//[[Rcpp::export]]
extern "C" SEXP notbayesEstimation(SEXP Xs, SEXP Ys, SEXP Zs, SEXP Vs) {
Rcpp::NumericVector Xr(Xs);
Rcpp::NumericMatrix Yr(Ys);
Rcpp::NumericMatrix Zr(Zs);
Rcpp::NumericMatrix Vr(Vs);
int n = Yr.nrow(), k = Yr.ncol();
int l = Vr.nrow(), m = Vr.ncol();
arma::mat x(Xr.begin(),n,k,false); //fit2$sigma
arma::mat y(Yr.begin(),n,k,false); //fit2b$stdev.unscaled
arma::mat z(Zr.begin(),1,1,false); //min.variance.factor
arma::mat v(Vr.begin(),l,m,false); //fit2$df.residual
arma::mat sda(n,1);
arma::vec dof(n);
arma::vec sd(n);
sd = sqrt( (y%x)%(y%x) + as_scalar(z));
sda = sd/(x%y);
dof = v;
Rcpp::NumericVector Sd = Rcpp::wrap(sd);
Rcpp::NumericVector Sda = Rcpp::wrap(sda);
Rcpp::NumericVector DOF = Rcpp::wrap(dof);
Sd.names() = Rcpp::List(Yr.attr("dimnames"))[0];
Sda.names() = Rcpp::List(Yr.attr("dimnames"))[0];
return Rcpp::List::create( Rcpp::Named("SD") = Sd,
Rcpp::Named("DOF") = DOF,
Rcpp::Named("sd.alpha") = Sda);
}
// [[Rcpp::export]]
Rcpp::NumericMatrix infoContentMethod_cpp(
Rcpp::StringVector& id1_,
Rcpp::StringVector& id2_,
Rcpp::List& anc_,
Rcpp::NumericVector& ic_,
const std::string& method_,
const std::string& ont_
) {
go_dist_func_t* go_dist;
// Resnik does not consider how distant the terms are from their common ancestor.
// Lin and Jiang take that distance into account.
if (method_ == "Resnik") {
go_dist = &go_dist_Resnik;
}
else if (method_ == "Lin") {
go_dist = &go_dist_Lin;
}
else if (method_ == "Jiang") {
go_dist = &go_dist_Jiang;
}
else if (method_ == "Rel") {
go_dist = &go_dist_Rel;
}
else {
throw std::runtime_error( "Unknown GO distance method" );
}
typedef std::string term_id_t;
typedef std::set<term_id_t> term_set_t;
// calculate the maximum IC and build the map of normalized IC
typedef std::map<term_id_t, double> ic_map_t;
ic_map_t normIcMap;
// more specific term, larger IC value.
// Normalized, all divide the most informative IC.
// all IC values range from 0(root node) to 1(most specific node)
double mic = NA_REAL;
{
Rcpp::StringVector icNames( ic_.names() );
for (std::size_t i=0; i < ic_.size(); i++ ) {
const double cic = ic_[i];
if ( Rcpp::NumericVector::is_na( cic ) || cic == R_PosInf ) continue;
if ( Rcpp::NumericVector::is_na( mic ) || mic < cic ) mic = cic;
}
LOG_DEBUG( "mic=" << mic );
for (std::size_t i=0; i < ic_.size(); i++ ) {
const double cic = ic_[i];
if ( Rcpp::NumericVector::is_na( cic ) || cic == R_PosInf ) continue;
normIcMap.insert( std::make_pair( (std::string) icNames[i], cic / mic ) );
}
}
// set root node IC to 0
if(ont_ == "DO") {
normIcMap["DOID:4"] = 0;
} else {
normIcMap["all"] = 0;
}
// convert anc_ into map of sets
typedef std::map<term_id_t, term_set_t> anc_map_t;
anc_map_t ancMap;
{
Rcpp::StringVector goTerms( anc_.names() );
for (std::size_t i=0; i < anc_.size(); i++ ) {
const std::vector<std::string> ancVec = Rcpp::as<std::vector<std::string> >( anc_[i] );
term_set_t ancestors( ancVec.begin(), ancVec.end() );
// term itself is also considered an ancestor
ancestors.insert( (std::string)goTerms[i] );
ancMap.insert( std::make_pair( (std::string) goTerms[i], ancestors ) );
