// [[Rcpp::export]]
Rcpp::RawVector magick_image_write( XPtrImage input, Rcpp::CharacterVector format, Rcpp::IntegerVector quality,
Rcpp::IntegerVector depth, Rcpp::CharacterVector density, Rcpp::CharacterVector comment){
if(!input->size())
return Rcpp::RawVector(0);
XPtrImage image = copy(input);
#if MagickLibVersion >= 0x691
//suppress write warnings see #74 and #116
image->front().quiet(true);
#endif
if(format.size())
for_each ( image->begin(), image->end(), Magick::magickImage(std::string(format[0])));
if(quality.size())
for_each ( image->begin(), image->end(), Magick::qualityImage(quality[0]));
if(depth.size())
for_each ( image->begin(), image->end(), Magick::depthImage(depth[0]));
if(density.size()){
for_each ( image->begin(), image->end(), Magick::resolutionUnitsImage(Magick::PixelsPerInchResolution));
for_each ( image->begin(), image->end(), Magick::densityImage(Point(density[0])));
}
if(comment.size())
for_each ( image->begin(), image->end(), Magick::commentImage(std::string(comment.at(0))));
Magick::Blob output;
writeImages( image->begin(), image->end(), &output );
Rcpp::RawVector res(output.length());
std::memcpy(res.begin(), output.data(), output.length());
return res;
}
SEXP assignRawSymmetricMatrixDiagonal(SEXP destination_, SEXP indices_, SEXP source_)
{
BEGIN_RCPP
Rcpp::S4 destination = destination_;
Rcpp::RawVector source = source_;
Rcpp::RawVector destinationData = destination.slot("data");
Rcpp::IntegerVector indices = indices_;
if(&(source(0)) == &(destinationData(0)))
{
throw std::runtime_error("Source and destination cannot be the same in assignRawSymmetricMatrixDiagonal");
}
if((indices.size()*(indices.size()+(R_xlen_t)1))/(R_xlen_t)2 != source.size())
{
throw std::runtime_error("Mismatch between index length and source object size");
}
for(R_xlen_t column = 0; column < indices.size(); column++)
{
for(R_xlen_t row = 0; row <= column; row++)
{
R_xlen_t rowIndex = indices[row];
R_xlen_t columnIndex = indices[column];
if(rowIndex > columnIndex)
{
std::swap(rowIndex, columnIndex);
}
destinationData((columnIndex*(columnIndex-(R_xlen_t)1))/(R_xlen_t)2+rowIndex-(R_xlen_t)1) = source((column*(column+(R_xlen_t)1))/(R_xlen_t)2 + row);
}
}
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;
}
// Calculate mk = sum_i I(M(ti)=k), 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 Getmk(Rcpp::IntegerVector& mk, const Rcpp::IntegerVector& Mt){
int n = Mt.size();
std::fill(mk.begin(), mk.end(), 0);
for (int i=0; i<n; ++i){
int k = Mt[i];
mk[k] +=1;
}
}
SEXP hclustThetaMatrix(SEXP mpcrossRF_, SEXP preClusterResults_)
{
BEGIN_RCPP
Rcpp::List preClusterResults = preClusterResults_;
bool noDuplicates;
R_xlen_t preClusterMarkers = countPreClusterMarkers(preClusterResults_, noDuplicates);
if(!noDuplicates)
{
throw std::runtime_error("Duplicate marker indices in call to hclustThetaMatrix");
}
Rcpp::S4 mpcrossRF = mpcrossRF_;
Rcpp::S4 rf = mpcrossRF.slot("rf");
Rcpp::S4 theta = rf.slot("theta");
Rcpp::RawVector data = theta.slot("data");
Rcpp::NumericVector levels = theta.slot("levels");
Rcpp::CharacterVector markers = theta.slot("markers");
if(markers.size() != preClusterMarkers)
{
throw std::runtime_error("Number of markers in precluster object was inconsistent with number of markers in mpcrossRF object");
}
R_xlen_t resultDimension = preClusterResults.size();
//Allocate enough storage. This symmetric matrix stores the *LOWER* triangular part, in column-major storage. Excluding the diagonal.
