, n, data);
Rcpp::List res;
try {
res = run_eigs_shift_sym((RealShift*) &cmat_op, n, k, opts->ncv, opts->rule,
sigma, opts->maxitr, opts->tol, opts->retvec != 0);
*info = 0;
} catch(...) {
*info = 1; // indicates error
}
*nconv = Rcpp::as<int>(res["nconv"]);
*niter = Rcpp::as<int>(res["niter"]);
*nops = Rcpp::as<int>(res["nops"]);
Rcpp::NumericVector val = res["values"];
std::copy(val.begin(), val.end(), evals);
if(opts->retvec != 0)
{
Rcpp::NumericMatrix vec = res["vectors"];
std::copy(vec.begin(), vec.end(), evecs);
}
VOID_END_RCPP
}
double lmerResp::updateMu(const Rcpp::NumericVector& gamma) {
#ifdef USE_RCPP_SUGAR
d_mu = d_offset + gamma;
#else
std::transform(gamma.begin(), gamma.end(), d_offset.begin(),
d_mu.begin(), std::plus<double>());
#endif
return updateWrss();
}
// [[Rcpp::export]]
arma::mat rmvnorm_arma(int n,
const arma::vec& mu,
const arma::mat& Sigma) {
unsigned int p = Sigma.n_cols;
Rcpp::NumericVector draw = Rcpp::rnorm(n*p);
arma::mat Z = arma::mat(draw.begin(), n, p, false, true);
arma::mat Y = arma::repmat(mu, 1, n).t()+Z * arma::chol(Sigma);
return Y;
}
// redis "set a vector" -- without R serialization, without attributes, ...
// this is somewhat experimental
std::string setVector(std::string key, Rcpp::NumericVector x) {
redisReply *reply =
static_cast<redisReply*>(redisCommand(prc_, "SET %s %b",
key.c_str(), x.begin(), x.size()*szdb));
std::string res(reply->str);
freeReplyObject(reply);
return(res);
}
void sampleScores(arma::mat& Z, arma::mat& A, arma::mat& F, Rcpp::NumericVector& sigma2inv,
int n, int k, int p) {
arma::vec s2i(sigma2inv.begin(), p, false);
arma::mat Sigma_inv = arma::diagmat(s2i);
arma::mat U = my_randn(k, n);
arma::mat fcov = arma::inv(arma::eye(k,k) + arma::trans(A)*Sigma_inv*A);
arma::mat R = arma::chol(fcov);
F = R*U + fcov*arma::trans(A)*Sigma_inv*Z;
}
// Calculate lk, k=1, ..., M with m0=0;
// where h=(h0, h1, ..., hM) with h0=0 and d=(d0, d1, ..., dM) with d0=0, dM=R_PosInf
void Getlk(Rcpp::NumericVector& lk, const Rcpp::IntegerVector& Mt, int M1, Rcpp::NumericVector d,
const Rcpp::NumericVector& t, const Rcpp::NumericVector& Xbeta){
int n = Mt.size();
std::fill(lk.begin(), lk.end(), 0);
for (int k=1; k<M1; ++k){
for (int i=0; i<n; ++i){
if(Mt[i]>=k) lk[k] += (std::min(d[k],t[i])-d[k-1])*std::exp(Xbeta[i]);
}
}
}
// gradient-descent ------------------------------------------------------------
//' @title Regular gradient descent for latent factor model with covariates
//' @param X The ratings matrix. Unobserved entries must be marked NA. Users
//' must be along rows, and tracks must be along columns.
//' @param Z The covariates associated with each pair. This must be shaped as an
//' array with users along rows, tracks along columns, and features along
//' slices.
//' @param k_factors The number of latent factors for the problem.
//' @param lambdas The regularization parameters for P, Q, and beta, respectively.
//' @param n_iter The number of gradient descent iterations to run.
//' @param batch_samples The proportion of the observed entries to sample in the
//' gradient for each factor in the gradient descent.
//' @param batch_factors The proportion of latent factors to update at each
//' iteration.
//' @param gamma The step-size in the gradient descent.
//' @param verbose Print the objective at each iteration of the descent?
//' @return A list with the following elements, \cr
//' $P The learned user latent factors. \cr
//' $Q The learned track latent factors. \cr
//' $beta The learned regression coefficients.
