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minor version bump
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@MikeDMorgan
MikeDMorgan committed Jan 15, 2024
2 parents 745815f + a214f0c commit e1f4c81
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Showing 3 changed files with 4 additions and 137 deletions.
3 changes: 0 additions & 3 deletions .github/workflows/RCMD_check.yml
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Expand Up @@ -26,11 +26,8 @@ jobs:
matrix:
config:
- {os: windows-latest, r: 'release'}
# - {os: windows-latest, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__windows__/focal/latest"}
- {os: macOS-latest, r: 'release'}
# - {os: macOS-latest, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__macos__/focal/latest"}
- {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
# - {os: ubuntu-20.04, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}

env:
R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
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2 changes: 1 addition & 1 deletion DESCRIPTION
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@@ -1,7 +1,7 @@
Package: miloR
Type: Package
Title: Differential neighbourhood abundance testing on a graph
Version: 1.99.10
Version: 1.99.11
Authors@R:
c(person("Mike", "Morgan", role=c("aut", "cre"), email="[email protected]",
comment=c(ORCID="0000-0003-0757-0711")),
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136 changes: 3 additions & 133 deletions src/paramEst.cpp
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Expand Up @@ -145,26 +145,6 @@ arma::mat coeffMatrix(const arma::mat& X, const arma::mat& Winv, const arma::mat
int c = Z.n_cols;
int m = X.n_cols;

// arma::sp_mat ul(m, m);
// arma::sp_mat ur(m, c);
// arma::sp_mat ll(c, m);
// arma::sp_mat lr(c, c);
//
// arma::sp_mat lhs_top(m, m+c);
// arma::sp_mat lhs_bot(c, m+c);
//
// arma::sp_mat res(m+c, m+c);
//
// arma::sp_mat sX(X);
// arma::sp_mat sZ(Z);
// arma::sp_mat sW(Winv);
// arma::sp_mat sG(Ginv);
//
// ul = sX.t() * sW * sX;
// ur = sX.t() * sW * sZ;
// ll = sZ.t() * sW * sX;
// lr = (sZ.t() * sW * sZ) + sG;

arma::mat ul(m, m);
arma::mat ur(m, c);
arma::mat ll(c, m);
Expand Down Expand Up @@ -259,114 +239,6 @@ arma::mat computeZstar(const arma::mat& Z, const arma::vec& curr_sigma, const Rc
}


// arma::vec solveEquationsPCG (const int& c, const int& m, const arma::mat& Winv, const arma::mat& Zt, const arma::mat& Xt,
// arma::mat coeffmat, arma::vec curr_theta, const arma::vec& ystar, const double& conv_tol){
// // solve the mixed model equations with a preconditioned conjugate gradient
// // A = coeffmat
// // x = theta
// // b = rhs
//
// arma::vec rhs_beta(m);
// arma::vec rhs_u(c);
// arma::mat rhs(m+c, 1);
//
// arma::vec theta_up(m+c, arma::fill::zeros);
//
// rhs_beta.col(0) = Xt * Winv * ystar;
// rhs_u.col(0) = Zt * Winv * ystar;
//
// rhs = arma::join_cols(rhs_beta, rhs_u);
//
// // I'll assume any preconditioning has already been applied
// // need a check for singular hessian here
// try{
// double _rcond = arma::rcond(coeffmat);
// bool is_singular;
// is_singular = _rcond < 1e-9;
//
// // check for singular condition
// if(is_singular){
// Rcpp::stop("Coefficients Hessian is computationally singular");
// }
//
// // can we just use solve here instead?
// // if the coefficient matrix is singular then do we resort to pinv?
// // is it worth doing a quick analysis of the eigen values of coeff?
// // the _rcond might be sufficient to tell us if the matrix is ill-conditioned
// // do we need to know a priori if we have a few large eigenvalues??
// // maybe this could be tweaked by setting the convergence criterai to > 0?
// theta_up = conjugateGradient(coeffmat, curr_theta, rhs, conv_tol);
// // theta_up = arma::solve(coeffmat, rhs);
//
// } catch(std::exception &ex){
// forward_exception_to_r(ex);
// } catch(...){
// Rf_error("c++ exception (unknown reason)");
// }
//
// return theta_up;
// }