Rcpp::NumericVector result(((resultDimension-(R_xlen_t)1)*resultDimension)/(R_xlen_t)2);
for(R_xlen_t column = 0; column < resultDimension; column++)
{
Rcpp::IntegerVector columnMarkers = preClusterResults(column);
for(R_xlen_t row = column + 1; row < resultDimension; row++)
{
Rcpp::IntegerVector rowMarkers = preClusterResults(row);
double total = 0;
R_xlen_t counter = 0;
for(R_xlen_t columnMarkerCounter = 0; columnMarkerCounter < columnMarkers.size(); columnMarkerCounter++)
{
R_xlen_t marker1 = columnMarkers[columnMarkerCounter]-(R_xlen_t)1;
for(R_xlen_t rowMarkerCounter = 0; rowMarkerCounter < rowMarkers.size(); rowMarkerCounter++)
{
R_xlen_t marker2 = rowMarkers[rowMarkerCounter]-(R_xlen_t)1;
R_xlen_t column = std::max(marker1, marker2);
R_xlen_t row = std::min(marker1, marker2);
Rbyte thetaDataValue = data((column*(column+(R_xlen_t)1))/(R_xlen_t)2 + row);
if(thetaDataValue != 0xFF)
{
total += levels(thetaDataValue);
counter++;
}
}
}
if(counter == 0) total = 0.5;
else total /= counter;
result(((resultDimension-(R_xlen_t)1)*resultDimension)/(R_xlen_t)2 - ((resultDimension - column)*(resultDimension-column-(R_xlen_t)1))/(R_xlen_t)2 + row-column-(R_xlen_t)1) = total;
}
}
return result;
END_RCPP
}
// [[Rcpp::export]]
Rcpp::NumericMatrix testColPost(Rcpp::NumericMatrix post, Rcpp::List m2u, int nthreads){
Rcpp::IntegerVector values = Rcpp::as<Rcpp::IntegerVector>(m2u["values"]);
Rcpp::IntegerVector map = Rcpp::as<Rcpp::IntegerVector>(m2u["map"]);
if (post.ncol() != map.length()) Rcpp::stop("posteriors doesn't match with m2u");
Rcpp::NumericMatrix smallerPost(post.nrow(), values.length());
Vec<double> foo; NMPreproc preproc(asVec(values), asVec(map), foo);
collapsePosteriors_core(asMat(smallerPost), asMat(post), preproc);
return smallerPost;
}
RcppExport SEXP GetAllPoints(SEXP x,SEXP n,SEXP c) {
try {
Rcpp::XPtr< flann::Index<flann::L2<float> > > index(x);
Rcpp::NumericVector npoints(n);
Rcpp::NumericVector cn(c);
int colNum = cn[0];
float *data = new float[colNum];
for(int i=0;i<colNum;i++) {
data[i] = 0;
i++;
}
flann::Matrix<float> dataset = flann::Matrix<float>(data,1,colNum);
delete [] data;
std::vector< std::vector<int> > indices;
std::vector< std::vector<float> > dists;
index->knnSearch(dataset,indices,dists,npoints[0],flann::SearchParams(-1));
std::sort (indices[0].begin(), indices[0].end());
Rcpp::NumericMatrix results(indices[0].size(), colNum);
Rcpp::IntegerVector rownames;
int num = indices[0].size();
//#pragma omp parallel for ordered schedule(dynamic)
for(int i=0;i<num;i++) {
float* indexPoint = index->getPoint(indices[0][i]);
for(int j=0;j<colNum;j++) {
results(i,j)=(*(indexPoint+j));
}
//#pragma omp ordered
rownames.push_back(indices[0][i]);
}
Rcpp::List dimnms = Rcpp::List::create(rownames, Rcpp::Range(1,colNum));
results.attr("dimnames") = dimnms;
return results;
} catch( std::exception &ex ) { // or use END_RCPP macro
forward_exception_to_r( ex );
} catch(...) {
::Rf_error( "c++ exception (unknown reason)" );
