bayesm/0000755000176000001440000000000012541204511011555 5ustar ripleyusersbayesm/inst/0000755000176000001440000000000012535644766012561 5ustar ripleyusersbayesm/inst/include/0000755000176000001440000000000012537615432014172 5ustar ripleyusersbayesm/inst/include/bayesm.h0000644000176000001440000001175712524675105015635 0ustar ripleyusers#ifndef __BAYESM_H__ #define __BAYESM_H__ #include #include #include #include using namespace arma; using namespace Rcpp; //CUSTOM STRUCTS-------------------------------------------------------------------------------------------------- //Used in rhierLinearMixture, rhierLinearModel, rhierMnlDP, rhierMnlRwMixture, rhierNegbinRw, and rsurGibbs struct moments{ vec y; mat X; mat XpX; vec Xpy; mat hess; }; //Used in rhierLinearMixture, rhierLinearModel, rhierMnlRWMixture, and utilityFunctions.cpp struct unireg{ vec beta; double sigmasq; }; //Used in rhierMnlDP, rhierMnlRwMixture, and utilityFunctions.cpp struct mnlMetropOnceOut{ vec betadraw; int stay; double oldll; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct lambda{ vec mubar; double Amu; double nu; mat V; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct priorAlpha{ double power; double alphamin; double alphamax; int n; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct murooti{ vec mu; mat rooti; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct thetaStarIndex{ ivec indic; std::vector thetaStar_vector; }; //Used in rhierMnlDP, rivDP struct DPOut{ ivec indic; std::vector thetaStar_vector; std::vector thetaNp1_vector; double alpha; int Istar; lambda lambda_struct; }; //EXPOSED FUNCTIONS----------------------------------------------------------------------------------------------- List rwishart(int const& nu, mat const& V); List rmultireg(mat const& Y, mat const& X, mat const& Bbar, mat const& A, int nu, mat const& V); vec rdirichlet(vec const& alpha); double llmnl(vec const& beta, vec const& y, mat const& X); mat lndIChisq(double nu, double ssq, mat const& X); double lndMvst(vec const& x, int nu, vec const& mu, mat const& rooti, bool NORMC); double lndMvn(vec const& x, vec const& mu, mat const& rooti); double lndIWishart(double nu, mat const& V, mat const& IW); vec rmvst(int nu, vec const& mu, mat const& root); vec breg(vec const& y, mat const& X, vec const& betabar, mat const& A); vec cgetC(double e, int k); List rmixGibbs( mat const& y, mat const& Bbar, mat const& A, int nu, mat const& V, vec const& a, vec const& p, vec const& z); //rmixGibbs contains the following support functions, which are called ONLY THROUGH rmixGibbs: drawCompsFromLabels, drawLabelsFromComps, and drawPFromLabels //SUPPORT FUNCTIONS (contained in utilityFunctions.cpp)----------------------------------------------------------- //Used in rmvpGibbs and rmnpGibbs vec condmom(vec const& x, vec const& mu, mat const& sigmai, int p, int j); double rtrun1(double mu, double sigma,double trunpt, int above); //Used in rhierLinearModel, rhierLinearMixture and rhierMnlRWMixture mat drawDelta(mat const& x,mat const& y,vec const& z,List const& comps,vec const& deltabar,mat const& Ad); unireg runiregG(vec const& y, mat const& X, mat const& XpX, vec const& Xpy, double sigmasq, mat const& A, vec const& Abetabar, int nu, double ssq); //Used in rnegbinRW and rhierNegbinRw double llnegbin(vec const& y, vec const& lambda, double alpha, bool constant); double lpostbeta(double alpha, vec const& beta, mat const& X, vec const& y, vec const& betabar, mat const& rootA); double lpostalpha(double alpha, vec const& beta, mat const& X, vec const& y, double a, double b); //Used in rbprobitGibbs and rordprobitGibbs vec breg1(mat const& root, mat const& X, vec const& y, vec const& Abetabar); vec rtrunVec(vec const& mu,vec const& sigma, vec const& a, vec const& b); //Used in rhierMnlDP and rhierMnlRwMixture mnlMetropOnceOut mnlMetropOnce(vec const& y, mat const& X, vec const& oldbeta, double oldll,double s, mat const& incroot, vec const& betabar, mat const& rootpi); //Used in rDPGibbs, rhierMnlDP, rivDP int rmultinomF(vec const& p); mat yden(std::vector const& thetaStar, mat const& y); ivec numcomp(ivec const& indic, int k); murooti thetaD(mat const& y, lambda const& lambda_struct); thetaStarIndex thetaStarDraw(ivec indic, std::vector thetaStar_vector, mat const& y, mat ydenmat, vec const& q0v, double alpha, lambda const& lambda_struct, int maxuniq); vec q0(mat const& y, lambda const& lambda_struct); vec seq_rcpp(double from, double to, int len); //kept _rcpp due to conflict with base seq function double alphaD(priorAlpha const& priorAlpha_struct, int Istar, int gridsize); murooti GD(lambda const& lambda_struct); lambda lambdaD(lambda const& lambda_struct, std::vector const& thetaStar_vector, vec const& alim, vec const& nulim, vec const& vlim, int gridsize); //FUNCTION TIMING (contained in functionTiming.cpp)--------------------------------------------------------------- void startMcmcTimer(); void infoMcmcTimer(int rep, int R); void endMcmcTimer(); #endif bayesm/src/0000755000176000001440000000000012541203643012351 5ustar ripleyusersbayesm/src/rmixGibbs_rcpp.cpp0000644000176000001440000001302312541203644016027 0ustar ripleyusers#include "bayesm.h" //W. Taylor: we considered moving the output to struct formats but the efficiency // gains were limited and the conversions back and forth between Lists and struct were cumbersome List drawCompsFromLabels(mat const& y, mat const& Bbar, mat const& A, int nu, mat const& V, int ncomp, vec const& z){ // Wayne Taylor 3/18/2015 // Function to draw the components based on the z labels vec b, r, mu; mat yk, Xk, Ck, sigma, rooti, S, IW, CI; List temp, rw, comps(ncomp); int n = z.n_rows; vec nobincomp = zeros(ncomp); //Determine the number of observations in each component for(int i = 0; i 0) { // If there are observations in this component, draw from the posterior yk = y.rows(find(z==(k+1))); //Note k starts at 0 and z starts at 1 Xk = ones(nobincomp[k], 1); temp = rmultireg(yk, Xk, Bbar, A, nu, V); sigma = as(temp["Sigma"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> rooti = solve(trimatu(chol(sigma)),eye(sigma.n_rows,sigma.n_cols)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mu = as(temp["B"]); comps(k) = List::create( Named("mu") = NumericVector(mu.begin(),mu.end()), //converts to a NumericVector, otherwise it will be interpretted as a matrix Named("rooti") = rooti ); } else { // If there are no obervations in this component, draw from the prior S = solve(trimatu(chol(V)),eye(V.n_rows,V.n_cols)); S = S * trans(S); rw = rwishart(nu, S); IW = as(rw["IW"]); CI = as(rw["CI"]); rooti = solve(trimatu(chol(IW)),eye(IW.n_rows,IW.n_cols)); b = vectorise(Bbar); r = rnorm(b.n_rows,0,1); mu = b + (CI * r) / sqrt(A(0,0)); comps(k) = List::create( Named("mu") = NumericVector(mu.begin(),mu.end()), //converts to a NumericVector, otherwise it will be interpretted as a matrix Named("rooti") = rooti); } } return(comps); } vec drawLabelsFromComps(mat const& y, vec const& p, List comps) { // Wayne Taylor 3/18/2015 // Function to determine which label is associated with each y value double logprod; vec mu, u; mat rooti; List compsk; int n = y.n_rows; vec res = zeros(n); int ncomp = comps.size(); mat prob(n,ncomp); for(int k = 0; k(compsk["mu"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> rooti = as(compsk["rooti"]); //Find log of MVN density using matrices logprod = log(prod(diagvec(rooti))); mat z(y); z.each_row() -= trans(mu); //subtracts mu from each row in z z = trans(rooti) * trans(z); z = -(y.n_cols/2.0) * log(2*M_PI) + logprod - .5 * sum(z % z, 0); // operator % performs element-wise multiplication prob.col(k) = trans(z); } prob = exp(prob); prob.each_row() %= trans(p); //element-wise multiplication // Cumulatively add each row and take a uniform draw between 0 and the cumulative sum prob = cumsum(prob, 1); u = as(runif(n)) % prob.col(ncomp-1); // Evaluative each column of "prob" until the uniform draw is less than the cumulative value for(int i = 0; i prob(i, res[i]++)); } return(res); } vec drawPFromLabels(vec const& a, vec const& z) { // Wayne Taylor 9/10/2014 // Function to draw the probabilities based on the label proportions vec a2 = a; int n = z.n_rows; //Count number of observations in each component for(int i = 0; i(n); std::vector thetaStar_vector(1); murooti thetaStar0_struct; thetaStar0_struct.mu = zeros(dimy); thetaStar0_struct.rooti = eye(dimy,dimy); thetaStar_vector[0] = thetaStar0_struct; //convert Prioralpha from List to struct priorAlpha priorAlpha_struct; priorAlpha_struct.power = PrioralphaList["power"]; priorAlpha_struct.alphamin = PrioralphaList["alphamin"]; priorAlpha_struct.alphamax = PrioralphaList["alphamax"]; priorAlpha_struct.n = PrioralphaList["n"]; //initialize lambda lambda lambda_struct; lambda_struct.mubar = zeros(dimy); lambda_struct.Amu = BayesmConstantA; lambda_struct.nu = dimy+BayesmConstantnuInc; lambda_struct.V = lambda_struct.nu*eye(dimy,dimy); //initialize alpha double alpha = BayesmConstantDPalpha; //intialize remaining variables thetaStarIndex thetaStarDrawOut_struct; std::vector new_utheta_vector(1), thetaNp1_vector(1); murooti thetaNp10_struct, out_struct; mat ydenmat; vec q0v, probs; uvec ind; int nunique, indsize; uvec spanall(dimy); for(int i = 0; i(R/keep); vec Istardraw = zeros(R/keep); vec adraw = zeros(R/keep); vec nudraw = zeros(R/keep); vec vdraw = zeros(R/keep); List thetaNp1draw(R/keep); imat inddraw = zeros(R/keep,n); //do scaling rowvec dvec, ybar; if(SCALE){ dvec = 1/sqrt(var(y,0,0)); //norm_type=0 performs normalisation using N-1, dim=0 is by column ybar = mean(y,0); y.each_row() -= ybar; //subtract ybar from each row y.each_row() %= dvec; //divide each row by dvec } //note on scaling //we model scaled y, z_i=D(y_i-ybar) D=diag(1/sigma1, ..., 1/sigma_dimy) //if p_z= 1/R sum(phi(z|mu,Sigma)) // p_y=1/R sum(phi(y|D^-1mu+ybar,D^-1SigmaD^-1) // rooti_y=Drooti_z //you might want to use quantiles instead, like median and (10,90) // start main iteration loop int mkeep = 0; if(nprint>0) startMcmcTimer(); for(int rep = 0; rep maxuniq) stop("maximum number of unique thetas exceeded"); //ydenmat is a length(thetaStar) x n array of density values given f(y[j,] | thetaStar[[i]] // note: due to remix step (below) we must recompute ydenmat each time! ydenmat = zeros(maxuniq,n); ydenmat(span(0,nunique-1),span::all) = yden(thetaStar_vector,y); thetaStarDrawOut_struct = thetaStarDraw(indic, thetaStar_vector, y, ydenmat, q0v, alpha, lambda_struct, maxuniq); thetaStar_vector = thetaStarDrawOut_struct.thetaStar_vector; indic = thetaStarDrawOut_struct.indic; nunique = thetaStar_vector.size(); //thetaNp1 and remix probs = zeros(nunique+1); for(int j = 0; j < nunique; j++){ ind = find(indic == (j+1)); indsize = ind.size(); probs[j] = indsize/(alpha + n + 0.0); new_utheta_vector[0] = thetaD(y(ind,spanall),lambda_struct); thetaStar_vector[j] = new_utheta_vector[0]; } probs[nunique] = alpha/(alpha+n+0.0); int ind = rmultinomF(probs); int probssize = probs.size(); if(ind == probssize) { out_struct = GD(lambda_struct); thetaNp10_struct.mu = out_struct.mu; thetaNp10_struct.rooti = out_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } else { out_struct = thetaStar_vector[ind-1]; thetaNp10_struct.mu = out_struct.mu; thetaNp10_struct.rooti = out_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } //draw alpha alpha = alphaD(priorAlpha_struct,nunique,gridsize); //draw lambda lambda_struct = lambdaD(lambda_struct,thetaStar_vector,lambda_hyper["alim"],lambda_hyper["nulim"],lambda_hyper["vlim"],gridsize); //print time to completion if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //save every keepth draw if((rep+1)%keep==0){ mkeep = (rep+1)/keep; alphadraw[mkeep-1] = alpha; Istardraw[mkeep-1] = nunique; adraw[mkeep-1] = lambda_struct.Amu; nu = lambda_struct.nu; nudraw[mkeep-1] = nu; mat V = lambda_struct.V; vdraw[mkeep-1] = V(0,0)/(nu+0.0); inddraw(mkeep-1,span::all) = trans(indic); thetaNp10_struct = thetaNp1_vector[0]; if(SCALE){ thetaNp10_struct.mu = thetaNp10_struct.mu/trans(dvec)+trans(ybar); thetaNp10_struct.rooti = diagmat(dvec)*thetaNp10_struct.rooti; } //here we put the draws into the list of lists of list format useful for finite mixture of normals utilities //we have to convetr to a NumericVector for the plotting functions to work thetaNp1draw[mkeep-1] = List::create(List::create(Named("mu") = NumericVector(thetaNp10_struct.mu.begin(),thetaNp10_struct.mu.end()),Named("rooti") = thetaNp10_struct.rooti)); } } if(nprint>0) endMcmcTimer(); return List::create( Named("inddraw") = inddraw, Named("thetaNp1draw") = thetaNp1draw, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw); } bayesm/src/Makevars0000644000176000001440000000012212541203644014041 0ustar ripleyusersPKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) PKG_CPPFLAGS = -I../inst/include/ bayesm/src/rmvst_rcpp.cpp0000644000176000001440000000067512541203644015265 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] vec rmvst(int nu, vec const& mu, mat const& root){ // Wayne Taylor 9/7/2014 // function to draw from MV s-t with nu df, mean mu, Sigma=t(root)%*%root // root is upper triangular cholesky root vec rnormd = rnorm(mu.size()); vec nvec = trans(root)*rnormd; return(nvec/sqrt(rchisq(1,nu)[0]/nu) + mu); //rchisq returns a vectorized object, so using [0] allows for the conversion to double } bayesm/src/rmnpGibbs_rcpp_loop.cpp0000644000176000001440000000764312541203644017070 0ustar ripleyusers#include "bayesm.h" //EXTRA FUNCTIONS SPECIFIC TO THE MAIN FUNCTION-------------------------------------------- vec drawwi(vec const& w, vec const& mu, mat const& sigmai, int p, int y){ // Wayne Taylor 9/8/2014 //function to draw w_i by Gibbing thru p vector int above; double bound; vec outwi = w; vec maxInd(2); for(int i = 0; i(w.n_rows); for(int i = 0; i(R/keep, pm1*pm1); mat betadraw = zeros(R/keep,k); vec wnew = zeros(Xrows); //set initial values of w,beta, sigma (or root of inv) vec wold = wnew; vec betaold = beta0; mat C = chol(solve(trimatu(sigma0),eye(sigma0.n_cols,sigma0.n_cols))); //trimatu interprets the matrix as upper triangular and makes solve more efficient //C is upper triangular root of sigma^-1 (G) = C'C mat sigmai, zmat, epsilon, S, IW, ucholinv, VSinv; vec betanew; List W; // start main iteration loop int mkeep = 0; if(nprint>0) startMcmcTimer(); for(int rep = 0; rep(W["C"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> //print time to completion if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //save every keepth draw if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1,span::all) = trans(betanew); IW = as(W["IW"]); sigmadraw(mkeep-1,span::all) = trans(vectorise(IW)); } wold = wnew; betaold = betanew; } if(nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmadraw") = sigmadraw); } bayesm/src/runireg_rcpp_loop.cpp0000644000176000001440000000414012541203644016605 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List runireg_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& A, int nu, double ssq, int R, int keep, int nprint) { // Keunwoo Kim 09/09/2014 // Purpose: perform iid draws from posterior of regression model using conjugate prior // Arguments: // y,X // betabar,A prior mean, prior precision // nu, ssq prior on sigmasq // R number of draws // keep thinning parameter // Output: list of beta, sigmasq // Model: // y = Xbeta + e e ~N(0,sigmasq) // y is n x 1 // X is n x k // beta is k x 1 vector of coefficients // Prior: // beta ~ N(betabar,sigmasq*A^-1) // sigmasq ~ (nu*ssq)/chisq_nu int mkeep; double s, sigmasq; mat RA, W, IR; vec z, btilde, res, beta; int nvar = X.n_cols; int nobs = y.size(); vec sigmasqdraw(R/keep); mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); for (int rep=0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if ((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); sigmasqdraw[mkeep-1] = sigmasq; } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmasqdraw") = NumericVector(sigmasqdraw.begin(),sigmasqdraw.end())); } bayesm/src/rhierLinearMixture_rcpp_loop.cpp0000644000176000001440000001023212541203644020753 0ustar ripleyusers#include "bayesm.h" //[[Rcpp::export]] List rhierLinearMixture_rcpp_loop(List const& regdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, int const& nu, mat const& V, int nu_e, vec const& ssq, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, vec ind, vec tau){ // Wayne Taylor 10/02/2014 int nreg = regdata.size(); int nvar = V.n_cols; int nz = Z.n_cols; mat rootpi, betabar, Abeta, Abetabar; int mkeep; unireg runiregout_struct; List regdatai, nmix; // convert List to std::vector of type "moments" std::vector regdata_vector; moments regdatai_struct; // store vector with struct for (int reg = 0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdatai_struct.XpX = as(regdatai["XpX"]); regdatai_struct.Xpy = as(regdatai["Xpy"]); regdata_vector.push_back(regdatai_struct); } // allocate space for draws mat oldbetas = zeros(nreg,nvar); mat taudraw(R/keep, nreg); cube betadraw(nreg, nvar, R/keep); mat probdraw(R/keep, oldprob.size()); mat