}
}
Rcpp::NumericMatrix res( id1_.size(), id2_.size() );
res.attr("dimnames") = Rcpp::Rcpp_list2( id1_, id2_ );
for ( std::size_t i = 0; i < id1_.size(); i++ ) {
const std::string id1_term = (std::string)id1_[i];
const ic_map_t::const_iterator iIcIt = normIcMap.find( id1_term );
if ( iIcIt != normIcMap.end() && iIcIt->second != 0 ) {
const double iIc = iIcIt->second;
LOG_DEBUG( "ic[" << id1_term << "]=" << iIc );
const anc_map_t::const_iterator iAncsIt = ancMap.find( id1_term );
for ( std::size_t j = 0; j < id2_.size(); j++ ) {
const std::string id2_term = (std::string)id2_[j];
const ic_map_t::const_iterator jIcIt = normIcMap.find( id2_term );
if ( jIcIt != normIcMap.end() && jIcIt->second != 0 ) {
const anc_map_t::const_iterator jAncsIt = ancMap.find( id2_term );
// find common ancestors
term_set_t commonAncs;
if ( iAncsIt != ancMap.end() && jAncsIt != ancMap.end() ) {
std::set_intersection( iAncsIt->second.begin(), iAncsIt->second.end(),
jAncsIt->second.begin(), jAncsIt->second.end(),
std::inserter( commonAncs, commonAncs.end() ) );
}
LOG_DEBUG( "n(commonAncs(" << id1_term << "," << id2_term << "))=" << commonAncs.size() );
// Information Content of the most informative common ancestor (MICA)
double mica = 0;
for ( term_set_t::const_iterator termIt = commonAncs.begin(); termIt != commonAncs.end(); ++termIt ) {
ic_map_t::const_iterator ancIcIt = normIcMap.find( *termIt );
if ( ancIcIt != normIcMap.end() && mica < ancIcIt->second ) mica = ancIcIt->second;
}
LOG_DEBUG( "mica(" << id1_term << "," << id2_term << ")=" << mica );
res(i,j) = go_dist( mica, iIc, jIcIt->second, mic );
} else {
res(i,j) = NA_REAL;
}
}
} else {
for ( std::size_t j = 0; j < id2_.size(); j++ ) {
res(i,j) = NA_REAL;
}
}
}
return ( res );
}
///' Calculate the network properties, data matrix not provided
///'
///' @details
///' \subsection{Input expectations:}{
///' Note that this function expects all inputs to be sensible, as checked by
///' the R function 'checkUserInput' and processed by 'networkProperties'.
///'
///' These requirements are:
///' \itemize{
///' \item{'net' is a square matrix, and its rownames are identical to its
///' column names.}
///' \item{'moduleAssigments' is a named character vector, where the names
///' represent node labels found in the discovery dataset. Unlike
///' 'PermutationProcedure', these may include nodes that are not
///' present in 'data' and 'net'.}
///' \item{The module labels specified in 'modules' must occur in
///' 'moduleAssignments'.}
///' }
///' }
///'
///' @param net adjacency matrix of network edge weights between all pairs of
///' nodes in the dataset in which to calculate the network properties.
///' @param moduleAssignments a named character vector containing the module
///' each node belongs to in the discovery dataset.
///' @param modules a character vector of modules for which to calculate the
///' network properties for.
///'
///' @return a list containing the summary profile, node contribution, module
///' coherence, weighted degree, and average edge weight for each 'module'.