}
return R_NilValue; // -Wall
}
//[[Rcpp::export]]
Rcpp::IntegerVector getAllNodesFast (Rcpp::IntegerMatrix edge, bool rooted) {
Rcpp::IntegerVector tmp = Rcpp::as_vector(edge);
Rcpp::IntegerVector maxN = Rcpp::range(tmp);
Rcpp::IntegerVector ans = Rcpp::seq_len(maxN[1] + 1);
if (rooted) {
return ans - 1;
}
else {
ans.erase(0);
return ans - 1;
}
}
SEXP getAllFunnels(SEXP Rmpcross)
{
char* stackmem;
{
std::string error;
{
int nFounders;
Rcpp::RObject mpcross_ = Rmpcross;
bool valid = validateMPCross(mpcross_, nFounders, error, true, false, false);
if(!valid)
{
goto signal_error;
}
Rcpp::List mpcross = Rmpcross;
Rcpp::DataFrame pedigree(mpcross["pedigree"]);
Rcpp::IntegerVector id = mpcross["id"];
int nFinals = id.length();
std::vector<int> fid = Rcpp::as<std::vector<int> >(mpcross["fid"]);
Rcpp::IntegerMatrix output(id.length(), nFounders);
std::vector<int> nIntercrossingGenerations;
nIntercrossingGenerations.resize(nFinals, 0);
//get number of intercrossing generations
bool ok = intercrossingGenerations(pedigree, nFounders, id, nIntercrossingGenerations);
if(!ok)
{
error = "Problem determining number of intercrossing generations";
goto signal_error;
}
//now get the actual funnels from the pedigree
int funnel[16];
for(int i = 0; i < id.length(); i++)
{
ok = getFunnel(id[i], pedigree, fid, nIntercrossingGenerations[i], funnel, pedigree.nrows(), nFounders);
if(!ok)
{
std::stringstream ss;
ss << "Problem with pedigree, for individual number " << (i+1) << ", having id " << id[i];
error = ss.str();
goto signal_error;
}
for(int j = 0; j < nFounders; j++) output(i, j) = funnel[j];
}
return output;
}
signal_error:
stackmem = (char*)alloca(error.size() + 4);
memset(stackmem, 0, error.size() + 4);
memcpy(stackmem, error.c_str(), error.size());
}
Rf_error(stackmem);
return R_NilValue;
}
// [[Rcpp::export]]
Rcpp::NumericVector seqC(double from_, double to_, double by_ = 1.0) {
int adjust = std::pow(10, std::ceil(std::log10(10 / by_)) - 1);
int from = adjust * from_;
int to = adjust * to_;
int by = adjust * by_;
std::size_t n = ((to - from) / by) + 1;
Rcpp::IntegerVector res = Rcpp::rep(from, n);
add_multiple ftor(by);
std::transform(res.begin(), res.end(), res.begin(), ftor);
return Rcpp::NumericVector(res) / adjust;
}
// [[Rcpp::export]]
Rcpp::IntegerVector tabulate_cpp(const Rcpp::IntegerVector & v, R_xlen_t nlevels) {
// Using R_xlen_t to avoid checking for entries < 0
std::vector<R_xlen_t> table(nlevels);
R_xlen_t n = v.size();
for (R_xlen_t i = 0; i < n; ++i) {
table.at( v.at(i) - 1 ) ++;
}
// Wrapp may throw errors with R_xlen_t
// return wrap(table);
IntegerVector a(table.size());
std::copy(table.begin(), table.end(), a.begin());
return a;
}
// [[Rcpp::export]]
Rcpp::List subsetCounts(Rcpp::IntegerVector counts, Rcpp::IntegerVector start, Rcpp::IntegerVector width, Rcpp::LogicalVector strand){
if (start.length() != width.length() || start.length() != strand.length()) Rcpp::stop("provided vectors have different lengths...");