Deltadraw(1,1); if(drawdelta) Deltadraw.zeros(R/keep, nz*nvar);//enlarge Deltadraw only if the space is required List compdraw(R/keep); if (nprint>0) startMcmcTimer(); for (int rep = 0; rep(mgout["p"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> ind = as(mgout["z"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> //now draw delta | {beta_i}, ind, comps if(drawdelta) olddelta = drawDelta(Z,oldbetas,ind,oldcomp,deltabar,Ad); //loop over all regression equations drawing beta_i | ind[i],z[i,],mu[ind[i]],rooti[ind[i]] for(int reg = 0; reg(oldcompreg[1]); //note: beta_i = Delta*z_i + u_i Delta is nvar x nz if(drawdelta){ olddelta.reshape(nvar,nz); betabar = as(oldcompreg[0])+olddelta*vectorise(Z(reg,span::all)); } else { betabar = as(oldcompreg[0]); } Abeta = trans(rootpi)*rootpi; Abetabar = Abeta*betabar; runiregout_struct = runiregG(regdata_vector[reg].y, regdata_vector[reg].X, regdata_vector[reg].XpX, regdata_vector[reg].Xpy, tau[reg], Abeta, Abetabar, nu_e, ssq[reg]); oldbetas(reg,span::all) = trans(runiregout_struct.beta); tau[reg] = runiregout_struct.sigmasq; } //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; taudraw(mkeep-1, span::all) = trans(tau); betadraw.slice(mkeep-1) = oldbetas; probdraw(mkeep-1, span::all) = trans(oldprob); if(drawdelta) Deltadraw(mkeep-1, span::all) = trans(vectorise(olddelta)); compdraw[mkeep-1] = oldcomp; } } if (nprint>0) endMcmcTimer(); nmix = List::create(Named("probdraw") = probdraw, Named("zdraw") = R_NilValue, //sets the value to NULL in R Named("compdraw") = compdraw); if(drawdelta){ return(List::create( Named("taudraw") = taudraw, Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("nmix") = nmix)); } else { return(List::create( Named("taudraw") = taudraw, Named("betadraw") = betadraw, Named("nmix") = nmix)); } } bayesm/src/lndMvn_rcpp.cpp0000644000176000001440000000114012541203644015334 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] double lndMvn(vec const& x, vec const& mu, mat const& rooti){ //Wayne Taylor 9/7/2014 // function to evaluate log of MV Normal density with mean mu, var Sigma // Sigma=t(root)%*%root (root is upper tri cholesky root) // Sigma^-1=rooti%*%t(rooti) // rooti is in the inverse of upper triangular chol root of sigma // note: this is the UL decomp of sigmai not LU! // Sigma=root'root root=inv(rooti) vec z = vectorise(trans(rooti)*(x-mu)); return((-(x.size()/2.0)*log(2*M_PI) -.5*(trans(z)*z) + sum(log(diagvec(rooti))))[0]); } bayesm/src/llmnl_rcpp.cpp0000644000176000001440000000075112541203644015223 0ustar ripleyusers#include "bayesm.h" //[[Rcpp::export]] double llmnl(vec const& beta, vec const& y, mat const& X){ // Wayne Taylor 9/7/2014 // Evaluates log-likelihood for the multinomial logit model int n = y.size(); int j = X.n_rows/n; mat Xbeta = X*beta; vec xby = zeros(n); vec denom = zeros(n); for(int i = 0; i const& thetaStar_vector){ // Wayne Taylor 3/14/2015 int dimz = z.n_cols; int dimx = x.n_cols; //variable type initializaion double sig; mat wk, zk, xk, rooti, Sigma, xt; vec yk, mu, e1, ee2, yt; uvec ind, colAllw, colAllz(dimz), colAllx(dimx); //Create the index vectors, the colAll vectors are equal to span::all but with uvecs (as required by .submat) for(int i = 0; i0){ if(isw) wk = w.submat(ind,colAllw); zk = z.submat(ind,colAllz); yk = y(ind); xk = x.submat(ind,colAllx); murooti thetaStark_struct = thetaStar_vector[k]; mu = thetaStark_struct.mu; rooti = thetaStark_struct.rooti; Sigma = solve(rooti,eye(2,2)); Sigma = trans(Sigma)*Sigma; e1 = xk-zk*delta; ee2 = mu[1] + (Sigma(0,1)/Sigma(0,0))*(e1-mu[0]); sig = sqrt(Sigma(1,1)-pow(Sigma(0,1),2.0)/Sigma(0,0)); yt = join_cols(yt,(yk-ee2)/sig); //analogous to rbind() if(isw) { xt = join_cols(xt,join_rows(xk,wk)/sig); } else { xt = join_cols(xt,xk/sig); } } } ytxtxtd out_struct; out_struct.yt = yt; out_struct.xt = xt; return(out_struct); } ytxtxtd get_ytxtd(vec const& y, mat const& z, double beta, vec const& gamma, mat const& x, mat const& w, int ncomp, ivec const& indic,std::vector const& thetaStar_vector, int dimd){ // Wayne Taylor 3/14/2015 int dimx = x.n_cols; //variable type initializaion int indsize, indicsize; vec zveck, yk, mu, ytk, u, yt; mat C, wk, zk, xk, rooti, Sigma, B, L, Li, z2, zt1, zt2, xtd; uvec colAllw, colAllz(dimd), colAllx(dimx), ind, seqindk, negseqindk; //Create index vectors (uvec) for submatrix views indicsize = indic.size(); //here the uvecs are declared once, and within each loop the correctly sized vector is extracted as needed uvec seqind(indicsize);for(int i = 0;i0){ mat xtdk(2*indsize,dimd); //extract the properly sized vector section seqindk = seqind.subvec(0,indsize-1); negseqindk = negseqind.subvec(0,indsize-1); if(isw) wk = w.submat(ind,colAllw); zk = z.submat(ind,colAllz); zveck = vectorise(trans(zk)); yk = y(ind); xk = x.submat(ind,colAllx); murooti thetaStark_struct = thetaStar_vector[k]; mu = thetaStark_struct.mu; rooti = thetaStark_struct.rooti; Sigma = solve(rooti,eye(2,2)); Sigma = trans(Sigma)*Sigma; B = C*Sigma*trans(C); L = trans(chol(B)); Li = solve(trimatl(L),eye(2,2)); // L is lower triangular, trimatl interprets the matrix as lower triangular and makes solve more efficient if(isw) { u = vectorise(yk-wk*gamma-mu[1]-beta*mu[0]); } else { u = vectorise(yk-mu[1]-beta*mu[0]); } ytk = vectorise(Li * join_cols(trans(xk-mu[0]),trans(u))); z2 = trans(join_rows(zveck,beta*zveck)); //join_rows is analogous to cbind() z2 = Li*z2; zt1 = z2(0,span::all); zt2 = z2(1,span::all); zt1.reshape(dimd,indsize); zt1 = trans(zt1); zt2.reshape(dimd,indsize); zt2=trans(zt2); xtdk(seqindk,colAllz) = zt1; xtdk(negseqindk,colAllz) = zt2; yt = join_cols(yt,ytk); xtd = join_cols(xtd,xtdk); } } ytxtxtd out_struct; out_struct.yt = yt; out_struct.xtd = xtd; return(out_struct); } DPOut rthetaDP(int maxuniq, double alpha, lambda lambda_struct, priorAlpha const& priorAlpha_struct, std::vector thetaStar_vector, ivec indic, vec const& q0v, mat const& y, int gridsize, List lambda_hyper){ // Wayne Taylor 3/14/2015 // function to make one draw from DP process // P. Rossi 1/06 // added draw of alpha 2/06 // removed lambdaD,etaD and function arguments 5/06 // removed thetaStar argument to .Call and creation of newthetaStar 7/06 // removed q0 computations as eta is not drawn 7/06 // changed for new version of thetadraw and removed calculation of thetaStar before // .Call 7/07 // y(i) ~ f(y|theta[[i]],eta) // theta ~ DP(alpha,G(lambda)) //output: // list with components: // thetaDraws: list, [[i]] is a list of the ith draw of the n theta's // where n is the length of the input theta and nrow(y) // thetaNp1Draws: list, [[i]] is ith draw of theta_{n+1} //args: // maxuniq: the maximum number of unique thetaStar values -- an error will be raised // if this is exceeded // alpha,lambda: starting values (or fixed DP prior values if not drawn). // Prioralpha: list of hyperparms of alpha prior // theta: list of starting value for theta's // thetaStar: list of unique values of theta, thetaStar[[i]] // indic: n vector of indicator for which unique theta (in thetaStar) // y: is a matrix nxk // thetaStar: list of unique values of theta, thetaStar[[i]] // q0v:a double vector with the same number of rows as y, giving \Int f(y(i)|theta,eta) dG_{lambda}(theta). int n = y.n_rows; int dimy = y.n_cols; //variable type initializaion int nunique, indsize, indp, probssize; vec probs; uvec ind; mat ydenmat; uvec spanall(dimy); for(int i = 0; i new_utheta(1), thetaNp1_vector(1); murooti thetaNp10_struct, outGD; vec p(n); p[n-1] = alpha/(alpha+(n-1)); for(int i = 0; i<(n-1); i++){ p[i] = 1/(alpha+(n-1)); } nunique = thetaStar_vector.size(); if(nunique > maxuniq) stop("maximum number of unique thetas exceeded"); //ydenmat is a length(thetaStar) x n array of density values given f(y[j,] | thetaStar[[i]] // note: due to remix step (below) we must recompute ydenmat each time! ydenmat = zeros(maxuniq,n); ydenmat(span(0,nunique-1),span::all) = yden(thetaStar_vector,y); thetaStarDrawOut_struct = thetaStarDraw(indic, thetaStar_vector, y, ydenmat, q0v, alpha, lambda_struct, maxuniq); thetaStar_vector = thetaStarDrawOut_struct.thetaStar_vector; indic = thetaStarDrawOut_struct.indic; nunique = thetaStar_vector.size(); //thetaNp1 and remix probs = zeros(nunique+1); for(int j = 0; j < nunique; j++){ ind = find(indic == (j+1)); indsize = ind.size(); probs[j] = indsize/(alpha + n + 0.0); new_utheta[0] = thetaD(y(ind,spanall),lambda_struct); thetaStar_vector[j] = new_utheta[0]; } probs[nunique] = alpha/(alpha+n+0.0); indp = rmultinomF(probs); probssize = probs.size(); if(indp == probssize) { outGD = GD(lambda_struct); thetaNp10_struct.mu = outGD.mu; thetaNp10_struct.rooti = outGD.rooti; thetaNp1_vector[0] = thetaNp10_struct; } else { outGD = thetaStar_vector[indp-1]; thetaNp10_struct.mu = outGD.mu; thetaNp10_struct.rooti = outGD.rooti; thetaNp1_vector[0] = thetaNp10_struct; } //draw alpha alpha = alphaD(priorAlpha_struct,nunique,gridsize); //draw lambda lambda_struct = lambdaD(lambda_struct,thetaStar_vector,lambda_hyper["alim"],lambda_hyper["nulim"],lambda_hyper["vlim"],gridsize); DPOut out_struct; out_struct.indic = indic; out_struct.thetaStar_vector = thetaStar_vector; out_struct.thetaNp1_vector = thetaNp1_vector; out_struct.alpha = alpha; out_struct.Istar = nunique; out_struct.lambda_struct = lambda_struct; return(out_struct); } //RCPP SECTION---- //[[Rcpp::export]] List rivDP_rcpp_loop(int R, int keep, int nprint, int dimd, vec const& mbg, mat const& Abg, vec const& md, mat const& Ad, vec const& y, bool isgamma, mat const& z, vec const& x, mat const& w, vec delta, List const& PrioralphaList, int gridsize, bool SCALE, int maxuniq, double scalex, double scaley, List const& lambda_hyper,double BayesmConstantA, int BayesmConstantnu){ // Wayne Taylor 3/14/2015 int n = y.size(); int dimg = 1; if(isgamma) dimg = w.n_cols; //variable type initializaion int Istar, bgsize, mkeep; double beta; vec gammaVec, q0v, bg; mat errMat, wEmpty, V; wEmpty.reset(); //enforce 0 elements ytxtxtd out_struct; //initialize indicator vector, thetaStar, ncomp, alpha ivec indic = ones(n); std::vector thetaStar_vector(1), thetaNp1_vector(1); murooti thetaNp10_struct, thetaStar0_struct; thetaStar0_struct.mu = zeros(2); thetaStar0_struct.rooti = eye(2,2); thetaStar_vector[0] = thetaStar0_struct; //Initialize lambda lambda lambda_struct; lambda_struct.mubar = zeros(2); lambda_struct.Amu = BayesmConstantA; lambda_struct.nu = BayesmConstantnu; lambda_struct.V = lambda_struct.nu*eye(2,2); //convert Prioralpha from List to struct priorAlpha priorAlpha_struct; priorAlpha_struct.power = PrioralphaList["power"]; priorAlpha_struct.alphamin = PrioralphaList["alphamin"]; priorAlpha_struct.alphamax = PrioralphaList["alphamax"]; priorAlpha_struct.n = PrioralphaList["n"]; int ncomp = 1; double alpha = 1.0; //allocate space for draws mat deltadraw = zeros(R/keep,dimd); vec betadraw = zeros(R/keep); vec alphadraw = zeros(R/keep); vec Istardraw = zeros(R/keep); mat gammadraw = zeros(R/keep,dimg); List thetaNp1draw(R/keep); vec nudraw = zeros(R/keep); vec vdraw = zeros(R/keep); vec adraw = zeros(R/keep); if(nprint>0) startMcmcTimer(); for(int rep = 0; rep < R; rep++) { //draw beta and gamma if(isgamma){ out_struct = get_ytxt(y,z,delta,x,w,ncomp,indic,thetaStar_vector); } else { out_struct = get_ytxt(y,z,delta,x,wEmpty,ncomp,indic,thetaStar_vector); } bg = breg(out_struct.yt,out_struct.xt,mbg,Abg); beta = bg[0]; bgsize = bg.size()-1; if(isgamma) gammaVec = bg.subvec(1,bgsize); //draw delta if(isgamma){ out_struct=get_ytxtd(y,z,beta,gammaVec,x,w,ncomp,indic,thetaStar_vector,dimd); } else { out_struct=get_ytxtd(y,z,beta,gammaVec,x,wEmpty,ncomp,indic,thetaStar_vector,dimd); } delta = breg(out_struct.yt,out_struct.xtd,md,Ad); //DP process stuff- theta | lambda if(isgamma) { errMat = join_rows(x-z*delta,y-beta*x-w*gammaVec); } else { errMat = join_rows(x-z*delta,y-beta*x); } q0v = q0(errMat,lambda_struct); DPOut DPout_struct = rthetaDP(maxuniq,alpha,lambda_struct,priorAlpha_struct,thetaStar_vector,indic,q0v,errMat,gridsize,lambda_hyper); indic = DPout_struct.indic; thetaStar_vector = DPout_struct.thetaStar_vector; alpha = DPout_struct.alpha; Istar = DPout_struct.Istar; thetaNp1_vector = DPout_struct.thetaNp1_vector; thetaNp10_struct = thetaNp1_vector[0]; ncomp=thetaStar_vector.size(); lambda_struct = DPout_struct.lambda_struct; if (nprint>0) if((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; deltadraw(mkeep-1,span::all) = trans(delta); betadraw[mkeep-1] = beta; alphadraw[mkeep-1] = alpha; Istardraw[mkeep-1] = Istar; if(isgamma) gammadraw(mkeep-1,span::all) = trans(gammaVec); //We need to convert from to NumericVector so that the nmix plotting works properly (it does not work for an nx1 matrix) thetaNp1draw[mkeep-1] = List::create(List::create(Named("mu") = NumericVector(thetaNp10_struct.mu.begin(),thetaNp10_struct.mu.end()),Named("rooti") = thetaNp10_struct.rooti)); adraw[mkeep-1] = lambda_struct.Amu; nudraw[mkeep-1] = lambda_struct.nu; V = lambda_struct.V; vdraw[mkeep-1] = V(0,0)/(lambda_struct.nu+0.0); } } //rescale if(SCALE){ deltadraw=deltadraw*scalex; betadraw=betadraw*scaley/scalex; if(isgamma) gammadraw=gammadraw*scaley; } if (nprint>0) endMcmcTimer(); return List::create( Named("deltadraw") = deltadraw, Named("betadraw") = betadraw, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("gammadraw") = gammadraw, Named("thetaNp1draw") = thetaNp1draw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw); } bayesm/src/rwishart_rcpp.cpp0000644000176000001440000000221412541203644015744 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rwishart(int const& nu, mat const& V){ // Wayne Taylor 4/7/2015 // Function to draw from Wishart (nu,V) and IW // W ~ W(nu,V) // E[W]=nuV // WI=W^-1 // E[WI]=V^-1/(nu-m-1) // T has sqrt chisqs on diagonal and normals below diagonal int m = V.n_rows; mat T = zeros(m,m); for(int i = 0; i < m; i++) { T(i,i) = sqrt(rchisq(1,nu-i)[0]); //rchisq returns a vectorized object, so using [0] allows for the conversion to double } for(int j = 0; j < m; j++) { for(int i = j+1; i < m; i++) { T(i,j) = rnorm(1)[0]; //rnorm returns a NumericVector, so using [0] allows for conversion to double }} mat C = trans(T)*chol(V); mat CI = solve(trimatu(C),eye(m,m)); //trimatu interprets the matrix as upper triangular and makes solve more efficient // C is the upper triangular root of Wishart therefore, W=C'C // this is the LU decomposition Inv(W) = CICI' Note: this is // the UL decomp not LU! // W is Wishart draw, IW is W^-1 return List::create( Named("W") = trans(C) * C, Named("IW") = CI * trans(CI), Named("C") = C, Named("CI") = CI); } bayesm/src/lndIChisq_rcpp.cpp0000644000176000001440000000047312541203644015764 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] mat lndIChisq(double nu, double ssq, mat const& X) { // Keunwoo Kim 07/24/2014 // Purpose: evaluate log-density of scaled Inverse Chi-sq density of random variable Z=nu*ssq/chisq(nu) return(-lgamma(nu/2)+(nu/2)*log((nu*ssq)/2)-((nu/2)+1)*log(X)-(nu*ssq)/(2*X)); } bayesm/src/rnmixGibbs_rcpp_loop.cpp0000644000176000001440000000241012541203644017234 0ustar ripleyusers#include "bayesm.h" //[[Rcpp::export]] List rnmixGibbs_rcpp_loop(mat const& y, mat const& Mubar, mat const& A, int nu, mat const& V, vec const& a, vec p, vec z, int const& R, int const& keep, int const& nprint) { // Wayne Taylor 9/10/2014 int mkeep = 0; mat pdraw(R/keep,p.size()); mat zdraw(R/keep,z.size()); List compdraw(R/keep); if(nprint>0) startMcmcTimer(); // start main iteration loop for(int rep = 0; rep(out["p"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> z = as(out["z"]); // print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; pdraw(mkeep-1,span::all) = trans(p); zdraw(mkeep-1,span::all) = trans(z); compdraw[mkeep-1] = compsd; } } if(nprint>0) endMcmcTimer(); return List::create( Named("probdraw") = pdraw, Named("zdraw") = zdraw, Named("compdraw") = compdraw); } bayesm/src/lndMvst_rcpp.cpp0000644000176000001440000000147012541203644015533 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] double lndMvst(vec const& x, int nu, vec const& mu, mat const& rooti, bool NORMC = false){ // Wayne Taylor 9/7/2014 // function to evaluate log of MVstudent t density with nu df, mean mu, // and with sigmai=rooti%*%t(rooti) note: this is the UL decomp of sigmai not LU! // rooti is in the inverse of upper triangular chol root of sigma // or Sigma=root'root root=inv(rooti) int dim = x.size(); double constant; if(NORMC){ constant = (nu/2.0)*log((double)nu)+lgamma((nu+dim)/2.0)-(dim/2.0)*log(M_PI)-lgamma(nu/2.0); //"2.0"" is used versus "2" so that the division is not truncated as an "int" } else { constant = 0.0; } vec z = vectorise(trans(rooti)*(x-mu)); return((constant-((dim+nu)/2.0)*log(nu+trans(z)*z)+sum(log(diagvec(rooti))))[0]); } bayesm/src/rdirichlet_rcpp.cpp0000644000176000001440000000063412541203644016236 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] vec rdirichlet(vec const& alpha){ // Wayne Taylor 4/7/2015 // Purpose: // draw from Dirichlet(alpha) int dim = alpha.size(); vec y = zeros(dim); for(int i = 0; i