// arma::vec conjugateGradient(arma::mat A, arma::vec x, arma::vec b, double conv_tol){
// // use conjugate gradients to solve the system of linear equations
// // Ax = b
// // Algorithm:
// // r_0 <- Ax_0 - b, p_0 <- -r_0, k <- 0
// // while r_k != 0:
// // alpha_k <- (rk^T * rk)/(pk^T * A * pk)
// // x_k+1 <- xk + alpha_k * pK
// // r_k+1 <- rk + alpha_k * A * pK
// // beta_k+1 <- r rk+1 + beta_k+1 * pk
// // pk+1 <- -r_k+1 + beta_k+1 * pk
// // k++
//
// // need to generate x_0 from the current estimates: [beta u]
// const unsigned int m = A.n_cols;
// const unsigned int n = b.size();
//
// arma::dcolvec xk(m);
// xk = x; // use current estimates as x0
// arma::vec xk_update(m);
// xk_update = arma::dcolvec(m);
// // x0.randu(); // initial x values
//
// double alpha_k = 0.0;
// double beta_k = 0.0;
//
// arma::dcolvec rk(n);
// arma::dcolvec rk_update(n);
// arma::dcolvec pk(m);
// arma::dcolvec pk_update(m);
//
// rk = (A * xk) - b;
// pk = -rk;
// unsigned int k = 0;
//
// Rcpp::LogicalVector _check_rzero = check_tol_arma_numeric(rk, conv_tol);
// bool _all_rk_zero = Rcpp::all(_check_rzero).is_false(); // .is_false() required for proper type casting to bool
//
// while(_all_rk_zero){ // evaluates true until all rk are zero
// alpha_k = (rk.t() * rk).eval()(0,0)/(pk.t() * A * pk).eval()(0, 0); // needed to convert vector inner product to scalar
// xk_update = xk + alpha_k * pk;
// rk_update = rk + alpha_k * A * pk;
// beta_k = (rk_update.t() * rk_update).eval()(0, 0)/(rk.t() * rk).eval()(0, 0); // needed to convert vector inner product to scalar
// pk_update = -rk_update + beta_k * pk;
//
// rk = rk_update;
// pk = pk_update;
// xk = xk_update;
//
// _check_rzero = check_tol_arma_numeric(rk, conv_tol);
// _all_rk_zero = Rcpp::all(_check_rzero).is_false(); // .is_false() required for proper type casting to bool
// k++;
// }
//
// Rprintf("CG completed in %u iterations\n", k);
// return xk_update;
// }


arma::vec estHasemanElstonGenetic(const arma::mat& Z, const arma::mat& PREML,
const Rcpp::List& u_indices, const arma::vec& ystar, const arma::mat& Kin){
// use HasemanElston regression to estimate variance components
Expand Down Expand Up @@ -410,15 +282,13 @@ arma::vec estHasemanElston(const arma::mat& Z, const arma::mat& PREML, const Rcp

// sparsify just the multiplication steps.
// arma::mat Ycovar(n, n);
arma::sp_mat sYcovar(n, n);
arma::sp_mat sP(PREML);
arma::sp_mat YT(ystar * ystar.t());
arma::mat Ycovar(n, n);
arma::mat YT(ystar * ystar.t());

sYcovar = sP * YT * sP; // project onto REML P matrix
Ycovar = PREML * YT * PREML; // project onto REML P matrix
// Ycovar = PREML * (ystar * ystar.t()) * PREML; // project onto REML P matrix

// select the upper triangular elements, including the diagonal
arma::mat Ycovar(sYcovar);
arma::uvec upper_indices = trimatu_ind(arma::size(Ycovar));
arma::vec Ybig = Ycovar(upper_indices);

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