int nr = start.length();
int len = counts.length();
int tot = 0;
int* S = start.begin(); int* W = width.begin();
for (int i = 0; i < nr; ++i){
int s = S[i] - 1;
int w = W[i];
if (s < 0) Rcpp::stop("negative start positions are invalid");
if (s + w > len) Rcpp::stop("range exceeds the lengths of the counts vector");
tot += w;
}
Rcpp::IntegerVector res(tot);
Rcpp::IntegerVector nstart(nr);
Rcpp::IntegerVector nend(nr);
int* R = res.begin(); int* C = counts.begin(); int* ST = strand.begin();
int* NS = nstart.begin(); int* NE = nend.begin();
int currpos = 0;
for (int i = 0; i < nr; ++i){
NS[i] = currpos + 1;
int w = W[i];
if (ST[i]) std::copy(C + S[i]-1, C + S[i]-1 + w, R + currpos);
else std::reverse_copy(C + S[i]-1, C + S[i]-1 + w, R + currpos);
currpos += w;
NE[i] = currpos;
}
return List::create(_("counts")=res, _("starts")=nstart, _("ends")=nend);
}
Rcpp::IntegerVector r_classify(const treetree::tree<T>& tr,
const std::vector<std::string>& labels) {
treetree::tree<forest::node<int> > tmp = classify(tr, labels);
Rcpp::IntegerVector ret;
std::vector<std::string> names;
names.reserve(tr.size());
for (treetree::tree<forest::node<int> >::const_pre_iterator
it = tmp.begin(); it != tmp.end(); ++it) {
ret.push_back(it->data_);
names.push_back(it->label_);
}
ret.attr("names") = names;
return ret;
}
void ribi::ctm::Helper::CalcMaxLikelihood(
const std::string& newick,
Rate& birth_rate,
Rate& death_rate,
const Part part
) const
{
assert(!newick.empty());
auto& r = ribi::Rinside().Get();
r.parseEval("library(ape)");
r.parseEval("library(DDD)");
r["newick"] = newick;
r.parseEval("phylogeny <- read.tree(text = newick)");
r.parseEval("branch_lengths <- phylogeny$edge.length");
switch (part)
{
case Part::branch_lengths: r["part"] = 0; break;
case Part::phylogeny : r["part"] = 1; break;
}
r.parseEval(
"max_likelihood <- bd_ML("
" brts = branch_lengths,"
" cond = 1," // # cond == 1 : conditioning on stem or crown age and non-extinction of the phylogeny
" btorph = part"
")"
);
r.parseEval("lambda0 <- max_likelihood$lambda0");
r.parseEval("mu0 <- max_likelihood$mu0");
r.parseEval("conv <- max_likelihood$conv");
const Rcpp::DoubleVector lambda0 = r["lambda0"];
const Rcpp::DoubleVector mu0 = r["mu0"];
const Rcpp::IntegerVector conv = r["conv"];
assert(lambda0.size() == 1);
assert(mu0.size() == 1);
assert(conv.size() == 1);
const bool has_converged = conv[0] == 0;
if (!has_converged)
{
std::stringstream s;
s << __func__ << ": has not converged" ;
throw std::runtime_error(s.str());
}
birth_rate = lambda0[0] / boost::units::si::second;
death_rate = mu0[0] / boost::units::si::second;
}
Permutation::Permutation(Rcpp::IntegerVector &vv)
: d_perm(vv),
n(vv.size()) {
int *vpt = vv.begin();
std::vector<bool> chk(n);
std::fill(chk.begin(), chk.end(), false);
for (int i = 0; i < n; i++) {
int vi = vpt[i];
if (vi < 0 || n <= vi)
throw runtime_error("permutation elements must be in [0,n)");
if (chk[vi])
throw runtime_error("permutation is not a permutation");