regdata_vector; moments regdatai_struct; // store vector with struct for (reg=0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdatai_struct.XpX = as(regdatai["XpX"]); regdatai_struct.Xpy = as(regdatai["Xpy"]); regdata_vector.push_back(regdatai_struct); } mat betas(nreg, nvar); mat Vbetadraw(R/keep, nvar*nvar); mat Deltadraw(R/keep, nz*nvar); mat taudraw(R/keep, nreg); cube betadraw(nreg, nvar, R/keep); if (nprint>0) startMcmcTimer(); //start main iteration loop for (int rep=0; rep(rmregout["Sigma"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> Delta = as(rmregout["B"]); //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; Vbetadraw(mkeep-1, span::all) = trans(vectorise(Vbeta)); Deltadraw(mkeep-1, span::all) = trans(vectorise(Delta)); taudraw(mkeep-1, span::all) = trans(tau); betadraw.slice(mkeep-1) = betas; } } if (nprint>0) endMcmcTimer(); return List::create( Named("Vbetadraw") = Vbetadraw, Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("taudraw") = taudraw); } bayesm/src/utilityFunctions.cpp0000644000176000001440000006073512541203644016465 0ustar ripleyusers#include "bayesm.h" //Used in rmvpGibbs and rmnpGibbs--------------------------------------------------------------------------------- vec condmom(vec const& x, vec const& mu, mat const& sigmai, int p, int j){ // Wayne Taylor 9/24/2014 //function to compute moments of x[j] | x[-j] //output is a vec: the first element is the conditional mean // the second element is the conditional sd vec out(2); int jm1 = j-1; int ind = p*jm1; double csigsq = 1./sigmai(ind+jm1); double m = 0.0; for(int i = 0; i .999999999) arg = .999999999; if(arg < .0000000001) arg = .0000000001; result = mu + sigma*R::qnorm(arg,0.0,1.0,1,0); return (result); } //Used in rhierLinearModel, rhierLinearMixture and rhierMnlRWMixture------------------------------------------------------ mat drawDelta(mat const& x,mat const& y,vec const& z,List const& comps,vec const& deltabar,mat const& Ad){ // Wayne Taylor 10/01/2014 // delta = vec(D) // given z and comps (z[i] gives component indicator for the ith observation, // comps is a list of mu and rooti) // y is n x p // x is n x k // y = xD' + U , rows of U are indep with covs Sigma_i given by z and comps int p = y.n_cols; int k = x.n_cols; int ncomp = comps.length(); mat xtx = zeros(k*p,k*p); mat xty = zeros(p,k); //this is the unvecced version, reshaped after the sum //Create the index vectors, the colAll vectors are equal to span::all but with uvecs (as required by .submat) uvec colAlly(p), colAllx(k); for(int i = 0; i0){ mat yi = y.submat(ind,colAlly); mat xi = x.submat(ind,colAllx); List compsi = comps[compi]; rowvec mui = as(compsi[0]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> mat rootii = trimatu(as(compsi[1])); //trimatu interprets the matrix as upper triangular yi.each_row() -= mui; //subtracts mui from each row of yi mat sigi = rootii*trans(rootii); xtx = xtx + kron(trans(xi)*xi,sigi); xty = xty + (sigi * (trans(yi)*xi)); } } xty.reshape(xty.n_rows*xty.n_cols,1); //vec(t(D)) ~ N(V^{-1}(xty + Ad*deltabar),V^{-1}) where V = (xtx+Ad) // compute the inverse of xtx+Ad mat ucholinv = solve(trimatu(chol(xtx+Ad)), eye(k*p,k*p)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat Vinv = ucholinv*trans(ucholinv); return(Vinv*(xty+Ad*deltabar) + trans(chol(Vinv))*as(rnorm(deltabar.size()))); } unireg runiregG(vec const& y, mat const& X, mat const& XpX, vec const& Xpy, double sigmasq, mat const& A, vec const& Abetabar, int nu, double ssq) { // Keunwoo Kim 09/16/2014 // Purpose: // perform one Gibbs iteration for Univ Regression Model // only does one iteration so can be used in rhierLinearModel // Model: // y = Xbeta + e e ~N(0,sigmasq) // y is n x 1 // X is n x k // beta is k x 1 vector of coefficients // Prior: // beta ~ N(betabar,A^-1) // sigmasq ~ (nu*ssq)/chisq_nu unireg out_struct; int n = y.size(); int k = XpX.n_cols; //first draw beta | sigmasq mat IR = solve(trimatu(chol(XpX/sigmasq+A)), eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient vec btilde = (IR*trans(IR)) * (Xpy/sigmasq + Abetabar); vec beta = btilde + IR*vec(rnorm(k)); //now draw sigmasq | beta double s = sum(square(y-X*beta)); sigmasq = (s + nu*ssq)/rchisq(1,nu+n)[0]; //rchisq returns a vectorized object, so using [0] allows for the conversion to double out_struct.beta = beta; out_struct.sigmasq = sigmasq; return (out_struct); } //Used in rnegbinRW and rhierNegbinRw------------------------------------------------------------------------------------- double llnegbin(vec const& y, vec const& lambda, double alpha, bool constant){ // Keunwoo Kim 11/02/2014 // Computes the log-likelihood // Arguments // y - a vector of observation // lambda - a vector of mean parameter (=exp(X*beta)) // alpha - dispersion parameter // constant - TRUE(FALSE) if it computes (un)normalized log-likeihood // PMF // pmf(y) = (y+alpha-1)Choose(y) * p^alpha * (1-p)^y // (y+alpha-1)Choose(y) = (alpha)*(alpha+1)*...*(alpha+y-1) / y! when y>=1 (0 when y=0) int i; int nobs = y.size(); vec prob = alpha/(alpha+lambda); vec logp(nobs); if (constant){ // normalized log-likelihood for (i=0; i(rnorm(X.n_cols)); double cll = llmnl(betac,y,X); double clpost = cll+lndMvn(betac,betabar,rootpi); double ldiff = clpost-oldll-lndMvn(oldbeta,betabar,rootpi); alphaminv << 1 << exp(ldiff); double alpha = min(alphaminv); if(alpha < 1) { unif = runif(1)[0]; //runif returns a NumericVector, so using [0] allows for conversion to double } else { unif=0;} if (unif <= alpha) { betadraw = betac; oldll = cll; } else { betadraw = oldbeta; stay = 1; } metropout_struct.betadraw = betadraw; metropout_struct.stay = stay; metropout_struct.oldll = oldll; return (metropout_struct); } //Used in rDPGibbs, rhierMnlDP, rivDP----------------------------------------------------------------------------- int rmultinomF(vec const& p){ // Wayne Taylor 1/28/2015 vec csp = cumsum(p); double rnd = runif(1)[0]; //runif returns a NumericVector, so using [0] allows for conversion to double int res = 0; int psize = p.size(); for(int i = 0; i < psize; i++){ if(rnd > csp[i]) res = res+1; } return(res+1); } mat yden(std::vector const& thetaStar_vector, mat const& y){ // Wayne Taylor 2/4/2015 // function to compute f(y | theta) // computes f for all values of theta in theta list of lists // arguments: // thetaStar is a list of lists. thetaStar[[i]] is a list with components, mu, rooti // y |theta[[i]] ~ N(mu,(rooti %*% t(rooti))^-1) rooti is inverse of Chol root of Sigma // output: // length(thetaStar) x n array of values of f(y[j,]|thetaStar[[i]] int nunique = thetaStar_vector.size(); int n = y.n_rows; int k = y.n_cols; mat ydenmat = zeros(nunique,n); vec mu; mat rooti, transy, quads; for(int i = 0; i < nunique; i++){ //now compute vectorized version of lndMvn //compute y_i'RIRI'y_i for all i mu = thetaStar_vector[i].mu; rooti = thetaStar_vector[i].rooti; transy = trans(y); transy.each_col() -= mu; //column-wise subtraction quads = sum(square(trans(rooti) * transy),0); //same as colSums ydenmat(i,span::all) = exp(-(k/2.0)*log(2*M_PI) + sum(log(rooti.diag())) - .5*quads); } return(ydenmat); } ivec numcomp(ivec const& indic, int k){ // Wayne Taylor 1/28/2015 //find the number of times each of k integers is in the vector indic ivec ncomp(k); for(int comp = 0; comp < k; comp++){ ncomp[comp]=sum(indic == (comp+1)); } return(ncomp); } murooti thetaD(mat const& y, lambda const& lambda_struct){ // Wayne Taylor 2/4/2015 // function to draw from posterior of theta given data y and base prior G0(lambda) // here y ~ N(mu,Sigma) // theta = list(mu=mu,rooti=chol(Sigma)^-1) // mu|Sigma ~ N(mubar,Sigma (x) Amu-1) // Sigma ~ IW(nu,V) // arguments: // y is n x k matrix of obs // lambda is list(mubar,Amu,nu,V) // output: // one draw of theta, list(mu,rooti) // Sigma=inv(rooti)%*%t(inv(rooti)) // note: we assume that y is a matrix. if there is only one obs, y is a 1 x k matrix mat X = ones(y.n_rows,1); mat A(1,1); A.fill(lambda_struct.Amu); List rout = rmultireg(y,X,trans(lambda_struct.mubar),A,lambda_struct.nu,lambda_struct.V); murooti out_struct; out_struct.mu = as(rout["B"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> out_struct.rooti = solve(chol(trimatu(as(rout["Sigma"]))),eye(y.n_cols,y.n_cols)); //trimatu interprets the matrix as upper triangular and makes solve more efficient return(out_struct); } thetaStarIndex thetaStarDraw(ivec indic, std::vector thetaStar_vector, mat const& y, mat ydenmat, vec const& q0v, double alpha, lambda const& lambda_struct, int maxuniq) { // Wayne Taylor 2/4/2015 // indic is n x 1 vector of indicator of which of thetaStar is assigned to each observation // thetaStar is list of the current components (some of which may never be used) // y is n x d matrix of observations // ydenmat is maxuniq x n matrix to store density evaluations - we assume first // length(Thetastar) rows are filled in with density evals // q0v is vector of bayes factors for new component and each observation // alpha is DP process tightness prior // lambda is list of priors for the base DP process measure // maxuniq maximum number of mixture components // yden is function to fill out an array // thetaD is function to draw theta from posterior of theta given y and G0 int n = indic.size(); ivec ncomp, indicC; int k, inc, cntNonzero; std::vector listofone_vector(1); std::vector thetaStarC_vector; //draw theta_i given theta_-i for(int i = 0; i(n-1); inc = 0; for(int j = 0; j<(n-1); j++){ if(j == i) {inc = inc + 1;} indicmi[j] = indic[inc]; inc = inc+1; } ncomp = numcomp(indicmi,k); for(int comp = 0; comp maxuniq) { stop("max number of comps exceeded"); } else { listofone_vector[0] = thetaD(y(i,span::all),lambda_struct); thetaStar_vector.push_back(listofone_vector[0]); ydenmat(k,span::all) = yden(listofone_vector,y); }} } //clean out thetaStar of any components which have zero observations associated with them //and re-write indic vector k = thetaStar_vector.size(); indicC = zeros(n); ncomp = numcomp(indic,k); cntNonzero = 0; for(int comp = 0; comp 1) { vec km1(k-1); for(int i = 0; i < (k-1); i++) km1[i] = i+1; //vector of 1:k SEE SEQ_ALONG lnk1k2 = (k/2.0)*log(2.0)+log((lambda_struct.nu-k)/2)+lgamma((lambda_struct.nu-k)/2)-lgamma(lambda_struct.nu/2)+sum(log(lambda_struct.nu/2-km1/2)); } else { lnk1k2 = (k/2.0)*log(2.0)+log((lambda_struct.nu-k)/2)+lgamma((lambda_struct.nu-k)/2)-lgamma(lambda_struct.nu/2); } constant = -(k/2.0)*log(2*M_PI)+(k/2.0)*log(lambda_struct.Amu/(1+lambda_struct.Amu)) + lnk1k2 + lambda_struct.nu*logdetR; // note: here we are using the fact that |V + S_i | = |R|^2 (1 + v_i'v_i) // where v_i = sqrt(Amu/(1+Amu))*t(R^-1)*(y_i-mubar), R is chol(V) // and S_i = Amu/(1+Amu) * (y_i-mubar)(y_i-mubar)' transy = trans(y); transy.each_col() -= lambda_struct.mubar; m = sqrt(lambda_struct.Amu/(1+lambda_struct.Amu))*trans(solve(trimatu(R),eye(y.n_cols,y.n_cols)))*transy; //trimatu interprets the matrix as upper triangular and makes solve more efficient vivi = sum(square(m),0); lnq0v = constant - ((lambda_struct.nu+1)/2)*(2*logdetR+log(1+vivi)); return(trans(exp(lnq0v))); } vec seq_rcpp(double from, double to, int len){ // Wayne Taylor 1/28/2015 // Same as R::seq() vec res(len); res[len-1] = to; res[0] = from; //note the order of these two statements is important, when gridsize = 1 res[0] will be rewritten to the correct number double increment = (res[len-1]-res[0])/(len-1); for(int i = 1; i<(len-1); i++) res[i] = res[i-1] + increment; return(res); } double alphaD(priorAlpha const& priorAlpha_struct, int Istar, int gridsize){ // Wayne Taylor 2/4/2015 // function to draw alpha using prior, p(alpha)= (1-(alpha-alphamin)/(alphamax-alphamin))**power //same as seq vec alpha = seq_rcpp(priorAlpha_struct.alphamin,priorAlpha_struct.alphamax-.000001,gridsize); vec lnprob(gridsize); for(int i = 0; i(Rout["IW"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> mat root = chol(Sigma); mat draws = rnorm(k); mat mu = lambda_struct.mubar + (1/sqrt(lambda_struct.Amu))*trans(root)*draws; murooti out_struct; out_struct.mu = mu; out_struct.rooti = solve(trimatu(root),eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient return(out_struct); } lambda lambdaD(lambda const& lambda_struct, std::vector const& thetaStar_vector, vec const& alim, vec const& nulim, vec const& vlim, int gridsize){ // Wayne Taylor 2/4/2015 // revision history // p. rossi 7/06 // vectorized 1/07 // changed 2/08 to paramaterize V matrix of IW prior to nu*v*I; then mode of Sigma=nu/(nu+2)vI // this means that we have a reparameterization to v* = nu*v // function to draw (nu, v, a) using uniform priors // theta_j=(mu_j,Sigma_j) mu_j~N(0,Sigma_j/a) Sigma_j~IW(nu,vI) // recall E[Sigma]= vI/(nu-dim-1) vec lnprob, probs, rowSumslgammaarg; int ind; //placeholder for matrix indexing murooti thetaStari_struct; mat rootii; vec mui; mat mout, rimu, arg, lgammaarg; double sumdiagriri, sumlogdiag, sumquads, adraw, nudraw, vdraw; murooti thetaStar0_struct = thetaStar_vector[0]; int d = thetaStar0_struct.mu.size(); int Istar = thetaStar_vector.size(); vec aseq = seq_rcpp(alim[0],alim[1],gridsize); vec nuseq = d-1+exp(seq_rcpp(nulim[0],nulim[1],gridsize)); //log uniform grid vec vseq = seq_rcpp(vlim[0],vlim[1],gridsize); // "brute" force approach would simply loop over the // "observations" (theta_j) and use log of the appropriate densities. To vectorize, we // notice that the "data" comes via various statistics: // 1. sum of log(diag(rooti_j) // 2. sum of tr(V%*%rooti_j%*%t(rooti_j)) where V=vI_d // 3. quadratic form t(mu_j-0)%*%rooti%*%t(rooti)%*%(mu_j-0) // thus, we will compute these first. // for documentation purposes, we leave brute force code in comment fields // extract needed info from thetastar list //mout has the rootis in form: [t(rooti_1), t(rooti_2), ...,t(rooti_Istar)] mout = zeros(d,Istar*d); ind = 0; for(int i = 0; i < Istar; i++){ thetaStari_struct = thetaStar_vector[i]; rootii = thetaStari_struct.rooti; ind = i*d; mout.submat(0, ind,d-1,ind+d-1) = trans(rootii); } sumdiagriri = sum(sum(square(mout),0)); //sum_i trace(rooti_i*trans(rooti_i)) // now get diagonals of rooti sumlogdiag = 0.0; for(int i = 0; i < Istar; i++){ ind = i*d; for(int j = 0; j < d; j++){ sumlogdiag = sumlogdiag+log(mout(j,ind+j)); } } //columns of rimu contain trans(rooti_i)*mu_i rimu = zeros(d,Istar); for(int i = 0; i < Istar; i++){ thetaStari_struct = thetaStar_vector[i]; mui = thetaStari_struct.mu; rootii = thetaStari_struct.rooti; rimu(span::all,i) = trans(rootii) * mui; } sumquads = sum(sum(square(rimu),0)); // draw a (conditionally indep of nu,v given theta_j) lnprob = zeros(aseq.size()); // for(i in seq(along=aseq)){ // for(j in seq(along=thetastar)){ // lnprob[i]=lnprob[i]+lndMvn(thetastar[[j]]$mu,c(rep(0,d)),thetastar[[j]]$rooti*sqrt(aseq[i]))} lnprob = Istar*(-(d/2.0)*log(2*M_PI))-.5*aseq*sumquads+Istar*d*log(sqrt(aseq))+sumlogdiag; lnprob = lnprob-max(lnprob) + 200; probs = exp(lnprob); probs = probs/sum(probs); adraw = aseq[rmultinomF(probs)-1]; // draw nu given v lnprob = zeros(nuseq.size()); // for(i in seq(along=nuseq)){ // for(j in seq(along=thetastar)){ // Sigma_j=crossprod(backsolve(thetastar[[j]]$rooti,diag(d))) // lnprob[i]=lnprob[i]+lndIWishart(nuseq[i],V,Sigma_j)} //same as arg = (nuseq+1-arg)/2.0; arg = zeros(gridsize,d); for(int i = 0; i < d; i++) { vec indvec(gridsize); indvec.fill(-(i+1)+1); arg(span::all,i) = indvec; } arg.each_col() += nuseq; arg = arg/2.0; lgammaarg = zeros(gridsize,d); for(int i = 0; i < gridsize; i++){ for(int j = 0; j < d; j++){ lgammaarg(i,j) = lgamma(arg(i,j)); }} rowSumslgammaarg = sum(lgammaarg,1); lnprob = zeros(gridsize); for(int i = 0; i(vseq.size()); // for(i in seq(along=vseq)){ // V=vseq[i]*diag(d) // for(j in seq(along=thetastar)){ // Sigma_j=crossprod(backsolve(thetastar[[j]]$rooti,diag(d))) // lnprob[i]=lnprob[i]+lndIWishart(nudraw,V,Sigma_j)} // lnprob=Istar*nudraw*d*log(sqrt(vseq))-.5*sumdiagriri*vseq lnprob = Istar*nudraw*d*log(sqrt(vseq*nudraw))-.5*sumdiagriri*vseq*nudraw; lnprob = lnprob-max(lnprob)+200; probs = exp(lnprob); probs = probs/sum(probs); vdraw = vseq[rmultinomF(probs)-1]; // put back into lambda lambda out_struct; out_struct.mubar = zeros(d); out_struct.Amu = adraw; out_struct.nu = nudraw; out_struct.V = nudraw*vdraw*eye(d,d); return(out_struct); } //Used in llnhlogit and simnhlogit--------------------------------------------------------------------------------- double root(double c1, double c2, double tol, int iterlim){ //function to find root of c1 - c2u = lnu int iter = 0; double uold = .1; double unew = .00001; while (iter <= iterlim && fabs(uold-unew) > tol){ uold = unew; unew=uold + (uold*(c1 -c2*uold - log(uold)))/(1. + c2*uold); if(unew < 1.0e-50) unew=1.0e-50; iter=iter+1; } return(unew); } //[[Rcpp::export]] vec callroot(vec const& c1, vec const& c2, double tol, int iterlim){ int n = c1.size(); vec u = zeros(n); for(int i = 0; i(wrap(mu)))-pnorm(gamma2-as(wrap(mu))); //pnorm takes Rcpp type NumericVector, NOT arma objects of type vec vec arg = as(temp); double epsilon = 1.0/(10^-50); for (int j=0; j1){ alpha = 1.0; } if (alpha<1){ unif = runif(1)[0]; //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element } else{ unif = 0; } if (unif<=alpha){ dstardraw = dstarc; oldll = cll; } else{ dstardraw = olddstar; stay = 1; } return List::create( Named("dstardraw") = dstardraw, Named("oldll") = oldll, Named("stay") = stay ); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] List rordprobitGibbs_rcpp_loop(vec const& y, mat const& X, int k, mat const& A, vec const& betabar, mat const& Ad, double s, mat const& inc_root, vec const& dstarbar, vec const& betahat, int R, int keep, int nprint){ // Keunwoo Kim 09/09/2014 // Purpose: draw from posterior for ordered probit using Gibbs Sampler and metropolis RW // Arguments: // Data // X is nobs x nvar, y is nobs vector of 1,2,.,k (ordinal variable) // Prior // A is nvar x nvar prior preci matrix // betabar is nvar x 1 prior mean // Ad is ndstar x ndstar prior preci matrix of dstar (ncut is number of cut-offs being estimated) // dstarbar is ndstar x 1 prior mean of dstar // Mcmc // R is number of draws // keep is thinning parameter // nprint - prints the estimated time remaining for every nprint'th draw // s is scale parameter of random work Metropolis // Output: list of betadraws and cutdraws // Model: // z=Xbeta + e < 0 e ~N(0,1) // y=1,..,k, if z~c(c[k], c[k+1]) // cutoffs = c[1],..,c[k+1] // dstar = dstar[1],dstar[k-2] // set c[1]=-100, c[2]=0, ...,c[k+1]=100 // c[3]=exp(dstar[1]),c[4]=c[3]+exp(dstar[2]),..., // c[k]=c[k-1]+exp(datsr[k-2]) // Note: 1. length of dstar = length of cutoffs - 3 // 2. Be careful in assessing prior parameter, Ad. .1 is too small for many applications. // Prior: // beta ~ N(betabar,A^-1) // dstar ~ N(dstarbar, Ad^-1) int stay, i, mkeep; vec z; List metropout; int nvar = X.n_cols; int ncuts = k+1; int ncut = ncuts-3; int ndstar = k-2; int ny = y.size(); mat betadraw(R/keep, nvar); mat cutdraw(R/keep, ncuts); mat dstardraw(R/keep, ndstar); vec staydraw(R/keep); vec cutoff1(ny); vec cutoff2(ny); vec sigma(X.n_rows); sigma.ones(); // compute the inverse of trans(X)*X+A mat ucholinv = solve(trimatu(chol(trans(X)*X+A)), eye(nvar,nvar)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat XXAinv = ucholinv*trans(ucholinv); mat root = chol(XXAinv); vec Abetabar = trans(A)*betabar; // compute the inverse of Ad ucholinv = solve(trimatu(chol(Ad)), eye(ndstar,ndstar)); mat Adinv = ucholinv*trans(ucholinv); mat rootdi = chol(Adinv); // set initial values for MCMC vec olddstar(ndstar); olddstar.zeros(); vec beta = betahat; vec cutoffs = dstartoc(olddstar); double oldll = lldstar(olddstar, y, X*betahat); if (nprint>0) startMcmcTimer(); //start main iteration loop for (int rep=0; rep(metropout["dstardraw"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> oldll = as(metropout["oldll"]); cutoffs = dstartoc(olddstar); stay = as(metropout["stay"]); //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; cutdraw(mkeep-1,span::all) = trans(cutoffs); dstardraw(mkeep-1,span::all) = trans(olddstar); betadraw(mkeep-1,span::all) = trans(beta); staydraw[mkeep-1] = stay; } } double accept = 1-sum(staydraw)/(R/keep); if (nprint>0) endMcmcTimer(); return List::create( Named("cutdraw") = cutdraw, Named("dstardraw") = dstardraw, Named("betadraw") = betadraw, Named("accept") = accept ); } bayesm/src/rhierMnlRwMixture_rcpp_loop.cpp0000644000176000001440000001042112541203644020600 0ustar ripleyusers#include "bayesm.h" //[[Rcpp::export]] List rhierMnlRwMixture_rcpp_loop(List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, int const& nu, mat const& V, double s, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, mat oldbetas, vec ind){ // Wayne Taylor 10/01/2014 int nlgt = lgtdata.size(); int nvar = V.n_cols; int nz = Z.n_cols; mat rootpi, betabar, ucholinv, incroot; int mkeep; mnlMetropOnceOut metropout_struct; List lgtdatai, nmix; // convert List to std::vector of struct std::vector lgtdata_vector; moments lgtdatai_struct; for (int lgt = 0; lgt(lgtdatai["y"]); lgtdatai_struct.X = as(lgtdatai["X"]); lgtdatai_struct.hess = as(lgtdatai["hess"]); lgtdata_vector.push_back(lgtdatai_struct); } // allocate space for draws vec oldll = zeros(nlgt); cube betadraw(nlgt, nvar, R/keep); mat probdraw(R/keep, oldprob.size()); vec loglike(R/keep); mat Deltadraw(1,1); if(drawdelta) Deltadraw.zeros(R/keep, nz*nvar);//enlarge Deltadraw only if the space is required List compdraw(R/keep); if (nprint>0) startMcmcTimer(); for (int rep = 0; rep(mgout["p"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> ind = as(mgout["z"]); //now draw delta | {beta_i}, ind, comps if(drawdelta) olddelta = drawDelta(Z,oldbetas,ind,oldcomp,deltabar,Ad); //loop over all LGT equations drawing beta_i | ind[i],z[i,],mu[ind[i]],rooti[ind[i]] for(int lgt = 0; lgt(oldcomplgt[1]); //note: beta_i = Delta*z_i + u_i Delta is nvar x nz if(drawdelta){ olddelta.reshape(nvar,nz); betabar = as(oldcomplgt[0])+olddelta*vectorise(Z(lgt,span::all)); } else { betabar = as(oldcomplgt[0]); } if (rep == 0) oldll[lgt] = llmnl(vectorise(oldbetas(lgt,span::all)),lgtdata_vector[lgt].y,lgtdata_vector[lgt].X); //compute inc.root ucholinv = solve(trimatu(chol(lgtdata_vector[lgt].hess+rootpi*trans(rootpi))), eye(nvar,nvar)); //trimatu interprets the matrix as upper triangular and makes solve more efficient incroot = chol(ucholinv*trans(ucholinv)); metropout_struct = mnlMetropOnce(lgtdata_vector[lgt].y,lgtdata_vector[lgt].X,vectorise(oldbetas(lgt,span::all)), oldll[lgt],s,incroot,betabar,rootpi); oldbetas(lgt,span::all) = trans(metropout_struct.betadraw); oldll[lgt] = metropout_struct.oldll; } //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw.slice(mkeep-1) = oldbetas; probdraw(mkeep-1, span::all) = trans(oldprob); loglike[mkeep-1] = sum(oldll); if(drawdelta) Deltadraw(mkeep-1, span::all) = trans(vectorise(olddelta)); compdraw[mkeep-1] = oldcomp; } } if (nprint>0) endMcmcTimer(); nmix = List::create(Named("probdraw") = probdraw, Named("zdraw") = R_NilValue, //sets the value to NULL in R Named("compdraw") = compdraw); if(drawdelta){ return(List::create( Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("nmix") = nmix, Named("loglike") = loglike)); } else { return(List::create( Named("betadraw") = betadraw, Named("nmix") = nmix, Named("loglike") = loglike)); } } bayesm/src/bayesBLP_rcpp_loop.cpp0000644000176000001440000003623412541203644016604 0ustar ripleyusers#include "bayesm.h" //SUPPORT FUNCTIONS SPECIFIC TO MAIN FUNCTION-------------------------------------------------------------------------------------- mat r2Sigma(vec const& r, int K){ // // Keunwoo Kim 10/28/2014 // // Purpose: // convert r (vector) into Sigma (matrix) // // Arguments: // r : K*(K+1)/2 length vector // K : number of parameters (=nrow(Sigma)) // // Output: // Sigma (K by K matrix) // int k, i, j; mat L = zeros(K, K); L.diag() = exp(r(span(0,K-1))); k = 0; for (i=0; i(J, J); // equivalent to struc = kron(eye(T, T), onesJJ) mat struc = zeros(J*T,J*T); for (t=0; t(T*J, T*J); mat offDiag = -choiceProb*trans(choiceProb)/H; mat Jac = struc%offDiag; Jac.diag() = sum(choiceProb%(1-choiceProb), 1)/H; double sumlogJacob = 0; for (t=0; t(J*T); vec mu1 = mu0/2; //relative increasement vec rel = (mu1 - mu0)/mu0; double max_rel = max(abs(rel)); while (max_rel > tol){ mu0 = mu1; expU = exp(u + mu0*ones(1,H)); temp1 = reshape(expU, J, T*H); expSum = 1 + sum(temp1, 0); expSum = reshape(expSum, T, H); // equivalent to expSum = kron(expSum, ones(J)); for (t=0; t(J)*expSum(t, span::all); } expSum = temp2; choiceProb = expU/expSum; share_hat = sum(choiceProb, 1)/H; mu1 = mu0 + log(share/share_hat); iter = iter + 1; rel = (mu0 - mu1)/mu0; max_rel = max(abs(rel)); } mat rtn = zeros(J*T, H+1); rtn(span::all,0) = mu1; rtn(span::all,span(1,H)) = choiceProb; return (rtn); } List rivDraw(vec const& mu, vec const& Xend, mat const& z, mat const& Xexo, vec const& theta_hat, mat const& A, vec const& deltabar, mat const& Ad, mat const& V, int nu, vec const& delta_old, mat const& Omega_old){ // // Keunwoo Kim 05/21/2015 // // Purpose: draw from posterior for linear I.V. model // // Arguments: // mu is vector of obs on lhs var in structural equation // Xend is "endogenous" var in structural eqn // Xexo is matrix of obs on "exogenous" vars in the structural eqn // z is matrix of obs on instruments // // deltabar is prior mean of delta // Ad is prior prec // theta_hat is prior mean vector for theta2,theta1 // A is prior prec of same // nu,V parms for IW on Omega // // delta_old is the starting value from the previous chain // Omega_old is the starting value from the previous chain // // Output: list of draws of delta,thetabar,Omega // // Model: // Xend=z'delta + e1 // mu=thetabar1*Xend + Xexo'thetabar2 + e2 // e1,e2 ~ N(0,Omega) // // Prior: // delta ~ N(deltabar,Ad^-1) // thetabar = vec(theta2,theta1) ~ N(theta_hat,A^-1) // Omega ~ IW(nu,V) // vec e1, ee2, bg, u, theta2; mat xt, Res, S, B, L, Li, z2, zt1, zt2, ucholinv, VSinv, mut; double sig,theta1; List out; int i; int n = mu.size(); int dimd = z.n_cols; int dimg = Xexo.n_cols; vec thetabar(dimg+1); mat C = eye(2,2); // set initial values mat Omega = Omega_old; vec delta = delta_old; mat xtd(2*n, dimd); vec zvec = vectorise(trans(z)); // // draw beta,gamma // e1 = Xend - z*delta; ee2 = (Omega(0,1)/Omega(0,0)) * e1; sig = sqrt(Omega(1,1)-((Omega(0,1)*Omega(0,1))/Omega(0,0))); mut = (mu-ee2)/sig; xt = join_rows(Xend,Xexo)/sig; bg = breg(mut,xt,theta_hat,A); theta1 = bg[0]; theta2 = bg(span(1,bg.size()-1)); // // draw delta // C(1,0) = theta1; B = C*Omega*trans(C); L = trans(chol(B)); Li = solve(trimatl(L),eye(2,2)); u = mu - Xexo*theta2; mut = vectorise(Li * trans(join_rows(Xend,u))); z2 = trans(join_rows(zvec, theta1*zvec)); z2 = Li*z2; zt1 = z2(0,span::all); zt2 = z2(1,span::all); zt1.reshape(dimd,n); zt1 = trans(zt1); zt2.reshape(dimd,n); zt2 = trans(zt2); for (i=0; i(out["IW"]); thetabar(span(0,dimg-1)) = theta2; thetabar[dimg] = theta1; return List::create( Named("deltadraw") = delta, Named("thetabardraw") = thetabar, Named("Omegadraw") = Omega ); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] List bayesBLP_rcpp_loop(bool IV, mat const& X, mat const& Z, vec const& share, int J, int T, mat const& v, int R, vec const& sigmasqR, mat const& A, vec const& theta_hat, vec const& deltabar, mat const& Ad, int nu0, double s0_sq, mat const& VOmega, double ssq, mat const& cand_cov, vec const& theta_bar_initial, vec const& r_initial, double tau_sq_initial, mat const& Omega_initial, vec const& delta_initial, double tol, int keep, int nprint){ // // Keunwoo Kim 05/21/2015 // // Purpose: // draw theta_bar and Sigma via hybrid Gibbs sampler (Jiang, Manchanda, and Rossi, 2009) // // Arguments: // Observation // IV: whether to use instrumental variable (TRUE or FALSE) // X: J*T by H (If IV is TRUE, the last column is endogenous variable.) // z: instrumental variables (If IV is FALSE, it is not used.) // share: vector of length J*T // // Dimension // J: number of alternatives // T: number of time // R: number of Gibbs sampling // // Prior // sigmasqR // theta_hat // A // deltabar (used when IV is TRUE) // Ad (used when IV is TRUE) // nu0 // s0_sq (used when IV is FALSE) // VOmega (used when IV is TRUE) // // Metropolis-Hastings // ssq: scaling parameter // cand_cov: var-cov matrix of random walk // // Initial values // theta_bar_initial // r_initial // tau_sq_initial (used when IV is FALSE) // Omega_initial (used when IV is TRUE) // delta_initial (used when IV is TRUE) // // Contraction mapping // tol: convergence tolerance for the contraction mapping // v: draws used for Monte-Carlo integration // // Output: // a List of theta_bar, r (Sigma), tau_sq, Omega, and delta draws // number of acceptance and loglikelihood // // Model & Prior: // shown in the below comments. int nu1, mkeep, I, jt; mat prob_t, Sigma_new, b, S, Sigma, Sigma_inv, rel, expU, share_hat, choiceProb, expSum, L, ucholinv, XXAinv, out_cont, Xexo, Xend, Omega_all, delta_all, zetaeta_old, zetaeta_new, rootiOmega; vec r_new, mu_new, theta_tilde, z, mu, err, mu0, mu1, eta_new, eta_old, tau_sq_all, zeta; double alpha, ll_new, ll_old, sumLogJaco_new, prior_new, prior_old, s1_sq, acceptrate; List ivout; double pi = M_PI; int K = theta_hat.size(); if (IV==TRUE){ Xexo = X(span::all, span(0,K-2)); Xend = X(span::all, K-1); I = Z.n_cols; } // number of MC integration draws int H = v.n_cols; // Allocate matrix for draws to be stored during MCMC if (IV==TRUE){ Omega_all = zeros(4,R/keep); delta_all = zeros(I,R/keep); }else{ tau_sq_all = zeros(R/keep); } mat theta_bar_all = zeros(K,R/keep); mat r_all = zeros(K*(K+1)/2,R/keep); mat Sigma_all = zeros(K*K,R/keep); vec ll_all = zeros(R/keep); // list to be returned to R List rtn; // initial values vec theta_bar = theta_bar_initial; mat Omega = Omega_initial; vec delta = delta_initial; vec r_old = r_initial; double tau_sq = tau_sq_initial; mat Sigma_old = r2Sigma(r_old, K); //=================================================================== // get initial mu and sumLogJaco: Contraction Mapping //=================================================================== // convert shares into mu out_cont = share2mu(Sigma_old, X, v, share, J, tol); mu = out_cont(span::all,0); choiceProb = out_cont(span::all,span(1,H)); // Jacobian double sumLogJaco_old = logJacob(choiceProb, J); vec mu_old = mu; //=================================================================== // Start MCMC //=================================================================== if (nprint>0) startMcmcTimer(); double n_accept = 0.0; for (int rep=0; rep(K*(K+1)/2); Sigma_new = r2Sigma(r_new, K); // convert share into mu_new out_cont = share2mu(Sigma_new, X, v, share, J, tol); mu_new = out_cont(span::all,0); choiceProb = out_cont(span::all,span(1,H)); // get eta_new eta_new = mu_new - X*theta_bar; // get eta_old eta_old = mu_old - X*theta_bar; if (IV==TRUE){ // get zeta zeta = Xend - Z*delta; // get ll_old zetaeta_old = join_rows(zeta, eta_old); rootiOmega = solve(trimatu(chol(Omega)), eye(2,2)); ll_old = 0; for (jt=0; jt(2), rootiOmega); } ll_old = ll_old + sumLogJaco_old; // get ll_new zetaeta_new = join_rows(zeta, eta_new); sumLogJaco_new = logJacob(choiceProb, J); ll_new = 0; for (jt=0; jt(2), rootiOmega); } ll_new = ll_new + sumLogJaco_new; }else{ // get ll_old ll_old = sum(log((1/sqrt(2*pi*tau_sq)) * exp(-(eta_old%eta_old)/(2*tau_sq)))) + sumLogJaco_old; // get ll_new sumLogJaco_new = logJacob(choiceProb, J); ll_new = sum(log((1/sqrt(2*pi*tau_sq)) * exp(-(eta_new%eta_new)/(2*tau_sq)))) + sumLogJaco_new; } // priors prior_new = sum(log((1/sqrt(2*pi*sigmasqR)) % exp(-(r_new%r_new)/(2*sigmasqR)))); prior_old = sum(log((1/sqrt(2*pi*sigmasqR)) % exp(-(r_old%r_old)/(2*sigmasqR)))); alpha = exp(ll_new + prior_new - ll_old - prior_old); if (alpha>1) {alpha = 1;} if (runif(1)[0]<=alpha) { r_old = r_new; Sigma_old = Sigma_new; mu_old = mu_new; sumLogJaco_old = sumLogJaco_new; n_accept = n_accept + 1; } //======================================================================== // STEP 2 // Draw theta_bar & tau^2 (or Omega & delta): Gibbs Sampler // mu = X*theta_bar + eta, eta~N(0,tau_sq) // (For IV case, see the comments in rivDraw above.) // Prior: // 1. theta_bar ~ N(theta_hat, A^-1) // 2. tau_sq ~ nu0*s0_sq/chisq(nu0) // Posterior: // 1. theta_bar | tau_sq ~ N(theta_tilde, (X^t X/tau_sq + A)^-1) // theta_tilde = (X^t X/tau_sq + A)^-1 * (tau_sq^-1*X^t mu + A*theta_hat) // 2. tau_sq | theta_bar ~ nu1*s1_sq/chisq(nu1) // nu1 = nu0 + n (n=J*T) // s1_sq = [nu0*s0_sq + (mu-X^t theta_bar)^t (mu-X^t theta_bar)]/[nu0 + n] //======================================================================== if (IV==TRUE){ ivout = rivDraw(mu_old, Xend, Z, Xexo, theta_hat, A, deltabar, Ad, VOmega, nu0, delta, Omega); delta = as(ivout["deltadraw"]); theta_bar = as(ivout["thetabardraw"]); Omega = as(ivout["Omegadraw"]); }else{ // compute the inverse of (trans(X)*X)/tau_sq + A ucholinv = solve(trimatu(chol((trans(X)*X)/tau_sq + A)), eye(K,K)); XXAinv = ucholinv*trans(ucholinv); theta_tilde = XXAinv * (trans(X)*mu_old/tau_sq + A*theta_hat); theta_bar = theta_tilde + ucholinv*vec(rnorm(K)); nu1 = nu0 + J*T; err = mu_old - X*theta_bar; s1_sq = (nu0*s0_sq + sum(err%err))/nu1; z = vec(rnorm(nu1)); tau_sq = nu1*s1_sq/sum(z%z); } // // print time to completion and draw # every nprint'th draw // if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //======================================================================== // STEP 3 // Store Draws //======================================================================== if((rep+1)%keep==0){ mkeep = (rep+1)/keep; if (IV==TRUE){ Omega_all(span::all,mkeep-1) = vectorise(Omega); delta_all(span::all,mkeep-1) = delta; }else{ tau_sq_all[mkeep-1] = tau_sq; } theta_bar_all(span::all,mkeep-1) = theta_bar; r_all(span::all,mkeep-1) = r_old; Sigma_all(span::all,mkeep-1) = vectorise(r2Sigma(r_old, K)); ll_all[mkeep-1] = ll_old; } } acceptrate = n_accept/R; rtn["tausqdraw"] = tau_sq_all; rtn["Omegadraw"] = Omega_all; rtn["deltadraw"] = delta_all; rtn["thetabardraw"] = theta_bar_all; rtn["rdraw"] = r_all; rtn["Sigmadraw"] = Sigma_all; rtn["ll"] = ll_all; rtn["acceptrate"] = acceptrate; if (nprint>0) endMcmcTimer(); return (rtn); } bayesm/src/ghkvec_rcpp.cpp0000644000176000001440000001175612541203644015363 0ustar ripleyusers#include "bayesm.h" //SUPPORT FUNCTIONS SPECIFIC TO MAIN FUNCTION-------------------------------------------------------------------------------------- vec HaltonSeq(int pn, int r, int burnin, bool rand){ // Keunwoo Kim 10/28/2014 // Purpose: // create a random Halton sequence // Arguments: // pn: prime number // r: number of draws // burnin: number of initial burn // rand: if TRUE, add a random scalor to sequence // Output: // a vector of Halton sequence, size r int t; vec add; // start at 0 vec seq = zeros(r+burnin+1); // how many numbers I have drawn so far. // I have 1 draw (0) now. int index = 1; // if done==1, it is done. int done = 0; int factor = pn; do{ for (t=0; t(index)*(t+1)/factor; if ((t+2)*index-1>r+burnin){ seq(span((t+1)*index, r+burnin)) = add(span(0, r+burnin-(t+1)*index)); done = 1; }else{ seq(span((t+1)*index, (t+2)*index-1)) = add; if ((t+2)*index==r+burnin+1){ done = 1; } } } } factor = factor*pn; index = index*pn; }while (done==0); // exclude the first 0 and some initial draws seq = seq(span(burnin+1,burnin+r)); if (rand==TRUE){ // make it random seq = seq + runif(1)[0]; for (int i=0; i=1) seq[i] = seq[i]-1; } } return (seq); } bool IsPrime(int number){ // Keunwoo Kim 5/14/2015 // This function is to check whether a number is prime or not. // This is used for setting default prime numbers. for (int f=2; f(dim); double res = 0; // choose R::runif vs. Halton draws vec udraw(r*dim); mat udrawHalton(dim, r); if (HALTON){ for (j=0; j0){ pa = 0.0; pb = R::pnorm(tpz,0,1,1,0); }else{ pb = 1.0; pa = R::pnorm(tpz,0,1,1,0); } prod = prod * (pb-pa); u = udraw[i*dim+j]; arg = u*pb + (1.0-u)*pa; if (arg > .999999999) arg=.999999999; if (arg < .0000000001) arg=.0000000001; z[j] = R::qnorm(arg,0,1,1,0); } res = res + prod; } res = res / r; return (res); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] vec ghkvec(mat const& L, vec const& trunpt, vec const& above, int r, bool HALTON=true, vec pn=IntegerVector::create(0)){ // Keunwoo Kim 5/14/2015 // Purpose: // routine to call ghk_oneside for n different truncation points stacked in to the // vector trunpt -- puts n results in vector res // Arguments: // L: lower Cholesky root of cov. matrix // trunpt: vector of truncation points // above: truncation above(1) or below(0) // r: number of draws // HALTON: TRUE or FALSE. If FALSE, use R::runif random number generator. // pn: prime number used in Halton sequence. // burnin: number of initial burnin of draws. Only applied when HALTON is TRUE. (not used any more) // Output: // a vector of integration values int dim = above.size(); int n = trunpt.size()/dim; // // handling default arguments // // generate prime numbers if (HALTON==true && pn[0]==0){ Rcout << "Halton sequence is generated by the smallest prime numbers: \n"; Rcout << " "; pn = zeros(dim); int cand = 2; int which = 0; while (pn[dim-1]==0){ if (IsPrime(cand)){ pn[which] = cand; which = which + 1; Rprintf("%d ", cand); } cand = cand + 1; } Rcout << "\n"; } // burn-in //if (HALTON==true && burnin==NA_INTEGER){ // burnin = max(pn); // Rprintf("Initial %d (= max of prime numbers) draws are burned. \n", burnin); //} int burnin = 0; vec res(n); for (int i=0; i(dimd); if (nprint>0) startMcmcTimer(); mat xtd(2*n, dimd); vec zvec = vectorise(trans(z)); // start main iteration loop for (int rep=0; rep(out["IW"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> // print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; deltadraw(mkeep-1, span::all) = trans(delta); betadraw[mkeep-1] = beta; gammadraw(mkeep-1, span::all) = trans(gamma); Sigmadraw(mkeep-1, span::all) = trans(vectorise(Sigma)); } } if (nprint>0) endMcmcTimer(); return List::create( Named("deltadraw") = deltadraw, Named("betadraw") = NumericVector(betadraw.begin(),betadraw.end()), Named("gammadraw") = gammadraw, Named("Sigmadraw") = Sigmadraw); } bayesm/src/rmultireg_rcpp.cpp0000644000176000001440000000427212541203644016121 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rmultireg(mat const& Y, mat const& X, mat const& Bbar, mat const& A, int nu, mat const& V) { // Keunwoo Kim 09/09/2014 // Purpose: draw from posterior for Multivariate Regression Model with natural conjugate prior // Arguments: // Y is n x m matrix // X is n x k // Bbar is the prior mean of regression coefficients (k x m) // A is prior precision matrix // nu, V are parameters for prior on Sigma // Output: list of B, Sigma draws of matrix of coefficients and Sigma matrix // Model: // Y=XB+U cov(u_i) = Sigma // B is k x m matrix of coefficients // Prior: // beta|Sigma ~ N(betabar,Sigma (x) A^-1) // betabar=vec(Bbar) // beta = vec(B) // Sigma ~ IW(nu,V) or Sigma^-1 ~ W(nu, V^-1) int n = Y.n_rows; int m = Y.n_cols; int k = X.n_cols; //first draw Sigma mat RA = chol(A); mat W = join_cols(X, RA); //analogous to rbind() in R mat Z = join_cols(Y, RA*Bbar); // note: Y,X,A,Bbar must be matrices! mat IR = solve(trimatu(chol(trans(W)*W)), eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient // W'W = R'R & (W'W)^-1 = IRIR' -- this is the UL decomp! mat Btilde = (IR*trans(IR)) * (trans(W)*Z); // IRIR'(W'Z) = (X'X+A)^-1(X'Y + ABbar) mat E = Z-W*Btilde; mat S = trans(E)*E; // E'E // compute the inverse of V+S mat ucholinv = solve(trimatu(chol(V+S)), eye(m,m)); mat VSinv = ucholinv*trans(ucholinv); List rwout = rwishart(nu+n, VSinv); // now draw B given Sigma // note beta ~ N(vec(Btilde),Sigma (x) Covxxa) // Cov=(X'X + A)^-1 = IR t(IR) // Sigma=CICI' // therefore, cov(beta)= Omega = CICI' (x) IR IR' = (CI (x) IR) (CI (x) IR)' // so to draw beta we do beta= vec(Btilde) +(CI (x) IR)vec(Z_mk) // Z_mk is m x k matrix of N(0,1) // since vec(ABC) = (C' (x) A)vec(B), we have // B = Btilde + IR Z_mk CI' mat CI = rwout["CI"]; //there is no need to use as(rwout["CI"]) since CI is being initiated as a mat in the same line mat draw = mat(rnorm(k*m)); draw.reshape(k,m); mat B = Btilde + IR*draw*trans(CI); return List::create( Named("B") = B, Named("Sigma") = rwout["IW"]); } bayesm/src/rcppexports.cpp0000644000176000001440000013473612541203644015465 0ustar ripleyusers// This file was generated by Rcpp::compileAttributes // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include "../inst/include/bayesm.h" #include #include using namespace Rcpp; // bayesBLP_rcpp_loop List bayesBLP_rcpp_loop(bool IV, mat const& X, mat const& Z, vec const& share, int J, int T, mat const& v, int R, vec const& sigmasqR, mat const& A, vec const& theta_hat, vec const& deltabar, mat const& Ad, int nu0, double s0_sq, mat const& VOmega, double ssq, mat const& cand_cov, vec const& theta_bar_initial, vec const& r_initial, double tau_sq_initial, mat const& Omega_initial, vec const& delta_initial, double tol, int keep, int nprint); RcppExport SEXP bayesm_bayesBLP_rcpp_loop(SEXP IVSEXP, SEXP XSEXP, SEXP ZSEXP, SEXP shareSEXP, SEXP JSEXP, SEXP TSEXP, SEXP vSEXP, SEXP RSEXP, SEXP sigmasqRSEXP, SEXP ASEXP, SEXP theta_hatSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP nu0SEXP, SEXP s0_sqSEXP, SEXP VOmegaSEXP, SEXP ssqSEXP, SEXP cand_covSEXP, SEXP theta_bar_initialSEXP, SEXP r_initialSEXP, SEXP tau_sq_initialSEXP, SEXP Omega_initialSEXP, SEXP delta_initialSEXP, SEXP tolSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< bool >::type IV(IVSEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type share(shareSEXP); Rcpp::traits::input_parameter< int >::type J(JSEXP); Rcpp::traits::input_parameter< int >::type T(TSEXP); Rcpp::traits::input_parameter< mat const& >::type v(vSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< vec const& >::type sigmasqR(sigmasqRSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< vec const& >::type theta_hat(theta_hatSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< int >::type nu0(nu0SEXP); Rcpp::traits::input_parameter< double >::type s0_sq(s0_sqSEXP); Rcpp::traits::input_parameter< mat const& >::type VOmega(VOmegaSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< mat const& >::type cand_cov(cand_covSEXP); Rcpp::traits::input_parameter< vec const& >::type theta_bar_initial(theta_bar_initialSEXP); Rcpp::traits::input_parameter< vec const& >::type r_initial(r_initialSEXP); Rcpp::traits::input_parameter< double >::type tau_sq_initial(tau_sq_initialSEXP); Rcpp::traits::input_parameter< mat const& >::type Omega_initial(Omega_initialSEXP); Rcpp::traits::input_parameter< vec const& >::type delta_initial(delta_initialSEXP); Rcpp::traits::input_parameter< double >::type tol(tolSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(bayesBLP_rcpp_loop(IV, X, Z, share, J, T, v, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, ssq, cand_cov, theta_bar_initial, r_initial, tau_sq_initial, Omega_initial, delta_initial, tol, keep, nprint)); return __result; END_RCPP } // breg vec breg(vec const& y, mat const& X, vec const& betabar, mat const& A); RcppExport SEXP bayesm_breg(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); __result = Rcpp::wrap(breg(y, X, betabar, A)); return __result; END_RCPP } // cgetC vec cgetC(double e, int k); RcppExport SEXP bayesm_cgetC(SEXP eSEXP, SEXP kSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< double >::type e(eSEXP); Rcpp::traits::input_parameter< int >::type k(kSEXP); __result = Rcpp::wrap(cgetC(e, k)); return __result; END_RCPP } // clusterMix_rcpp_loop List clusterMix_rcpp_loop(mat const& zdraw, double cutoff, bool SILENT, int nprint); RcppExport SEXP bayesm_clusterMix_rcpp_loop(SEXP zdrawSEXP, SEXP cutoffSEXP, SEXP SILENTSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< mat const& >::type zdraw(zdrawSEXP); Rcpp::traits::input_parameter< double >::type cutoff(cutoffSEXP); Rcpp::traits::input_parameter< bool >::type SILENT(SILENTSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(clusterMix_rcpp_loop(zdraw, cutoff, SILENT, nprint)); return __result; END_RCPP } // ghkvec vec ghkvec(mat const& L, vec const& trunpt, vec const& above, int r, bool HALTON, vec pn); RcppExport SEXP bayesm_ghkvec(SEXP LSEXP, SEXP trunptSEXP, SEXP aboveSEXP, SEXP rSEXP, SEXP HALTONSEXP, SEXP pnSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< mat const& >::type L(LSEXP); Rcpp::traits::input_parameter< vec const& >::type trunpt(trunptSEXP); Rcpp::traits::input_parameter< vec const& >::type above(aboveSEXP); Rcpp::traits::input_parameter< int >::type r(rSEXP); Rcpp::traits::input_parameter< bool >::type HALTON(HALTONSEXP); Rcpp::traits::input_parameter< vec >::type pn(pnSEXP); __result = Rcpp::wrap(ghkvec(L, trunpt, above, r, HALTON, pn)); return __result; END_RCPP } // llmnl double llmnl(vec const& beta, vec const& y, mat const& X); RcppExport SEXP bayesm_llmnl(SEXP betaSEXP, SEXP ySEXP, SEXP XSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type beta(betaSEXP); Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); __result = Rcpp::wrap(llmnl(beta, y, X)); return __result; END_RCPP } // lndIChisq mat lndIChisq(double nu, double ssq, mat const& X); RcppExport SEXP bayesm_lndIChisq(SEXP nuSEXP, SEXP ssqSEXP, SEXP XSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); __result = Rcpp::wrap(lndIChisq(nu, ssq, X)); return __result; END_RCPP } // lndIWishart double lndIWishart(double nu, mat const& V, mat const& IW); RcppExport SEXP bayesm_lndIWishart(SEXP nuSEXP, SEXP VSEXP, SEXP IWSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< mat const& >::type IW(IWSEXP); __result = Rcpp::wrap(lndIWishart(nu, V, IW)); return __result; END_RCPP } // lndMvn double lndMvn(vec const& x, vec const& mu, mat const& rooti); RcppExport SEXP bayesm_lndMvn(SEXP xSEXP, SEXP muSEXP, SEXP rootiSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< vec const& >::type mu(muSEXP); Rcpp::traits::input_parameter< mat const& >::type rooti(rootiSEXP); __result = Rcpp::wrap(lndMvn(x, mu, rooti)); return __result; END_RCPP } // lndMvst double lndMvst(vec const& x, int nu, vec const& mu, mat const& rooti, bool NORMC); RcppExport SEXP bayesm_lndMvst(SEXP xSEXP, SEXP nuSEXP, SEXP muSEXP, SEXP rootiSEXP, SEXP NORMCSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type mu(muSEXP); Rcpp::traits::input_parameter< mat const& >::type rooti(rootiSEXP); Rcpp::traits::input_parameter< bool >::type NORMC(NORMCSEXP); __result = Rcpp::wrap(lndMvst(x, nu, mu, rooti, NORMC)); return __result; END_RCPP } // rbprobitGibbs_rcpp_loop List rbprobitGibbs_rcpp_loop(vec const& y, mat const& X, vec const& Abetabar, mat const& root, vec beta, vec const& sigma, vec const& a, vec const& b, int R, int keep, int nprint); RcppExport SEXP bayesm_rbprobitGibbs_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP AbetabarSEXP, SEXP rootSEXP, SEXP betaSEXP, SEXP sigmaSEXP, SEXP aSEXP, SEXP bSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type Abetabar(AbetabarSEXP); Rcpp::traits::input_parameter< mat const& >::type root(rootSEXP); Rcpp::traits::input_parameter< vec >::type beta(betaSEXP); Rcpp::traits::input_parameter< vec const& >::type sigma(sigmaSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec const& >::type b(bSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(rbprobitGibbs_rcpp_loop(y, X, Abetabar, root, beta, sigma, a, b, R, keep, nprint)); return __result; END_RCPP } // rdirichlet vec rdirichlet(vec const& alpha); RcppExport SEXP bayesm_rdirichlet(SEXP alphaSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type alpha(alphaSEXP); __result = Rcpp::wrap(rdirichlet(alpha)); return __result; END_RCPP } // rDPGibbs_rcpp_loop List rDPGibbs_rcpp_loop(int R, int keep, int nprint, mat y, List const& lambda_hyper, bool SCALE, int maxuniq, List const& PrioralphaList, int gridsize, double BayesmConstantA, int BayesmConstantnuInc, double BayesmConstantDPalpha); RcppExport SEXP bayesm_rDPGibbs_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP ySEXP, SEXP lambda_hyperSEXP, SEXP SCALESEXP, SEXP maxuniqSEXP, SEXP PrioralphaListSEXP, SEXP gridsizeSEXP, SEXP BayesmConstantASEXP, SEXP BayesmConstantnuIncSEXP, SEXP BayesmConstantDPalphaSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< mat >::type y(ySEXP); Rcpp::traits::input_parameter< List const& >::type lambda_hyper(lambda_hyperSEXP); Rcpp::traits::input_parameter< bool >::type SCALE(SCALESEXP); Rcpp::traits::input_parameter< int >::type maxuniq(maxuniqSEXP); Rcpp::traits::input_parameter< List const& >::type PrioralphaList(PrioralphaListSEXP); Rcpp::traits::input_parameter< int >::type gridsize(gridsizeSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantA(BayesmConstantASEXP); Rcpp::traits::input_parameter< int >::type BayesmConstantnuInc(BayesmConstantnuIncSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantDPalpha(BayesmConstantDPalphaSEXP); __result = Rcpp::wrap(rDPGibbs_rcpp_loop(R, keep, nprint, y, lambda_hyper, SCALE, maxuniq, PrioralphaList, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha)); return __result; END_RCPP } // rhierLinearMixture_rcpp_loop List rhierLinearMixture_rcpp_loop(List const& regdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, int const& nu, mat const& V, int nu_e, vec const& ssq, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, vec ind, vec tau); RcppExport SEXP bayesm_rhierLinearMixture_rcpp_loop(SEXP regdataSEXP, SEXP ZSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP mubarSEXP, SEXP AmuSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP nu_eSEXP, SEXP ssqSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP drawdeltaSEXP, SEXP olddeltaSEXP, SEXP aSEXP, SEXP oldprobSEXP, SEXP indSEXP, SEXP tauSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< mat const& >::type mubar(mubarSEXP); Rcpp::traits::input_parameter< mat const& >::type Amu(AmuSEXP); Rcpp::traits::input_parameter< int const& >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< int >::type nu_e(nu_eSEXP); Rcpp::traits::input_parameter< vec const& >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< bool >::type drawdelta(drawdeltaSEXP); Rcpp::traits::input_parameter< mat >::type olddelta(olddeltaSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec >::type oldprob(oldprobSEXP); Rcpp::traits::input_parameter< vec >::type ind(indSEXP); Rcpp::traits::input_parameter< vec >::type tau(tauSEXP); __result = Rcpp::wrap(rhierLinearMixture_rcpp_loop(regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, drawdelta, olddelta, a, oldprob, ind, tau)); return __result; END_RCPP } // rhierLinearModel_rcpp_loop List rhierLinearModel_rcpp_loop(List const& regdata, mat const& Z, mat const& Deltabar, mat const& A, int nu, mat const& V, int nu_e, vec const& ssq, vec tau, mat Delta, mat Vbeta, int R, int keep, int nprint); RcppExport SEXP bayesm_rhierLinearModel_rcpp_loop(SEXP regdataSEXP, SEXP ZSEXP, SEXP DeltabarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP, SEXP nu_eSEXP, SEXP ssqSEXP, SEXP tauSEXP, SEXP DeltaSEXP, SEXP VbetaSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< mat const& >::type Deltabar(DeltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< int >::type