//' @export
// [[Rcpp::export]]
Rcpp::List svd_cov(Rcpp::NumericMatrix X, Rcpp::NumericVector Z_vec,
int k_factors, Rcpp::NumericVector lambdas, int n_iter,
double batch_samples, double batch_factors,
double gamma_pq, double gamma_beta, bool verbose) {
// convert to arma
Rcpp::IntegerVector Z_dim = Z_vec.attr("dim");
arma::cube Z(Z_vec.begin(), Z_dim[0], Z_dim[1], Z_dim[2], false);
// initialize results
int m = X.nrow();
int n = X.ncol();
int l = Z_dim[2];
arma::mat P = 1.0 / sqrt(m) * arma::randn(m, k_factors);
arma::mat Q = 1.0 / sqrt(n) * arma::randn(n, k_factors);
arma::vec beta = 1.0 / sqrt(l) * arma::randn(l);
arma::vec objs = arma::zeros(n_iter);
// perform gradient descent steps
for(int cur_iter = 0; cur_iter < n_iter; cur_iter++) {
// update user factors
arma::uvec cur_factors = get_batch_ix(m, batch_factors);
for(int i = 0; i < cur_factors.n_elem; i++) {
int cur_ix = cur_factors(i);
P.row(cur_ix) = update_factor(X.row(cur_ix),
Z(arma::span(cur_ix), arma::span(), arma::span()),
arma::conv_to<arma::vec>::from(P.row(cur_ix)), Q, beta,
batch_samples, lambdas[0], gamma_pq).t();
}
// update track factors
cur_factors = get_batch_ix(n, batch_factors);
for(int j = 0; j < cur_factors.n_elem; j++) {
int cur_ix = cur_factors(j);
Q.row(cur_ix) = update_factor(X.column(cur_ix),
Z(arma::span(), arma::span(cur_ix), arma::span()),
arma::conv_to<arma::vec>::from(Q.row(cur_ix)), P, beta,
batch_samples, lambdas[1], gamma_pq).t();
}
// update regression coefficients
beta = update_beta(Rcpp::as<arma::mat>(X), Z, P, Q, beta, lambdas[2], gamma_beta);
objs[cur_iter] = objective_fun(Rcpp::as<arma::mat>(X), Z, P, Q, beta, lambdas);
if(verbose) {
printf("iteration %d \n", cur_iter);
printf("Objective: %g \n", objs[cur_iter]);
}
}
return Rcpp::List::create(Rcpp::Named("P") = P,
Rcpp::Named("Q") = Q,
Rcpp::Named("beta") = beta,
Rcpp::Named("objs") = objs);
}
// [[Rcpp::export]]
SEXP EtaIndep (Rcpp::NumericVector y_rcpp,
Rcpp::NumericMatrix y_lagged_rcpp,
Rcpp::NumericMatrix z_dependent_rcpp,
Rcpp::NumericMatrix z_independent_rcpp,
Rcpp::NumericVector beta_rcpp,
Rcpp::NumericVector mu_rcpp,
Rcpp::NumericVector sigma_rcpp,
Rcpp::NumericMatrix gamma_dependent_rcpp,
Rcpp::NumericVector gamma_independent_rcpp)
{
int M = mu_rcpp.size();
int n = y_rcpp.size();
arma::mat eta(n, M);
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_independent(z_independent_rcpp.begin(),
z_independent_rcpp.nrow(), z_independent_rcpp.ncol(), false);
arma::colvec beta(beta_rcpp.begin(), beta_rcpp.size(), 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);
for (int j = 0; j < M; j++)
{
eta.col(j) = y - y_lagged * beta -
z_dependent * gamma_dependent.col(j) -
z_independent * gamma_independent - mu(j);
eta.col(j) = eta.col(j) % eta.col(j); // element-wise multiplication
eta.col(j) = exp(-eta.col(j) / (2 * (sigma(j) * sigma(j))));
eta.col(j) = eta.col(j) / (SQRT2PI * sigma(j));
}
return (wrap(eta));
}
// redis "prepend to list" -- without R serialization
// as above: pure vector, no attributes, ...
std::string listLPush(std::string key, Rcpp::NumericVector x) {
// uses binary protocol, see hiredis doc at github
redisReply *reply =
static_cast<redisReply*>(redisCommand(prc_, "LPUSH %s %b",
key.c_str(), x.begin(), x.size()*szdb));
//std::string res(reply->str);
std::string res = "";
freeReplyObject(reply);
return(res);
}
double glmerResp::updateMu(const Rcpp::NumericVector& gamma) {
#ifdef USE_RCPP_SUGAR
d_eta = d_offset + gamma;
#else
std::transform(d_offset.begin(), d_offset.end(), gamma.begin(),
d_eta.begin(), std::plus<double>());
#endif
NumericVector mmu = d_fam.linkInv(d_eta);
std::copy(mmu.begin(), mmu.end(), d_mu.begin());
return updateWrss();
}
/**
* Check and install new value of theta. Update Lambda.