chk[vi] = true;
}
}
static double bic_logistic(Rcpp::NumericMatrix X,
Rcpp::NumericVector y,
Rcpp::NumericMatrix beta_new,
double eps,
Rcpp::IntegerVector nk)
{
int n_tot = X.nrow();
int p = X.ncol();
int K = nk.size();
int idx = 0;
double ll = 0.0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int i = 0; i < n; i++) {
double lp = 0.0;
for (int j = 0; j < p; j++) {
lp += elem(X, idx+i, j) * elem(beta_new, j, k);
}
ll += y[idx+i] * lp - log(1.0 + exp(lp));
}
idx += n;
}
double bic = -2.0 * ll + df(beta_new, eps) * log(n_tot);
return bic;
}
//' 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);
}
}
}
static void print(Rcpp::IntegerVector a)
{
for (int i = 0; i < a.size(); i++) {
printf("%d ", a[i]);
}
putchar('\n');
}
// [[Rcpp::export]]
Rcpp::IntegerVector testSortCounts(Rcpp::IntegerVector v){
Rcpp::IntegerVector res(v.length());
Vec<int> v2 = asVec(v);
Vec<int> v3 = asVec(res);
sortCounts(v2, v3);
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 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;
}
// Calculate CPO for spatial Copula Cox PH
arma::vec LinvSpCopulaCox(Rcpp::NumericVector tobs, Rcpp::IntegerVector delta, Rcpp::NumericVector Xbeta,
Rcpp::NumericVector h, Rcpp::NumericVector d, arma::mat Cinv, arma::vec z){
int n = delta.size();
arma::vec res(n);
for(int i=0; i<n; ++i){
double Cii = Cinv(i,i);
double s2i = 1.0/Cii;
double mui = -s2i*( arma::dot(Cinv.col(i), z) - Cii*z[i] );
double si = std::sqrt(s2i);
double Fi = Foft(tobs[i], h, d, Xbeta[i]);
double PinvFyi = Rf_qnorm5(Fi, 0, 1, true, false);
double newzi = (PinvFyi-mui)/si;
if(delta[i]==0){
double St = 1.0-Rf_pnorm5(newzi, 0, 1, true, false);
//Rprintf( "St=%f\n", St );
res(i) = 1.0/St;
} else {
double fi = foft(tobs[i], h, d, Xbeta[i]);
double diff = -0.5*std::pow(newzi, 2) + 0.5*std::pow(PinvFyi, 2);
double ft = (1.0/si*std::exp(diff)*fi);
res(i) = 1.0/ft;
//Rprintf( "ft=%f\n", ft );
}
}
return(res);
}
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 double bic_linear(Rcpp::NumericMatrix X,
Rcpp::NumericVector y,
Rcpp::NumericMatrix beta_new,
double eps,
Rcpp::IntegerVector nk)
{
int n_tot = X.nrow();
int p = X.ncol();
int K = nk.size();
/*calculate SSe*/
double SSe = 0.0;
int idx = 0;
for (int k = 0; k < K; k++) {
int n = nk[k];
for (int i = 0; i < n; i++) {
double Xrow_betacol = 0.0;
for (int j = 0; j < p; j++) {
Xrow_betacol += elem(X, idx+i, j) * elem(beta_new, j, k);
}
SSe += pow(y[idx+i] - Xrow_betacol, 2);
}
idx += n;
}
double ll = -n_tot / 2.0 * (log(SSe) - log(n_tot) + log(2.0 * M_PI) + 1);
double bic = -2 * ll + df(beta_new, eps) * log(n_tot);
return bic;
}
//Function to summarize results from BeQTL and return a dataframe
// [[Rcpp::export]]
Rcpp::DataFrame SumRes(const arma::mat & cormat, const arma::mat & errmat, const Rcpp::DataFrame SnpDF, const Rcpp::DataFrame Genedf, const int samplesize, const double tcutoff){
Rcpp::Rcout<<"Generating Tmat"<<std::endl;
arma::mat tmat = sqrt(samplesize-2)*(cormat/(sqrt(1-square(cormat))));