nu_e(nu_eSEXP); Rcpp::traits::input_parameter< vec const& >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< vec >::type tau(tauSEXP); Rcpp::traits::input_parameter< mat >::type Delta(DeltaSEXP); Rcpp::traits::input_parameter< mat >::type Vbeta(VbetaSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(rhierLinearModel_rcpp_loop(regdata, Z, Deltabar, A, nu, V, nu_e, ssq, tau, Delta, Vbeta, R, keep, nprint)); return __result; END_RCPP } // rhierMnlDP_rcpp_loop List rhierMnlDP_rcpp_loop(int R, int keep, int nprint, List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, List const& PrioralphaList, List const& lambda_hyper, bool drawdelta, int nvar, mat oldbetas, double s, int maxuniq, int gridsize, double BayesmConstantA, int BayesmConstantnuInc, double BayesmConstantDPalpha); RcppExport SEXP bayesm_rhierMnlDP_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP lgtdataSEXP, SEXP ZSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP PrioralphaListSEXP, SEXP lambda_hyperSEXP, SEXP drawdeltaSEXP, SEXP nvarSEXP, SEXP oldbetasSEXP, SEXP sSEXP, SEXP maxuniqSEXP, SEXP gridsizeSEXP, SEXP BayesmConstantASEXP, SEXP BayesmConstantnuIncSEXP, SEXP BayesmConstantDPalphaSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< List const& >::type lgtdata(lgtdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< List const& >::type PrioralphaList(PrioralphaListSEXP); Rcpp::traits::input_parameter< List const& >::type lambda_hyper(lambda_hyperSEXP); Rcpp::traits::input_parameter< bool >::type drawdelta(drawdeltaSEXP); Rcpp::traits::input_parameter< int >::type nvar(nvarSEXP); Rcpp::traits::input_parameter< mat >::type oldbetas(oldbetasSEXP); Rcpp::traits::input_parameter< double >::type s(sSEXP); Rcpp::traits::input_parameter< int >::type maxuniq(maxuniqSEXP); Rcpp::traits::input_parameter< int >::type gridsize(gridsizeSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantA(BayesmConstantASEXP); Rcpp::traits::input_parameter< int >::type BayesmConstantnuInc(BayesmConstantnuIncSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantDPalpha(BayesmConstantDPalphaSEXP); __result = Rcpp::wrap(rhierMnlDP_rcpp_loop(R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha)); return __result; END_RCPP } // rhierMnlRwMixture_rcpp_loop List rhierMnlRwMixture_rcpp_loop(List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, int const& nu, mat const& V, double s, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, mat oldbetas, vec ind); RcppExport SEXP bayesm_rhierMnlRwMixture_rcpp_loop(SEXP lgtdataSEXP, SEXP ZSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP mubarSEXP, SEXP AmuSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP sSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP drawdeltaSEXP, SEXP olddeltaSEXP, SEXP aSEXP, SEXP oldprobSEXP, SEXP oldbetasSEXP, SEXP indSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< List const& >::type lgtdata(lgtdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< mat const& >::type mubar(mubarSEXP); Rcpp::traits::input_parameter< mat const& >::type Amu(AmuSEXP); Rcpp::traits::input_parameter< int const& >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type s(sSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< bool >::type drawdelta(drawdeltaSEXP); Rcpp::traits::input_parameter< mat >::type olddelta(olddeltaSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec >::type oldprob(oldprobSEXP); Rcpp::traits::input_parameter< mat >::type oldbetas(oldbetasSEXP); Rcpp::traits::input_parameter< vec >::type ind(indSEXP); __result = Rcpp::wrap(rhierMnlRwMixture_rcpp_loop(lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind)); return __result; END_RCPP } // rhierNegbinRw_rcpp_loop List rhierNegbinRw_rcpp_loop(List const& regdata, List const& hessdata, mat const& Z, mat Beta, mat Delta, mat const& Deltabar, mat const& Adelta, int nu, mat const& V, double a, double b, int R, int keep, double sbeta, double alphacroot, int nprint, mat rootA, double alpha, bool fixalpha); RcppExport SEXP bayesm_rhierNegbinRw_rcpp_loop(SEXP regdataSEXP, SEXP hessdataSEXP, SEXP ZSEXP, SEXP BetaSEXP, SEXP DeltaSEXP, SEXP DeltabarSEXP, SEXP AdeltaSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP aSEXP, SEXP bSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP sbetaSEXP, SEXP alphacrootSEXP, SEXP nprintSEXP, SEXP rootASEXP, SEXP alphaSEXP, SEXP fixalphaSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< List const& >::type hessdata(hessdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< mat >::type Beta(BetaSEXP); Rcpp::traits::input_parameter< mat >::type Delta(DeltaSEXP); Rcpp::traits::input_parameter< mat const& >::type Deltabar(DeltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Adelta(AdeltaSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type a(aSEXP); Rcpp::traits::input_parameter< double >::type b(bSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< double >::type sbeta(sbetaSEXP); Rcpp::traits::input_parameter< double >::type alphacroot(alphacrootSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< mat >::type rootA(rootASEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< bool >::type fixalpha(fixalphaSEXP); __result = Rcpp::wrap(rhierNegbinRw_rcpp_loop(regdata, hessdata, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, sbeta, alphacroot, nprint, rootA, alpha, fixalpha)); return __result; END_RCPP } // rivDP_rcpp_loop List rivDP_rcpp_loop(int R, int keep, int nprint, int dimd, vec const& mbg, mat const& Abg, vec const& md, mat const& Ad, vec const& y, bool isgamma, mat const& z, vec const& x, mat const& w, vec delta, List const& PrioralphaList, int gridsize, bool SCALE, int maxuniq, double scalex, double scaley, List const& lambda_hyper, double BayesmConstantA, int BayesmConstantnu); RcppExport SEXP bayesm_rivDP_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP dimdSEXP, SEXP mbgSEXP, SEXP AbgSEXP, SEXP mdSEXP, SEXP AdSEXP, SEXP ySEXP, SEXP isgammaSEXP, SEXP zSEXP, SEXP xSEXP, SEXP wSEXP, SEXP deltaSEXP, SEXP PrioralphaListSEXP, SEXP gridsizeSEXP, SEXP SCALESEXP, SEXP maxuniqSEXP, SEXP scalexSEXP, SEXP scaleySEXP, SEXP lambda_hyperSEXP, SEXP BayesmConstantASEXP, SEXP BayesmConstantnuSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< int >::type dimd(dimdSEXP); Rcpp::traits::input_parameter< vec const& >::type mbg(mbgSEXP); Rcpp::traits::input_parameter< mat const& >::type Abg(AbgSEXP); Rcpp::traits::input_parameter< vec const& >::type md(mdSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< bool >::type isgamma(isgammaSEXP); Rcpp::traits::input_parameter< mat const& >::type z(zSEXP); Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< mat const& >::type w(wSEXP); Rcpp::traits::input_parameter< vec >::type delta(deltaSEXP); Rcpp::traits::input_parameter< List const& >::type PrioralphaList(PrioralphaListSEXP); Rcpp::traits::input_parameter< int >::type gridsize(gridsizeSEXP); Rcpp::traits::input_parameter< bool >::type SCALE(SCALESEXP); Rcpp::traits::input_parameter< int >::type maxuniq(maxuniqSEXP); Rcpp::traits::input_parameter< double >::type scalex(scalexSEXP); Rcpp::traits::input_parameter< double >::type scaley(scaleySEXP); Rcpp::traits::input_parameter< List const& >::type lambda_hyper(lambda_hyperSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantA(BayesmConstantASEXP); Rcpp::traits::input_parameter< int >::type BayesmConstantnu(BayesmConstantnuSEXP); __result = Rcpp::wrap(rivDP_rcpp_loop(R, keep, nprint, dimd, mbg, Abg, md, Ad, y, isgamma, z, x, w, delta, PrioralphaList, gridsize, SCALE, maxuniq, scalex, scaley, lambda_hyper, BayesmConstantA, BayesmConstantnu)); return __result; END_RCPP } // rivGibbs_rcpp_loop List rivGibbs_rcpp_loop(vec const& y, vec const& x, mat const& z, mat const& w, vec const& mbg, mat const& Abg, vec const& md, mat const& Ad, mat const& V, int nu, int R, int keep, int nprint); RcppExport SEXP bayesm_rivGibbs_rcpp_loop(SEXP ySEXP, SEXP xSEXP, SEXP zSEXP, SEXP wSEXP, SEXP mbgSEXP, SEXP AbgSEXP, SEXP mdSEXP, SEXP AdSEXP, SEXP VSEXP, SEXP nuSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< mat const& >::type z(zSEXP); Rcpp::traits::input_parameter< mat const& >::type w(wSEXP); Rcpp::traits::input_parameter< vec const& >::type mbg(mbgSEXP); Rcpp::traits::input_parameter< mat const& >::type Abg(AbgSEXP); Rcpp::traits::input_parameter< vec const& >::type md(mdSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(rivGibbs_rcpp_loop(y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint)); return __result; END_RCPP } // rmixGibbs List rmixGibbs(mat const& y, mat const& Bbar, mat const& A, int nu, mat const& V, vec const& a, vec const& p, vec const& z); RcppExport SEXP bayesm_rmixGibbs(SEXP ySEXP, SEXP BbarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP, SEXP aSEXP, SEXP pSEXP, SEXP zSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< mat const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type Bbar(BbarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec const& >::type p(pSEXP); Rcpp::traits::input_parameter< vec const& >::type z(zSEXP); __result = Rcpp::wrap(rmixGibbs(y, Bbar, A, nu, V, a, p, z)); return __result; END_RCPP } // rmixture List rmixture(int n, vec pvec, List comps); RcppExport SEXP bayesm_rmixture(SEXP nSEXP, SEXP pvecSEXP, SEXP compsSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type n(nSEXP); Rcpp::traits::input_parameter< vec >::type pvec(pvecSEXP); Rcpp::traits::input_parameter< List >::type comps(compsSEXP); __result = Rcpp::wrap(rmixture(n, pvec, comps)); return __result; END_RCPP } // rmnlIndepMetrop_rcpp_loop List rmnlIndepMetrop_rcpp_loop(int R, int keep, int nu, vec const& betastar, mat const& root, vec const& y, mat const& X, vec const& betabar, mat const& rootpi, mat const& rooti, double oldlimp, double oldlpost, int nprint); RcppExport SEXP bayesm_rmnlIndepMetrop_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nuSEXP, SEXP betastarSEXP, SEXP rootSEXP, SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP rootpiSEXP, SEXP rootiSEXP, SEXP oldlimpSEXP, SEXP oldlpostSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type betastar(betastarSEXP); Rcpp::traits::input_parameter< mat const& >::type root(rootSEXP); Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type rootpi(rootpiSEXP); Rcpp::traits::input_parameter< mat const& >::type rooti(rootiSEXP); Rcpp::traits::input_parameter< double >::type oldlimp(oldlimpSEXP); Rcpp::traits::input_parameter< double >::type oldlpost(oldlpostSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(rmnlIndepMetrop_rcpp_loop(R, keep, nu, betastar, root, y, X, betabar, rootpi, rooti, oldlimp, oldlpost, nprint)); return __result; END_RCPP } // rmnpGibbs_rcpp_loop List rmnpGibbs_rcpp_loop(int R, int keep, int nprint, int pm1, ivec const& y, mat const& X, vec const& beta0, mat const& sigma0, mat const& V, int nu, vec const& betabar, mat const& A); RcppExport SEXP bayesm_rmnpGibbs_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP pm1SEXP, SEXP ySEXP, SEXP XSEXP, SEXP beta0SEXP, SEXP sigma0SEXP, SEXP VSEXP, SEXP nuSEXP, SEXP betabarSEXP, SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< int >::type pm1(pm1SEXP); Rcpp::traits::input_parameter< ivec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type beta0(beta0SEXP); Rcpp::traits::input_parameter< mat const& >::type sigma0(sigma0SEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); __result = Rcpp::wrap(rmnpGibbs_rcpp_loop(R, keep, nprint, pm1, y, X, beta0, sigma0, V, nu, betabar, A)); return __result; END_RCPP } // rmultireg List rmultireg(mat const& Y, mat const& X, mat const& Bbar, mat const& A, int nu, mat const& V); RcppExport SEXP bayesm_rmultireg(SEXP YSEXP, SEXP XSEXP, SEXP BbarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< mat const& >::type Y(YSEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< mat const& >::type Bbar(BbarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); __result = Rcpp::wrap(rmultireg(Y, X, Bbar, A, nu, V)); return __result; END_RCPP } // rmvpGibbs_rcpp_loop List rmvpGibbs_rcpp_loop(int R, int keep, int nprint, int p, ivec const& y, mat const& X, vec const& beta0, mat const& sigma0, mat const& V, int nu, vec const& betabar, mat const& A); RcppExport SEXP bayesm_rmvpGibbs_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP pSEXP, SEXP ySEXP, SEXP XSEXP, SEXP beta0SEXP, SEXP sigma0SEXP, SEXP VSEXP, SEXP nuSEXP, SEXP betabarSEXP, SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< int >::type p(pSEXP); Rcpp::traits::input_parameter< ivec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type beta0(beta0SEXP); Rcpp::traits::input_parameter< mat const& >::type sigma0(sigma0SEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); __result = Rcpp::wrap(rmvpGibbs_rcpp_loop(R, keep, nprint, p, y, X, beta0, sigma0, V, nu, betabar, A)); return __result; END_RCPP } // rmvst vec rmvst(int nu, vec const& mu, mat const& root); RcppExport SEXP bayesm_rmvst(SEXP nuSEXP, SEXP muSEXP, SEXP rootSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type mu(muSEXP); Rcpp::traits::input_parameter< mat const& >::type root(rootSEXP); __result = Rcpp::wrap(rmvst(nu, mu, root)); return __result; END_RCPP } // rnegbinRw_rcpp_loop List rnegbinRw_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& rootA, double a, double b, vec beta, double alpha, bool fixalpha, mat const& betaroot, double const& alphacroot, int R, int keep, int nprint); RcppExport SEXP bayesm_rnegbinRw_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP rootASEXP, SEXP aSEXP, SEXP bSEXP, SEXP betaSEXP, SEXP alphaSEXP, SEXP fixalphaSEXP, SEXP betarootSEXP, SEXP alphacrootSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type rootA(rootASEXP); Rcpp::traits::input_parameter< double >::type a(aSEXP); Rcpp::traits::input_parameter< double >::type b(bSEXP); Rcpp::traits::input_parameter< vec >::type beta(betaSEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< bool >::type fixalpha(fixalphaSEXP); Rcpp::traits::input_parameter< mat const& >::type betaroot(betarootSEXP); Rcpp::traits::input_parameter< double const& >::type alphacroot(alphacrootSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(rnegbinRw_rcpp_loop(y, X, betabar, rootA, a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint)); return __result; END_RCPP } // rnmixGibbs_rcpp_loop List rnmixGibbs_rcpp_loop(mat const& y, mat const& Mubar, mat const& A, int nu, mat const& V, vec const& a, vec p, vec z, int const& R, int const& keep, int const& nprint); RcppExport SEXP bayesm_rnmixGibbs_rcpp_loop(SEXP ySEXP, SEXP MubarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP, SEXP aSEXP, SEXP pSEXP, SEXP zSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< mat const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type Mubar(MubarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec >::type p(pSEXP); Rcpp::traits::input_parameter< vec >::type z(zSEXP); Rcpp::traits::input_parameter< int const& >::type R(RSEXP); Rcpp::traits::input_parameter< int const& >::type keep(keepSEXP); Rcpp::traits::input_parameter< int const& >::type nprint(nprintSEXP); __result = Rcpp::wrap(rnmixGibbs_rcpp_loop(y, Mubar, A, nu, V, a, p, z, R, keep, nprint)); return __result; END_RCPP } // rordprobitGibbs_rcpp_loop List rordprobitGibbs_rcpp_loop(vec const& y, mat const& X, int k, mat const& A, vec const& betabar, mat const& Ad, double s, mat const& inc_root, vec const& dstarbar, vec const& betahat, int R, int keep, int nprint); RcppExport SEXP bayesm_rordprobitGibbs_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP kSEXP, SEXP ASEXP, SEXP betabarSEXP, SEXP AdSEXP, SEXP sSEXP, SEXP inc_rootSEXP, SEXP dstarbarSEXP, SEXP betahatSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< int >::type k(kSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< double >::type s(sSEXP); Rcpp::traits::input_parameter< mat const& >::type inc_root(inc_rootSEXP); Rcpp::traits::input_parameter< vec const& >::type dstarbar(dstarbarSEXP); Rcpp::traits::input_parameter< vec const& >::type betahat(betahatSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(rordprobitGibbs_rcpp_loop(y, X, k, A, betabar, Ad, s, inc_root, dstarbar, betahat, R, keep, nprint)); return __result; END_RCPP } // rscaleUsage_rcpp_loop List rscaleUsage_rcpp_loop(int k, mat const& x, int p, int n, int R, int keep, int ndghk, int nprint, mat y, vec mu, mat Sigma, vec tau, vec sigma, mat Lambda, double e, bool domu, bool doSigma, bool dosigma, bool dotau, bool doLambda, bool doe, int nu, mat const& V, mat const& mubar, mat const& Am, vec const& gsigma, vec const& gl11, vec const& gl22, vec const& gl12, int nuL, mat const& VL, vec const& ge); RcppExport SEXP bayesm_rscaleUsage_rcpp_loop(SEXP kSEXP, SEXP xSEXP, SEXP pSEXP, SEXP nSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP ndghkSEXP, SEXP nprintSEXP, SEXP ySEXP, SEXP muSEXP, SEXP SigmaSEXP, SEXP tauSEXP, SEXP sigmaSEXP, SEXP LambdaSEXP, SEXP eSEXP, SEXP domuSEXP, SEXP doSigmaSEXP, SEXP dosigmaSEXP, SEXP dotauSEXP, SEXP doLambdaSEXP, SEXP doeSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP mubarSEXP, SEXP AmSEXP, SEXP gsigmaSEXP, SEXP gl11SEXP, SEXP gl22SEXP, SEXP gl12SEXP, SEXP nuLSEXP, SEXP VLSEXP, SEXP geSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int >::type k(kSEXP); Rcpp::traits::input_parameter< mat const& >::type x(xSEXP); Rcpp::traits::input_parameter< int >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type n(nSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type ndghk(ndghkSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< mat >::type y(ySEXP); Rcpp::traits::input_parameter< vec >::type mu(muSEXP); Rcpp::traits::input_parameter< mat >::type Sigma(SigmaSEXP); Rcpp::traits::input_parameter< vec >::type tau(tauSEXP); Rcpp::traits::input_parameter< vec >::type sigma(sigmaSEXP); Rcpp::traits::input_parameter< mat >::type Lambda(LambdaSEXP); Rcpp::traits::input_parameter< double >::type e(eSEXP); Rcpp::traits::input_parameter< bool >::type domu(domuSEXP); Rcpp::traits::input_parameter< bool >::type doSigma(doSigmaSEXP); Rcpp::traits::input_parameter< bool >::type dosigma(dosigmaSEXP); Rcpp::traits::input_parameter< bool >::type dotau(dotauSEXP); Rcpp::traits::input_parameter< bool >::type doLambda(doLambdaSEXP); Rcpp::traits::input_parameter< bool >::type doe(doeSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< mat const& >::type mubar(mubarSEXP); Rcpp::traits::input_parameter< mat const& >::type Am(AmSEXP); Rcpp::traits::input_parameter< vec const& >::type gsigma(gsigmaSEXP); Rcpp::traits::input_parameter< vec const& >::type gl11(gl11SEXP); Rcpp::traits::input_parameter< vec const& >::type gl22(gl22SEXP); Rcpp::traits::input_parameter< vec const& >::type gl12(gl12SEXP); Rcpp::traits::input_parameter< int >::type nuL(nuLSEXP); Rcpp::traits::input_parameter< mat const& >::type VL(VLSEXP); Rcpp::traits::input_parameter< vec const& >::type ge(geSEXP); __result = Rcpp::wrap(rscaleUsage_rcpp_loop(k, x, p, n, R, keep, ndghk, nprint, y, mu, Sigma, tau, sigma, Lambda, e, domu, doSigma, dosigma, dotau, doLambda, doe, nu, V, mubar, Am, gsigma, gl11, gl22, gl12, nuL, VL, ge)); return __result; END_RCPP } // rsurGibbs_rcpp_loop List rsurGibbs_rcpp_loop(List const& regdata, vec const& indreg, vec const& cumnk, vec const& nk, mat const& XspXs, mat Sigmainv, mat const& A, vec const& Abetabar, int nu, mat const& V, int nvar, mat E, mat const& Y, int R, int keep, int nprint); RcppExport SEXP bayesm_rsurGibbs_rcpp_loop(SEXP regdataSEXP, SEXP indregSEXP, SEXP cumnkSEXP, SEXP nkSEXP, SEXP XspXsSEXP, SEXP SigmainvSEXP, SEXP ASEXP, SEXP AbetabarSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP nvarSEXP, SEXP ESEXP, SEXP YSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< vec const& >::type indreg(indregSEXP); Rcpp::traits::input_parameter< vec const& >::type cumnk(cumnkSEXP); Rcpp::traits::input_parameter< vec const& >::type nk(nkSEXP); Rcpp::traits::input_parameter< mat const& >::type XspXs(XspXsSEXP); Rcpp::traits::input_parameter< mat >::type Sigmainv(SigmainvSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< vec const& >::type Abetabar(AbetabarSEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< int >::type nvar(nvarSEXP); Rcpp::traits::input_parameter< mat >::type E(ESEXP); Rcpp::traits::input_parameter< mat const& >::type Y(YSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(rsurGibbs_rcpp_loop(regdata, indreg, cumnk, nk, XspXs, Sigmainv, A, Abetabar, nu, V, nvar, E, Y, R, keep, nprint)); return __result; END_RCPP } // rtrun NumericVector rtrun(NumericVector const& mu, NumericVector const& sigma, NumericVector const& a, NumericVector const& b); RcppExport SEXP bayesm_rtrun(SEXP muSEXP, SEXP sigmaSEXP, SEXP aSEXP, SEXP bSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< NumericVector const& >::type mu(muSEXP); Rcpp::traits::input_parameter< NumericVector const& >::type sigma(sigmaSEXP); Rcpp::traits::input_parameter< NumericVector const& >::type a(aSEXP); Rcpp::traits::input_parameter< NumericVector const& >::type b(bSEXP); __result = Rcpp::wrap(rtrun(mu, sigma, a, b)); return __result; END_RCPP } // runireg_rcpp_loop List runireg_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& A, int nu, double ssq, int R, int keep, int nprint); RcppExport SEXP bayesm_runireg_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP ssqSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(runireg_rcpp_loop(y, X, betabar, A, nu, ssq, R, keep, nprint)); return __result; END_RCPP } // runiregGibbs_rcpp_loop List runiregGibbs_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& A, int nu, double ssq, double sigmasq, int R, int keep, int nprint); RcppExport SEXP bayesm_runiregGibbs_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP ssqSEXP, SEXP sigmasqSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< int >::type nu(nuSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< double >::type sigmasq(sigmasqSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); __result = Rcpp::wrap(runiregGibbs_rcpp_loop(y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint)); return __result; END_RCPP } // rwishart List rwishart(int const& nu, mat const& V); RcppExport SEXP bayesm_rwishart(SEXP nuSEXP, SEXP VSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< int const& >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); __result = Rcpp::wrap(rwishart(nu, V)); return __result; END_RCPP } // callroot vec callroot(vec const& c1, vec const& c2, double tol, int iterlim); RcppExport SEXP bayesm_callroot(SEXP c1SEXP, SEXP c2SEXP, SEXP tolSEXP, SEXP iterlimSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< vec const& >::type c1(c1SEXP); Rcpp::traits::input_parameter< vec const& >::type c2(c2SEXP); Rcpp::traits::input_parameter< double >::type tol(tolSEXP); Rcpp::traits::input_parameter< int >::type iterlim(iterlimSEXP); __result = Rcpp::wrap(callroot(c1, c2, tol, iterlim)); return __result; END_RCPP } bayesm/src/rsurGibbs_rcpp_loop.cpp0000644000176000001440000001031312541203644017073 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rsurGibbs_rcpp_loop(List const& regdata, vec const& indreg, vec const& cumnk, vec const& nk, mat const& XspXs, mat Sigmainv, mat const& A, vec const& Abetabar, int nu, mat const& V, int nvar, mat E, mat const& Y, int R, int keep, int nprint){ // Keunwoo Kim 09/19/2014 // Purpose: implement Gibbs Sampler for SUR // Arguments: // Data -- regdata // regdata is a list of lists of data for each regression // regdata[[i]] contains data for regression equation i // regdata[[i]]$y is y, regdata[[i]]$X is X // note: each regression can have differing numbers of X vars // but you must have same no of obs in each equation. // Prior -- list of prior hyperparameters // betabar,A prior mean, prior precision // nu, V prior on Sigma // Mcmc -- list of MCMC parms // R number of draws // keep -- thinning parameter // nprint - print estimated time remaining on every nprint'th draw // Output: list of betadraw,Sigmadraw // Model: // y_i = X_ibeta + e_i // y is nobs x 1 // X is nobs x k_i // beta is k_i x 1 vector of coefficients // i=1,nreg total regressions // (e_1,k,...,e_nreg,k) ~ N(0,Sigma) k=1,...,nobs // we can also write as stacked regression // y = Xbeta+e // y is nobs*nreg x 1,X is nobs*nreg x (sum(k_i)) // routine draws beta -- the stacked vector of all coefficients // Prior: // beta ~ N(betabar,A^-1) // Sigma ~ IW(nu,V) int reg, mkeep, i, j; vec beta, btilde, yti; mat IR, ucholinv, EEVinv, Sigma, Xtipyti, Ydti; List regdatai, rwout; int nreg = regdata.size(); // convert List to std::vector of struct std::vector regdata_vector; moments regdatai_struct; // store vector with struct for (reg=0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdata_vector.push_back(regdatai_struct); } int nobs = (regdatai_struct.y).size(); mat XtipXti = zeros(sum(nk), sum(nk)); mat Sigmadraw(R/keep, nreg*nreg); mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); for (int rep=0; rep(rwout["IW"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> Sigmainv = as(rwout["W"]); //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); Sigmadraw(mkeep-1, span::all) = trans(vectorise(Sigma)); } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("Sigmadraw") = Sigmadraw); } bayesm/src/rnegbinRw_rcpp_loop.cpp0000644000176000001440000000651012541203644017072 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rnegbinRw_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& rootA, double a, double b, vec beta, double alpha, bool fixalpha, mat const& betaroot, double const& alphacroot, int R, int keep, int nprint){ // Keunwoo Kim 11/02/2014 // Arguments: // Data // X is nobs X nvar matrix // y is nobs vector // Prior - list containing the prior parameters // betabar, rootA - mean of beta prior, chol-root of inverse of variance covariance of beta prior // a, b - parameters of alpha prior // Mcmc - list containing // R is number of draws // keep is thinning parameter (def = 1) // nprint - print estimated time remaining on every nprint'th draw (def = 100) // betaroot - step size for beta RW // alphacroot - step size for alpha RW // beta - initial guesses for beta // alpha - initial guess for alpha // fixalpha - if TRUE, fix alpha and draw only beta // // Output: // // Model: // (y|lambda,alpha) ~ Negative Binomial(Mean = lambda, Overdispersion par = alpha) // ln(lambda) = X * beta // // Prior: // beta ~ N(betabar, A^-1) // alpha ~ Gamma(a,b) where mean = a/b and variance = a/(b^2) // vec betac; double ldiff, acc, unif, logalphac, oldlpostalpha, oldlpostbeta, clpostbeta, clpostalpha; int mkeep, rep; int nvar = X.n_cols; int nacceptbeta = 0; int nacceptalpha = 0; vec alphadraw(R/keep); mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); //start main iteration loop for (rep=0; rep 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ beta = betac; nacceptbeta = nacceptbeta + 1; } // Draw alpha if (!fixalpha){ logalphac = log(alpha) + alphacroot*rnorm(1)[0]; //rnorm returns a NumericVector, so using [0] allows for conversion to double oldlpostalpha = lpostalpha(alpha, beta, X, y, a, b); clpostalpha = lpostalpha(exp(logalphac), beta, X, y, a, b); ldiff = clpostalpha - oldlpostalpha; acc = exp(ldiff); if (acc > 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ alpha = exp(logalphac); nacceptalpha = nacceptalpha + 1; } } if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); alphadraw[mkeep-1] = alpha; } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("alphadraw") = alphadraw, Named("nacceptbeta") = nacceptbeta, Named("nacceptalpha") = nacceptalpha); } bayesm/src/rscaleUsage_rcpp_loop.cpp0000644000176000001440000003054212541203644017375 0ustar ripleyusers#include "bayesm.h" #include //used for "sample" function //SUPPORT FUNCTIONS SPECIFIC TO MAIN FUNCTION-------------------------------------------------------------------------------------- double ghk(mat const& L, vec const& a, vec const& b, int const& n, int const& dim){ // Wayne Taylor 4/29/15 //routine to implement ghk with a region : a[i-1] <= x_i <= b[i-1] //r mcculloch 8/04 //L is lower triangular root of Sigma random vector is assumed to have zero mean //n is number of draws to use in GHK //dim is the dimension of L //modified 6/05 by rossi to check arg into qnorm //converted to rcpp 5/15 int i,j; NumericVector aa(1),bb(1),pa(1),pb(1),arg(1); double u,prod,mu; vec z(dim); double res=0.0; for(i=0;i0) mu = as_scalar(L(j,span(0,j-1))*z(span(0,j-1))); //previously done via a loop for(k=0;k .999999999) arg[0]=.999999999; if(arg[0] < .0000000001) arg[0]=.0000000001; z[j] = qnorm(arg,0.0,1.0)[0]; } res += prod; } res /= n; return (res); } mat dy(mat y, mat const& x, vec const& c, vec const& mu, mat const& beta, vec const& s, vec const& tau, vec const& sigma){ // Wayne Taylor 4/29/15 //Variable declaration double sigman,taun; rowvec yn; vec xn; int p = y.n_cols; int nobs = y.n_rows; //cm = conditional mean, cs = condtional standard deviation //u =uniform for truncated normal draw double cm,cs,u; // standardized truncation points (a,b) // cdf at truncation points (pa,pb) NumericVector a(1),b(1),pa(1),pb(1); double qout; //loop over coordinates of y - first by rows, then by columns for(int n = 0; n(n); double offset = p*log((double)k); vec a,b; double ghkres,lghkres; uvec xia(p),xib(p); mat Li; for(int i = 0; i(cbetarows,p); uvec ui(1),ind(p-1); int counter; uvec cbetaAllRow(cbetarows); for(int i = 0; i(2,2); S(0,0) = (n-1)*moms[2] + n*pow(moms[0],2); S(0,1) = (n-1)*moms[3] + n*moms[0]*(moms[1]-Lam(1,1)); S(1,0) = S(0,1); S(1,1) = (n-1)*moms[4] + n*pow(moms[1]-Lam(1,1),2); return(S); } double llL(mat const& Lam, int n, mat const& S, mat const& V,int nu){ //Wayne Taylor 4/29/15 int d = Lam.n_cols; double dlam = Lam(0,0)*Lam(1,1)-pow(Lam(0,1),2); mat M = (S+V) * solve(Lam,eye(d,d)); double ll = -.5*(n+nu+3)*log(dlam) -.5*sum(M.diag()); return(ll); } //MAIN FUNCTION------------------------------------------------------------------------------------ //[[Rcpp::export]] List rscaleUsage_rcpp_loop(int k, mat const& x, int p, int n, int R, int keep, int ndghk, int nprint, mat y, vec mu, mat Sigma, vec tau, vec sigma, mat Lambda, double e, bool domu, bool doSigma, bool dosigma, bool dotau, bool doLambda, bool doe, int nu, mat const& V, mat const& mubar, mat const& Am, vec const& gsigma, vec const& gl11,vec const& gl22, vec const& gl12, int nuL, mat const& VL, vec const& ge){ // R.McCulloch, 12/04 code for scale usage R function (rScaleUsage) // changed to R error function, P. Rossi 05/12 // converted to rcpp W. Taylor 04/15 //variable declaration int mkeep, ng, ei, pi; double eprop, eold; double Ai, A, xtx, beta, s2, m, a, b, s, qr, llold, llprop, lrat, paccept; vec cc, xty, ete, pv, h, moms(5),rgl11, rgl12a, rgl12, rgl22, absege, minvec; uvec eiu; mat Res, S, yd, Si, Vmi, Rm, Ri, Vm, mm, onev, xx, ytemp, yy, eps, dat, temp, SS; List bs, rwout; rowvec onesp = ones(p); int nk = R/keep; int ndpost = nk*keep; mat drSigma = zeros(nk,pow(p,2.0)); mat drmu = zeros(nk,p); mat drtau = zeros(nk,n); mat drsigma = zeros(nk,n); mat drLambda = zeros(nk,4); vec dre = zeros(nk); if(nprint>0) startMcmcTimer(); for(int rep = 0; rep < ndpost; rep++) { cc = cgetC(e,k); bs = condd(Sigma); y = dy(y,x,cc,mu,as(bs["beta"]),as(bs["s"]),tau,sigma); //draw Sigma if(doSigma) { Res = y; Res.each_row() -= trans(mu); Res.each_col() -= tau; Res.each_col() /= sigma; S = trans(Res)*Res; rwout = rwishart(nu+n,solve(V+S,eye(p,p))); Sigma = as(rwout["IW"]); } //draw mu if(domu) { yd = y; yd.each_col() -= tau; Si = solve(Sigma,eye(p,p)); Vmi = as_scalar(sum(1/pow(sigma,2)))*Si + Am; Rm = chol(Vmi); Ri = solve(trimatu(Rm),eye(p,p)); Vm = solve(Vmi,eye(p,p)); mm = Vm * (Si * (trans(yd) * (1/pow(sigma,2))) + Am * mubar); mu = vectorise(mm + Ri * as(rnorm(p))); } //draw tau if(dotau) { Ai = Lambda(0,0) - pow(Lambda(0,1),2)/Lambda(1,1); A = 1.0/Ai; onev = ones(p,1); Rm = chol(Sigma); xx = trans(solve(trans(Rm),onev)); ytemp = trans(y); ytemp.each_col() -= mu; yy = trans(solve(trans(Rm),ytemp)); xtx = accu(pow(xx,2)); //To get a sum of all the elements regardless of the argument type (ie. matrix or vector), use accu() xty = vectorise(xx*trans(yy)); beta = A*Lambda(0,1)/Lambda(1,1); for(int j = 0; j(p); ete = vectorise(onesp * pow(eps,2)); a = Lambda(1,1); b = Lambda(0,1)/Lambda(0,0); s = sqrt(Lambda(1,1)-pow(Lambda(0,1),2)/Lambda(0,0)); for(int j = 0; j pow(Lambda(0,1),2)/Lambda(1,1))); ng = rgl11.size(); pv = zeros(ng); for(int j = 0; j -sqrt(Lambda(0,0)*Lambda(1,1)))); ng = rgl12.size(); pv = zeros(ng); for(int j = 0; j pow(Lambda(0,1),2)/Lambda(0,0))); ng = rgl22.size(); pv = zeros(ng); for(int j = 0;j0) { e = eprop; } else { minvec << 1 << exp(lrat); paccept = min(minvec); if(rbinom(1,1,paccept)[0]==1){ e = eprop; } else { e = eold; } } } if (nprint>0) if((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; drSigma(mkeep-1,span::all) = trans(vectorise(Sigma)); drmu(mkeep-1,span::all) = trans(mu); drtau(mkeep-1,span::all) = trans(tau); drsigma(mkeep-1,span::all) = trans(sigma); drLambda(mkeep-1,span::all) = trans(vectorise(Lambda)); dre[mkeep-1] = e; } } if (nprint>0) endMcmcTimer(); return List::create( Named("ndpost") = ndpost, Named("drmu") = drmu, Named("drtau") = drtau, Named("drsigma") = drsigma, Named("drLambda") = drLambda, Named("dre") = dre, Named("drSigma") = drSigma); } bayesm/src/runiregGibbs_rcpp_loop.cpp0000644000176000001440000000373712541203644017567 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List runiregGibbs_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& A, int nu, double ssq, double sigmasq, int R, int keep, int nprint) { // Keunwoo Kim 09/09/2014 // Purpose: perform iid draws from posterior of regression model using conjugate prior // Arguments: // y,X // betabar,A prior mean, prior precision // nu, ssq prior on sigmasq // R number of draws // keep thinning parameter // Output: list of beta, sigmasq // Model: // y = Xbeta + e e ~N(0,sigmasq) // y is n x 1 // X is n x k // beta is k x 1 vector of coefficients // Prior: // beta ~ N(betabar,sigmasq*A^-1) // sigmasq ~ (nu*ssq)/chisq_nu // int mkeep; double s; mat RA, W, IR; vec