*
* @param nt New value of theta
*/
void reModule::setTheta(const Rcpp::NumericVector& nt)
throw (std::runtime_error) {
R_len_t nth = d_lower.size(), nLind = d_Lind.size();
if (nt.size() != nth)
throw runtime_error("setTheta: size mismatch of nt and d_lower");
#ifdef USE_RCPP_SUGAR
if (any(nt < d_lower).is_true()) // check that nt is feasible
throw runtime_error("setTheta: theta not in feasible region");
#else
double *lp = d_lower.begin(), *ntp = nt.begin();
for (R_len_t i = 0; i < nth; i++)
if (ntp[i] < lp[i])
throw runtime_error("setTheta: theta not in feasible region");
#endif
copy(nt.begin(), nt.end(), d_theta.begin());
// update Lambda from theta and Lind
double *Lamx = (double*)d_Lambda.x, *th = d_theta.begin();
int *Li = d_Lind.begin();
for (R_len_t i = 0; i < nLind; i++) Lamx[i] = th[Li[i] - 1];
}
// [[Rcpp::export]]
SEXP FilterIndepOld (Rcpp::NumericVector y_rcpp,
Rcpp::NumericMatrix y_lagged_rcpp,
Rcpp::NumericMatrix z_dependent_rcpp,
Rcpp::NumericMatrix z_independent_rcpp,
Rcpp::NumericVector beta_rcpp,
Rcpp::NumericVector mu_rcpp,
Rcpp::NumericVector sigma_rcpp,
Rcpp::NumericMatrix gamma_dependent_rcpp,
Rcpp::NumericVector gamma_independent_rcpp,
Rcpp::NumericMatrix transition_probs_rcpp,
Rcpp::NumericVector initial_dist_rcpp
)
{
int n = y_rcpp.size();
int M = mu_rcpp.size();
arma::mat xi_k_t(M, n); // make a transpose first for easier column operations.
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_independent(z_independent_rcpp.begin(),
z_independent_rcpp.nrow(), z_independent_rcpp.ncol(), false);
arma::colvec beta(beta_rcpp.begin(), beta_rcpp.size(), 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::mat transition_probs(transition_probs_rcpp.begin(),
transition_probs_rcpp.nrow(), transition_probs_rcpp.ncol(), false);
arma::colvec initial_dist(initial_dist_rcpp.begin(), initial_dist_rcpp.size(), false);
double likelihood = 0;
SEXP eta_rcpp = EtaIndep(y_rcpp, y_lagged_rcpp,
z_dependent_rcpp, z_independent_rcpp,
beta_rcpp, mu_rcpp, sigma_rcpp,
gamma_dependent_rcpp, gamma_independent_rcpp);
arma::mat eta_t = (Rcpp::as<arma::mat>(eta_rcpp)).t();
xi_k_t.col(0) = eta_t.col(0) % initial_dist;
double total = sum(xi_k_t.col(0));
xi_k_t.col(0) = xi_k_t.col(0) / total;
likelihood += log(total);
for (int k = 1; k < n; k++)
{
xi_k_t.col(k) = eta_t.col(k) % (transition_probs * xi_k_t.col(k-1));
total = sum(xi_k_t.col(k));
xi_k_t.col(k) = xi_k_t.col(k) / total;
likelihood += log(total);
}
return Rcpp::List::create(Named("xi.k") = wrap(xi_k_t.t()),
Named("likelihood") = wrap(likelihood));
}
//[[Rcpp::export]]
extern "C" SEXP aggregategsSumSigma( SEXP SDs, SEXP DOFs, SEXP geneSets) {
Rcpp::NumericVector SD(SDs);
Rcpp::NumericVector DOF(DOFs);
Rcpp::List geneSet(geneSets);
//note this function assumes that each input is not NA
//calculates the sd and mindof in order , names are assigned in R
int n = SD.size();
int m = DOF.size();
int o = geneSet.size(); //we assume non-empty (reduce complexity)
arma::vec sumSigma(o); //there is a sumSigma for each geneSet
arma::vec finalDof(o);
//need to run the sapply function
for ( int i=0 ; i < o ; i++) { //running a for loop
SEXP nn = geneSet[i];
Rcpp::NumericVector index(nn);
int p = index.size();
arma::vec test(p);
//we subset before computing, and use the Rcpp index vector to avoid creating an unsigned vector as armadillo type