Rcpp::Rcout<<"Finding strong t"<<std::endl;
arma::uvec goods = find(abs(tmat)>tcutoff);
Rcpp::Rcout<<"Generating matrix index"<<std::endl;
arma::umat goodmat = Ind(tmat.n_rows,goods);
Rcpp::Rcout<<"Subsetting tmat"<<std::endl;
Rcpp::Rcout<<"This many good results"<<goodmat.n_rows<<std::endl;
arma::vec tvec = tmat(goods);
Rcpp::Rcout<<"Subsetting Errmat"<<std::endl;
arma::vec errvec = errmat(goods);
Rcpp::Rcout<<"Generating SNP and Gene lists"<<std::endl;
Rcpp::IntegerVector GoodGenes = Rcpp::wrap(arma::conv_to<arma::ivec>::from(goodmat.col(0)));
Rcpp::IntegerVector GoodSNPs = Rcpp::wrap(arma::conv_to<arma::ivec>::from(goodmat.col(1)));
//Subset SNP anno
Rcpp::CharacterVector SNPnames = SnpDF["rsid"];
SNPnames = SNPnames[GoodSNPs];
arma::ivec SNPchr = Rcpp::as<arma::ivec>(SnpDF["Chrom"]);
SNPchr = SNPchr(goodmat.col(1));
arma::ivec SNPpos = Rcpp::as<arma::ivec>(SnpDF["Pos"]);
SNPpos = SNPpos(goodmat.col(1));
//Subset Geneanno
Rcpp::CharacterVector GeneNames = Genedf["Symbol"];
GeneNames = GeneNames[GoodGenes];
arma::ivec Genechr = Rcpp::as<arma::ivec>(Genedf["Chrom"]);
Genechr = Genechr(goodmat.col(0));
arma::ivec Genestart = Rcpp::as<arma::ivec>(Genedf["Start"]);
Genestart = Genestart(goodmat.col(0));
arma::ivec Genestop = Rcpp::as<arma::ivec>(Genedf["Stop"]);
Genestop = Genestop(goodmat.col(0));
arma::ivec CisDist(GoodGenes.length());
Rcpp::Rcout<<"Calculating Cisdist"<<std::endl;
CisDist = arma::min(arma::join_cols(abs(Genestop-SNPpos),abs(Genestart-SNPpos)),1);
Rcpp::Rcout<<"CisDist Calculated"<<std::endl;
CisDist.elem(find(Genechr!=SNPchr)).fill(-1);
return Rcpp::DataFrame::create(Rcpp::Named("SNP")=SNPnames,
Rcpp::Named("Gene")=GeneNames,
Rcpp::Named("t-stat")=Rcpp::wrap(tvec),
Rcpp::Named("err")=Rcpp::wrap(errvec),
Rcpp::Named("CisDist")=Rcpp::wrap(CisDist));
}
void check_topset(const Rcpp::IntegerVector& top) {
for (size_t t=1; t<top.size(); ++t) {
if (top[t] < top[t-1]) {
throw std::runtime_error("numbers of top genes must be sorted");
}
}
return;
}
Rcpp::IntegerVector match_and_substract(Rcpp::IntegerVector x, Rcpp::IntegerVector yInt) {
int y(1);
y = yInt[0];
for (unsigned k=0; k < x.size(); ++k) {
if (x[k] > y)
x[k] = x[k] - 1;
}
return x;
}
R_xlen_t countPreClusterMarkers(SEXP preClusterResults_, bool& noDuplicates)
{
Rcpp::List preClusterResults = preClusterResults_;
std::vector<int> markers;
for(Rcpp::List::iterator i = preClusterResults.begin(); i != preClusterResults.end(); i++)
{
Rcpp::IntegerVector Rmarkers = *i;
for(Rcpp::IntegerVector::iterator j = Rmarkers.begin(); j != Rmarkers.end(); j++)
{
markers.push_back(*j);
}
}
R_xlen_t nMarkers1 = markers.size();
std::sort(markers.begin(), markers.end());
std::vector<int>::iterator lastUnique = std::unique(markers.begin(), markers.end());
R_xlen_t nMarkers2 = std::distance(markers.begin(), lastUnique);
noDuplicates = nMarkers1 == nMarkers2;
return nMarkers1;
}
// 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]);
}
}
}