///'
///' @keywords internal
// [[Rcpp::export]]
Rcpp::List NetPropsNoData (
Rcpp::NumericMatrix net,
Rcpp::CharacterVector moduleAssignments,
Rcpp::CharacterVector modules
) {
// convert the colnames / rownames to C++ equivalents
const std::vector<std::string> nodeNames (Rcpp::as<std::vector<std::string>>(colnames(net)));
unsigned int nNodes = net.ncol();
R_CheckUserInterrupt();
/* Next, we need to create two mappings:
* - From node IDs to indices in the dataset of interest
* - From modules to node IDs
* - From modules to only node IDs present in the dataset of interest
*/
const namemap nodeIdxMap = MakeIdxMap(nodeNames);
const stringmap modNodeMap = MakeModMap(moduleAssignments);
const stringmap modNodePresentMap = MakeModMap(moduleAssignments, nodeIdxMap);
// What modules do we actually want to analyse?
const std::vector<std::string> mods (Rcpp::as<std::vector<std::string>>(modules));
R_CheckUserInterrupt();
// Calculate the network properties for each module
std::string mod; // iterators
unsigned int mNodesPresent, mNodes;
arma::uvec nodeIdx, propIdx, nodeRank;
namemap propIdxMap;
std::vector<std::string> modNodeNames;
arma::vec WD; // results containers
double avgWeight;
Rcpp::NumericVector degree; // for casting to R equivalents
Rcpp::List results; // final storage container
for (auto mi = mods.begin(); mi != mods.end(); ++mi) {
// What nodes are in this module?
// modNodeNames = names(moduleAssignments[moduleAssignments == mod])
mod = *mi;
modNodeNames = GetModNodeNames(mod, modNodeMap);
// initialise results containers with NA values for nodes not present in
// the dataset we're calculating the network properties in.
degree = Rcpp::NumericVector(modNodeNames.size(), NA_REAL);
avgWeight = NA_REAL;
degree.names() = modNodeNames;
// Create a mapping between node names and the result vectors
propIdxMap = MakeIdxMap(modNodeNames);
// Get just the indices of nodes that are present in the requested dataset
nodeIdx = GetNodeIdx(mod, modNodePresentMap, nodeIdxMap);
mNodesPresent = nodeIdx.n_elem;
// And a mapping of those nodes to the initialised vectors
propIdx = GetNodeIdx(mod, modNodePresentMap, propIdxMap);
mNodes = propIdx.n_elem;
// Calculate the properties if the module has nodes in the test dataset
if (nodeIdx.n_elem > 0) {
// sort the node indices for sequential memory access
nodeRank = SortNodes(nodeIdx.memptr(), mNodesPresent);
WD = WeightedDegree(net.begin(), nNodes, nodeIdx.memptr(), mNodesPresent);
WD = WD(nodeRank); // reorder results
avgWeight = AverageEdgeWeight(WD.memptr(), WD.n_elem);
R_CheckUserInterrupt();
// Fill the results vectors appropriately
Fill(degree, WD.memptr(), mNodesPresent, propIdx.memptr(), mNodes);
}
results.push_back(
Rcpp::List::create(
Rcpp::Named("degree") = degree,
Rcpp::Named("avgWeight") = avgWeight
)
);
}
results.names() = mods;
return(results);
}
//[[Rcpp::export]]
Rcpp::List checkTreeCpp(Rcpp::S4 obj, Rcpp::List opts) {
std::string err, wrn;
Rcpp::IntegerMatrix ed = obj.slot("edge");
int nrow = ed.nrow();
Rcpp::IntegerVector ances = getAnces(ed);
//Rcpp::IntegerVector desc = getDesc(ed);
int nroots = nRoots(ances);
bool rooted = nroots > 0;
Rcpp::NumericVector edLength = obj.slot("edge.length");
Rcpp::CharacterVector edLengthNm = edLength.names();
Rcpp::CharacterVector label = obj.slot("label");
Rcpp::CharacterVector labelNm = label.names();
Rcpp::CharacterVector edLabel = obj.slot("edge.label");
Rcpp::CharacterVector edLabelNm = edLabel.names();
Rcpp::IntegerVector allnodesSafe = getAllNodesSafe(ed);
Rcpp::IntegerVector allnodesFast = getAllNodesFast(ed, rooted);
int nEdLength = edLength.size();
int nLabel = label.size();
int nEdLabel = edLabel.size();
int nEdges = nrow;
bool hasEdgeLength = !all_naC(edLength);
// check tips
int ntipsSafe = nTipsSafe(ances);
int ntipsFast = nTipsFastCpp(ances);
bool testnTips = ntipsFast == ntipsSafe;
if (! testnTips) {
err.append("Tips incorrectly labeled. ");
}
//check internal nodes
bool testNodes = Rcpp::all(allnodesSafe == allnodesFast).is_true() && // is both ways comparison needed?