Rcpp::NumericVector sd = SD[index -1]; //converting to 0 based
Rcpp::NumericVector dof = DOF[index-1];
//fast copy pointer address without data cache copy armadillo variables
arma::vec asd(sd.begin(),sd.size(),false);
arma::colvec adof(dof.begin(),dof.size(),false);
test =(asd%asd)%(adof/(adof-2));
sumSigma(i) = sqrt(sum(test)); //summing the subsets
finalDof(i) = floor(min(adof));
}
return Rcpp::List::create( Rcpp::Named("SumSigma") = Rcpp::wrap(sumSigma),
Rcpp::Named("MinDof") = Rcpp::wrap(finalDof));
}
//' rcpp_trunc_ndist
//'
//' Truncated normal distribution (mean 1, respective upper and lower limits of
//' 0 and 2). Code copied directly from `github.com/mpadge/tnorm`, with the
//' readme of that repo demonstrating the speed advantages of using this rather
//' than pre-existing approaches (the R package `truncnorm`).
//'
//' @param len Number of elements to be simulated
//' @param sd Standard deviation
//'
//' @return A vector of truncated normally distributed values
//'
// [[Rcpp::export]]
Rcpp::NumericVector rcpp_trunc_ndist (int len, double sd)
{
double tempd;
// Set up truncated normal distribution
Rcpp::NumericVector eps (len);
std::vector <double> z;
z.resize (0);
while (z.size () < len)
{
eps = Rcpp::rnorm (len, 1.0, sd);
for (Rcpp::NumericVector::iterator it = eps.begin ();
it != eps.end (); ++it)
if (*it >= 0.0 && *it <= 2.0)
z.push_back (*it);
}
z.resize (len);
std::copy (z.begin (), z.end (), eps.begin ());
z.resize (0);
return eps;
}
double nlmerResp::updateMu(Rcpp::NumericVector const &gamma) throw(std::runtime_error) {
int n = d_y.size();
#ifdef USE_RCPP_SUGAR
Rcpp::NumericVector gam = gamma + d_offset;
#else
NumericVector gam(d_offset.size());
std::transform(gamma.begin(), gamma.end(), d_offset.begin(),
gam.begin(), std::plus<double>());
#endif
double *gg = gam.begin();
for (int p = 0; p < d_pnames.size(); p++) {
std::string pn(d_pnames[p]);
Rcpp::NumericVector pp = d_nlenv.get(pn);
std::copy(gg + n * p, gg + n * (p + 1), pp.begin());
}
NumericVector rr = d_nlmod.eval(SEXP(d_nlenv));
if (rr.size() != n)
throw std::runtime_error("dimension mismatch");
std::copy(rr.begin(), rr.end(), d_mu.begin());
NumericMatrix rrg = rr.attr("gradient");
std::copy(rrg.begin(), rrg.end(), d_sqrtXwt.begin());
return updateWrss();
}
// [[Rcpp::export]]
double trapzRcpp(const Rcpp::NumericVector X, const Rcpp::NumericVector Y){
if( Y.size() != X.size()){
Rcpp::stop("The input Y-grid does not have the same number of points as input X-grid.");
}
if(is_sorted(X.begin(),X.end())){
double trapzsum = 0;
for (unsigned int ind = 0; ind != X.size()-1; ++ind){
trapzsum += 0.5 * (X[ind + 1] - X[ind]) *(Y[ind] + Y[ind + 1]);
}
return trapzsum;
} else {
Rcpp::stop("The input X-grid is not sorted.");
return std::numeric_limits<double>::quiet_NaN();
}
return std::numeric_limits<double>::quiet_NaN();
}
SEXP conditionalPoissonSecondInclusion(SEXP sizes_sexp, SEXP n_sexp)
{
BEGIN_RCPP
Rcpp::NumericVector sizes = Rcpp::as<Rcpp::NumericVector>(sizes_sexp);
int n = Rcpp::as<int>(n_sexp);
conditionalPoissonArgs args;
args.weights.insert(args.weights.begin(), sizes.begin(), sizes.end());
args.n = n;
std::vector<mpfr_class> inclusionProbabilities;
args.zeroWeights.resize(sizes.size());
args.deterministicInclusion.resize(sizes.size());
args.indices.clear();
for(int i = 0; i != sizes.size(); i++)
{
args.zeroWeights[i] = args.deterministicInclusion[i] = false;
if(sizes[i] < 0 || sizes[i] > 1)
{
throw std::runtime_error("Sizes must be values in [0, 1]");
}
else if(sizes[i] == 0)
{
args.zeroWeights[i] = true;
}
else if(sizes[i] == 1)
{
args.deterministicInclusion[i] = true;
args.indices.push_back(i);
}
}
computeExponentialParameters(args);
conditionalPoissonInclusionProbabilities(args, inclusionProbabilities);
boost::numeric::ublas::matrix<mpfr_class> secondOrder(sizes.size(), sizes.size());
conditionalPoissonSecondOrderInclusionProbabilities(args, inclusionProbabilities, secondOrder);
Rcpp::NumericMatrix result(sizes.size(), sizes.size());
for(int i = 0; i < sizes.size(); i++)
{
for(int j = 0; j < sizes.size(); j++)
{
result(i, j) = secondOrder(i, j).convert_to<double>();
}
}
return result;
END_RCPP
}
SEXP conditionalPoissonInclusion(SEXP sizes_sexp, SEXP n_sexp)
{
BEGIN_RCPP
Rcpp::NumericVector sizes = Rcpp::as<Rcpp::NumericVector>(sizes_sexp);
int n = Rcpp::as<int>(n_sexp);
conditionalPoissonArgs args;
args.weights.insert(args.weights.begin(), sizes.begin(), sizes.end());
args.n = n;
std::vector<mpfr_class> inclusionProbabilities;
args.zeroWeights.resize(sizes.size());
args.deterministicInclusion.resize(sizes.size());
args.indices.clear();
for(int i = 0; i != sizes.size(); i++)
{
args.zeroWeights[i] = args.deterministicInclusion[i] = false;
if(sizes[i] < 0 || sizes[i] > 1)
{
throw std::runtime_error("Sizes must be values in [0, 1]");
}
else if(sizes[i] == 0)
{
args.zeroWeights[i] = true;
}
else if(sizes[i] == 1)
{
args.deterministicInclusion[i] = true;
args.indices.push_back(i);
}
}
computeExponentialParameters(args);
conditionalPoissonInclusionProbabilities(args, inclusionProbabilities);
std::vector<double> inclusionProbabilities_double;
std::transform(inclusionProbabilities.begin(), inclusionProbabilities.end(), std::back_inserter(inclusionProbabilities_double), [](mpfr_class x){ return x.convert_to<double>(); });
return Rcpp::wrap(inclusionProbabilities_double);
END_RCPP
}
//[[Rcpp::export]]
arma::cube PathDisturb(const arma::vec& start,
Rcpp::NumericVector disturb_) {
// R objects to C++
const arma::ivec d_dims = disturb_.attr("dim");
const std::size_t n_dim = d_dims(0);
const std::size_t n_dec = d_dims(3) + 1;
const std::size_t n_path = d_dims(2);
const arma::cube disturb(disturb_.begin(), n_dim, n_dim * n_path, n_dec - 1,
false);
// Simulating the sample paths
arma::cube path(n_path, n_dim, n_dec);
// Assign starting values
for (std::size_t ii = 0; ii < n_dim; ii++) {
path.slice(0).col(ii).fill(start(ii));
}
// Disturb the paths
for (std::size_t pp = 0; pp < n_path; pp++) {
for (std::size_t tt = 1; tt < n_dec; tt++) {
path.slice(tt).row(pp) = path.slice(tt - 1).row(pp) *
arma::trans(disturb.slice(tt - 1).cols(n_dim * pp, n_dim * (pp + 1) - 1));
}
}
return path;
}
void densePred::setCoef0(const Rcpp::NumericVector& cc)
throw (std::runtime_error) {
if (cc.size() != d_coef0.size())
throw std::runtime_error("size mismatch in setCoef0");
std::copy(cc.begin(), cc.end(), d_coef0.begin());
}
void vec_insert( vec* obj, int position, Rcpp::NumericVector data) {
vec::iterator it = obj->begin() + position;
obj->insert( it, data.begin(), data.end() );
}