Rcpp::all(allnodesFast == allnodesSafe).is_true();
if (! testNodes) {
err.append("Nodes incorrectly labeled. ");
}
// check edge lengths
if (hasEdgeLength) {
if (nEdLength != nEdges) {
err.append("Number of edge lengths do not match number of edges. ");
}
// if (nb_naC(edLength) > nroots) { // not enough! -- best done in R
// err.append("Only the root should have NA as an edge length. ");
// }
if (getRange(edLength, TRUE)[0] < 0) {
err.append("Edge lengths must be non-negative. ");
}
Rcpp::CharacterVector edgeLblSupp = edgeIdCpp(ed, "all");
Rcpp::CharacterVector edgeLblDiff = Rcpp::setdiff(edLengthNm, edgeLblSupp);
if ( edgeLblDiff.size() != 0 ) {
err.append("Edge lengths incorrectly labeled. ");
}
}
// check label names
Rcpp::CharacterVector chrLabelNm = Rcpp::as<Rcpp::CharacterVector>(allnodesFast);
int j = 0;
while (j < nroots) { //remove root(s)
chrLabelNm.erase(0);
j++;
}
bool testLabelNm = isLabelName(labelNm, chrLabelNm);
if (!testLabelNm) {
err.append("Tip and node labels must be a named vector, the names must match the node IDs. ");
err.append("Use tipLabels<- and/or nodeLabels<- to update them. ");
}
// check that tips have labels
Rcpp::CharacterVector tiplabel(ntipsFast);
std::copy (label.begin(), label.begin()+ntipsFast, tiplabel.begin());
bool emptyTipLabel = is_true(any(Rcpp::is_na(tiplabel)));
if ( emptyTipLabel ) {
err.append("All tips must have a label.");
}
// check edgeLabels
Rcpp::CharacterVector chrEdgeLblNm = edgeIdCpp(ed, "all");
bool testEdgeLblNm = isLabelName(edLabelNm, chrEdgeLblNm);
if (!testEdgeLblNm) {
err.append("Edge labels are not labelled correctly. Use the function edgeLabels<- to update them. ");
}
// make sure that tips and node labels are unique
if (hasDuplicatedLabelsCpp(label)) {
std::string labOpt = opts["allow.duplicated.labels"];
if (labOpt == "fail") {
err.append("Labels are not unique. ");
}
if (labOpt == "warn") {
wrn.append("Labels are not unique. ");
}
}
// check for polytomies
if (hasPolytomy(ances)) {
std::string msgPoly = "Tree includes polytomies. ";
std::string polyOpt = opts["poly"];
if (polyOpt == "fail") {
err.append(msgPoly);
}
if (polyOpt == "warn") {
wrn.append(msgPoly);
}
}
// check number of roots
if (nroots > 1) {
std::string msgRoot = "Tree has more than one root. ";
std::string rootOpt = opts["multiroot"];
if (rootOpt == "fail") {
err.append(msgRoot);
}
if (rootOpt == "warn") {
wrn.append(msgRoot);
}
}
// check for singletons
if (hasSingleton(ances)) {
std::string msgSing = "Tree contains singleton nodes. ";
std::string singOpt = opts["singleton"];
if (singOpt == "fail") {
err.append(msgSing);
}
if (singOpt == "warn") {
wrn.append(msgSing);
}
}
return Rcpp::List::create(err, wrn);
}