z, btilde, beta; int nvar = X.n_cols; int nobs = y.size(); vec sigmasqdraw(R/keep); mat betadraw(R/keep, nvar); mat XpX = trans(X)*X; vec Xpy = trans(X)*y; vec Abetabar = A*betabar; if (nprint>0) startMcmcTimer(); for (int rep=0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); sigmasqdraw[mkeep-1] = sigmasq; } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmasqdraw") = NumericVector(sigmasqdraw.begin(),sigmasqdraw.end())); } bayesm/src/clusterMix_rcpp_loop.cpp0000644000176000001440000001014212541203644017270 0ustar ripleyusers#include "bayesm.h" //EXTRA FUNCTIONS SPECIFIC TO THE MAIN FUNCTION-------------------------------------------- mat ztoSim(vec const& z){ // function to convert indicator vector to Similarity matrix // Sim is n x n matrix, Sim[i,j]=1 if pair(i,j) are in same group // z is n x 1 vector of indicators (1,...,p) int n = z.size(); vec onevec = ones(n); // equivalent to zvec=c(rep(z,n)) in R vec zvec = kron(onevec, z); vec zcomp = kron(z, onevec);// equivalent to as.numeric((zvec==zcomp)) in R mat Sim = zeros(n*n,1); for (int i=0; i(n); int groupn = 1; for (i=0; i0){ groupn = groupn + 1; } } return (z); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] List clusterMix_rcpp_loop(mat const& zdraw, double cutoff, bool SILENT, int nprint){ // Keunwoo Kim 10/06/2014 // Purpose: // cluster observations based on draws of indicators of // normal mixture components // Arguments: // zdraw is a R x nobs matrix of draws of indicators (typically output from rnmixGibbs) // the rth row of zdraw contains rth draw of indicators for each observations // each element of zdraw takes on up to p values for up to p groups. The maximum // number of groups is nobs. Typically, however, the number of groups will be small // and equal to the number of components used in the normal mixture fit. // cutoff is a cutoff used in determining one clustering scheme it must be // a number between .5 and 1. // nprint - print every nprint'th draw // Output: // two clustering schemes each with a vector of length nobs which gives the assignment // of each observation to a cluster // clustera (finds zdraw with similarity matrix closest to posterior mean of similarity) // clusterb (finds clustering scheme by assigning ones if posterior mean of similarity matrix cutoff and computing associated z ) int rep, i; uword index; // type uword means unsigned integer. Necessary for finding the index of min. int nobs = zdraw.n_cols; char buf[32]; // compute posterior mean of Similarity matrix if (!SILENT){ Rcout << "Computing Posterior Expectation of Similarity Matrix\n"; Rcout << "processing draws ...\n"; } mat Pmean = zeros(nobs, nobs); int R = zdraw.n_rows; for (rep=0; rep(R); for (rep=0; rep cutoff vec Pmeanvec = vectorise(Pmean); mat Sim = zeros(nobs*nobs,1); for (i=0; i=cutoff) Sim(i,0) = 1; } Sim.reshape(nobs,nobs); vec clusterb = Simtoz(Sim); return List::create( Named("clustera") = clustera, Named("clusterb") = clusterb); } bayesm/src/rtrun_rcpp.cpp0000644000176000001440000000102412541203644015251 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] NumericVector rtrun(NumericVector const& mu, NumericVector const& sigma, NumericVector const& a, NumericVector const& b){ // Wayne Taylor 9/7/2014 // function to draw from univariate truncated norm // a is vector of lower bounds for truncation // b is vector of upper bounds for truncation NumericVector FA = pnorm((a-mu)/sigma); NumericVector FB = pnorm((b-mu)/sigma); return(mu+sigma*qnorm(runif(mu.size())*(FB-FA)+FA)); } bayesm/src/Makevars.win0000644000176000001440000000012312541203644014636 0ustar ripleyusersPKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) PKG_CPPFLAGS = -I../inst/include/ bayesm/src/rhierNegbinRw_rcpp_loop.cpp0000644000176000001440000001477012541203644017711 0ustar ripleyusers#include "bayesm.h" //EXTRA FUNCTIONS SPECIFIC TO THE MAIN FUNCTION-------------------------------------------- double llnegbinpooled(std::vector regdata_vector, mat Beta, double alpha){ // Wayne Taylor 12/01/2014 // "Unlists" the regdata and calculates the negative binomial loglikelihood using individual-level betas int nreg = regdata_vector.size(); double ll = 0.0; for(int reg = 0; reg(nreg); vec clpostbeta = zeros(nreg); cube Betadraw = zeros(nreg, nvar, R/keep); vec alphadraw = zeros(R/keep); vec llike = zeros(R/keep); mat Vbetadraw = zeros(R/keep,nvar*nvar); mat Deltadraw = zeros(R/keep,nvar*nz); // convert regdata and hessdata Lists to std::vector of struct std::vector regdata_vector; moments regdatai_struct; List regdatai,hessi; // store vector with struct for (int reg = 0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdatai_struct.hess = as(hessi["hess"]); regdata_vector.push_back(regdatai_struct); } if (nprint>0) startMcmcTimer(); // start main iteration loop for (rep = 0; rep < R; rep++){ mat betabar = Z*Delta; // Draw betai for(int reg = 0; reg 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ Beta(reg,span::all) = trans(betac); nacceptbeta = nacceptbeta + 1; } } // Draw alpha if (!fixalpha){ logalphac = log(alpha) + alphacroot*rnorm(1)[0]; //rnorm returns a NumericVector, so using [0] allows for conversion to double oldlpostalpha = llnegbinpooled(regdata_vector,Beta,alpha)+(a-1)*log(alpha) - b*alpha; clpostalpha = llnegbinpooled(regdata_vector,Beta,exp(logalphac))+(a-1)*logalphac - b*exp(logalphac); ldiff = clpostalpha - oldlpostalpha; acc = exp(ldiff); if (acc > 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ alpha = exp(logalphac); nacceptalpha = nacceptalpha + 1; } } // Draw Vbeta and Delta using rmultireg List temp = rmultireg(Beta,Z,Deltabar,Adelta,nu,V); mat Vbeta = as(temp["Sigma"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> Vbetainv = solve(Vbeta,eye(nvar,nvar)); rootA = chol(Vbetainv); Delta = as(temp["B"]); if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; Betadraw.slice(mkeep-1) = Beta; alphadraw[mkeep-1] = alpha; Vbetadraw(mkeep-1,span::all) = trans(vectorise(Vbeta)); Deltadraw(mkeep-1,span::all) = trans(vectorise(Delta)); llike[mkeep-1] = llnegbinpooled(regdata_vector,Beta,alpha); } } if (nprint>0) endMcmcTimer(); return List::create( Named("llike") = llike, Named("Betadraw") = Betadraw, Named("alphadraw") = alphadraw, Named("Vbetadraw") = Vbetadraw, Named("Deltadraw") = Deltadraw, Named("acceptrbeta") = nacceptbeta/(R*nreg*1.0)*100, Named("acceptralpha") = nacceptalpha/(R*1.0)*100); } bayesm/src/rmixture_rcpp.cpp0000644000176000001440000000373412541203644015770 0ustar ripleyusers#include "bayesm.h" //FUNCTION SPECIFIC TO MAIN FUNCTION-------------------------------- vec rcomp(List comp) { // Wayne Taylor 9/10/14 //purpose: draw multivariate normal with mean and variance given by comp // arguments: // comp is a list of length 2, // comp[[1]] is the mean and comp[[2]] is R^{-1} = comp[[2]], Sigma = t(R)%*%R vec mu = comp[0]; mat rooti = comp[1]; int dim = rooti.n_cols; mat root = solve(trimatu(rooti),eye(dim,dim)); //trimatu interprets the matrix as upper triangular and makes solve more efficient return(vectorise(mu+trans(root)*as(rnorm(mu.size())))); } //[[Rcpp::export]] List rmixture(int n, vec pvec, List comps) { // Wayne Taylor 9/10/2014 // revision history: // commented by rossi 3/05 // // purpose: iid draws from mixture of multivariate normals // arguments: // n: number of draws // pvec: prior probabilities of normal components // comps: list, each member is a list comp with ith normal component // ~N(comp[[1]],Sigma), Sigma = t(R)%*%R, R^{-1} = comp[[2]] // output: // list of x (n by length(comp[[1]]) matrix of draws) and z latent indicators of // component //Draw vector of indices using base R 'sample' function mat prob(n,pvec.size()); for(int i = 0; i(runif(n)) % prob.col(pvec.size()-1); // Evaluative each column of "prob" until the uniform draw is less than the cumulative value vec z = zeros(n); for(int i = 0; i prob(i, z[i]++)); List comp0 = comps[0]; vec mu0 = comp0[0]; mat x(n,mu0.size()); //Draw from MVN from comp determined from z index //Note z starts at 1, not 0 for(int i = 0; i(w.size()); for(int i = 0; i(R/keep, p*p); mat betadraw = zeros(R/keep,k); vec wnew = zeros(X.n_rows); //set initial values of w,beta, sigma (or root of inv) vec wold = wnew; vec betaold = beta0; mat C = chol(solve(trimatu(sigma0),eye(sigma0.n_cols,sigma0.n_cols))); //C is upper triangular root of sigma^-1 (G) = C'C //trimatu interprets the matrix as upper triangular and makes solve more efficient mat sigmai, zmat, epsilon, S, IW, ucholinv, VSinv; vec betanew; List W; // start main iteration loop int mkeep = 0; if(nprint>0) startMcmcTimer(); for(int rep = 0; rep(W["C"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> //print time to completion if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //save every keepth draw if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1,span::all) = trans(betanew); IW = as(W["IW"]); sigmadraw(mkeep-1,span::all) = trans(vectorise(IW)); } wold = wnew; betaold = betanew; } if(nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmadraw") = sigmadraw); } bayesm/src/cgetC_rcpp.cpp0000644000176000001440000000233112541203644015126 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] vec cgetC(double e, int k){ //Wayne Taylor 4/29/15 //purpose: get a list of cutoffs for use with scale usage problems //arguments: // e: the "e" parameter from the paper // k: the point scale, eg. items are rated from 1,2,...k // output: // vector of grid points vec temp = zeros(k-1); for(int i = 0; i<(k-1); i++) temp[i] = i + 1.5; double m1 = sum(temp); temp = pow(temp,2); double m2 = sum(temp); vec c = zeros(k+1); //first sum to get s's, this is a waste since it should be done //once but I don't want to see this things anywhere else and it should take no time double s0 = k-1; double s1=0.0,s2=0.0,s3=0.0,s4=0.0; for(int i=1;i(ncolX); rowvec beta = zeros(ncolX); double cloglike, clpost, climp, ldiff, alpha, unif, oldloglike; vec alphaminv; if(nprint>0) startMcmcTimer(); // start main iteration loop for(int rep = 0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1,span::all) = beta; loglike[mkeep-1] = oldloglike; } } if(nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("loglike") = loglike, Named("naccept") = naccept); } bayesm/src/lndIWishart_rcpp.cpp0000644000176000001440000000230712541203644016334 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] double lndIWishart(double nu, mat const& V, mat const& IW){ // Keunwoo Kim 07/24/2014 // Purpose: evaluate log-density of inverted Wishart with normalizing constant // Arguments: // nu is d. f. parm // V is location matrix // IW is the value at which the density should be evaluated // Note: in this parameterization, E[IW]=V/(nu-k-1) int k = V.n_cols; mat Uiw = chol(IW); mat Uiwi = solve(trimatu(Uiw), eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat IWi = Uiwi*trans(Uiwi); mat cholV = chol(V); double lndetVd2 = sum(log(cholV.diag())); double lndetIWd2 = sum(log(Uiw.diag())); // first evaluate constant double cnst = ((nu*k)/2)*log(2.0)+((k*(k-1))/4.0)*log(M_PI); // (k*(k-1))/4 is recognized as integer. "4.0" allows it to be recognized as a double. vec seq_1_k = cumsum(ones(k)); // build c(1:k) through cumsum function vec arg = (nu+1-seq_1_k)/2.0; // lgamma cannot receive arma::vec input. Compute cnst+sum(lgamma(arg)). for (int i=0; i0 e~N(0,1) // Prior: beta ~ N(betabar,A^-1) int mkeep; vec mu; vec z; int nvar = X.n_cols; mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); //start main iteration loop for (int rep=0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); } } if (nprint>0) endMcmcTimer(); return List::create(Named("betadraw") = betadraw); } bayesm/src/rhierMnlDP_rcpp_loop.cpp0000644000176000001440000003070012541203644017137 0ustar ripleyusers#include "bayesm.h" //FUNCTIONS SPECIFIC TO MAIN FUNCTION------------------------------------------------------ mat drawDelta(mat const& x,mat const& y,ivec const& z,std::vector const& comps_vector,vec const& deltabar,mat const& Ad){ // Wayne Taylor 2/21/2015 // delta = vec(D) // given z and comps (z[i] gives component indicator for the ith observation, // comps is a list of mu and rooti) // y is n x p // x is n x k // y = xD' + U , rows of U are indep with covs Sigma_i given by z and comps int p = y.n_cols; int k = x.n_cols; int ncomp = comps_vector.size(); mat xtx = zeros(k*p,k*p); mat xty = zeros(p,k); //this is the unvecced version, reshaped after the sum //Create the index vectors, the colAll vectors are equal to span::all but with uvecs (as required by .submat) uvec colAlly(p), colAllx(k); for(int i = 0; i0){ mat yi = y.submat(ind,colAlly); mat xi = x.submat(ind,colAllx); murooti compsi_struct = comps_vector[compi]; yi.each_row() -= trans(compsi_struct.mu); //the subtraction operation is repeated on each row of yi mat sigi = compsi_struct.rooti*trans(compsi_struct.rooti); xtx = xtx + kron(trans(xi)*xi,sigi); xty = xty + (sigi * (trans(yi)*xi)); } } xty.reshape(xty.n_rows*xty.n_cols,1); //vec(t(D)) ~ N(V^{-1}(xty + Ad*deltabar),V^{-1}) where V = (xtx+Ad) // compute the inverse of xtx+Ad mat ucholinv = solve(trimatu(chol(xtx+Ad)), eye(k*p,k*p)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat Vinv = ucholinv*trans(ucholinv); return(Vinv*(xty+Ad*deltabar) + trans(chol(Vinv))*as(rnorm(deltabar.size()))); } DPOut rDPGibbs1(mat y, lambda lambda_struct, std::vector thetaStar_vector, int maxuniq, ivec indic, vec q0v, double alpha, priorAlpha const& priorAlpha_struct, int gridsize, List const& lambda_hyper){ // Wayne Taylor 2/21/2015 //revision history: //created from rDPGibbs by Rossi 3/08 //do one draw of DP Gibbs sampler with normal base //Model: // y_i ~ N(y|thetai) // thetai|G ~ G // G|lambda,alpha ~ DP(G|G0(lambda),alpha) //Priors: // alpha: starting value // lambda: // G0 ~ N(mubar,Sigma (x) Amu^-1) // mubar=vec(mubar) // Sigma ~ IW(nu,nu*V) V=v*I note: mode(Sigma)=nu/(nu+2)*v*I // mubar=0 // amu is uniform on grid specified by alim // nu is log uniform, nu=d-1+exp(Z) z is uniform on seq defined bvy nulim // v is uniform on sequence specificd by vlim // priorAlpha_struct: // alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power // alphamin=exp(digamma(Istarmin)-log(gamma+log(N))) // alphamax=exp(digamma(Istarmax)-log(gamma+log(N))) // gamma= .5772156649015328606 //output: // ind - vector of indicators for which observations are associated with which comp in thetaStar // thetaStar - list of unique normal component parms // lambda - list of of (a,nu,V) // alpha // thetaNp1 - one draw from predictive given thetaStar, lambda,alphama int n = y.n_rows; int dimy = y.n_cols; int nunique, indsize, indp, probssize; vec probs; uvec ind; mat ydenmat; uvec spanall(dimy); for(int i = 0; i new_utheta_vector(1), thetaNp1_vector(1); murooti thetaNp10_struct, outGD_struct; for(int rep = 0; rep<1; rep++) { //note we only do one loop! q0v = q0(y,lambda_struct); nunique = thetaStar_vector.size(); if(nunique > maxuniq) stop("maximum number of unique thetas exceeded"); //ydenmat is a length(thetaStar) x n array of density values given f(y[j,] | thetaStar[[i]] // note: due to remix step (below) we must recompute ydenmat each time! ydenmat = zeros(maxuniq,n); ydenmat(span(0,nunique-1),span::all) = yden(thetaStar_vector,y); thetaStarDrawOut_struct = thetaStarDraw(indic, thetaStar_vector, y, ydenmat, q0v, alpha, lambda_struct, maxuniq); thetaStar_vector = thetaStarDrawOut_struct.thetaStar_vector; indic = thetaStarDrawOut_struct.indic; nunique = thetaStar_vector.size(); //thetaNp1 and remix probs = zeros(nunique+1); for(int j = 0; j < nunique; j++){ ind = find(indic == (j+1)); indsize = ind.size(); probs[j] = indsize/(alpha + n + 0.0); new_utheta_vector[0] = thetaD(y(ind,spanall),lambda_struct); thetaStar_vector[j] = new_utheta_vector[0]; } probs[nunique] = alpha/(alpha+n+0.0); indp = rmultinomF(probs); probssize = probs.size(); if(indp == probssize) { outGD_struct = GD(lambda_struct); thetaNp10_struct.mu = outGD_struct.mu; thetaNp10_struct.rooti = outGD_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } else { outGD_struct = thetaStar_vector[indp-1]; thetaNp10_struct.mu = outGD_struct.mu; thetaNp10_struct.rooti = outGD_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } //draw alpha alpha = alphaD(priorAlpha_struct,nunique,gridsize); //draw lambda lambda_struct = lambdaD(lambda_struct,thetaStar_vector,lambda_hyper["alim"],lambda_hyper["nulim"],lambda_hyper["vlim"],gridsize); } //note indic is the vector of indicators for each obs correspond to which thetaStar DPOut out_struct; out_struct.thetaStar_vector = thetaStar_vector; out_struct.thetaNp1_vector = thetaNp1_vector; out_struct.alpha = alpha; out_struct.lambda_struct = lambda_struct; out_struct.indic = indic; return(out_struct); } //MAIN FUNCTION------------------------------------------------------------------------------------- //[[Rcpp::export]] List rhierMnlDP_rcpp_loop(int R, int keep, int nprint, List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, List const& PrioralphaList, List const& lambda_hyper, bool drawdelta, int nvar, mat oldbetas, double s, int maxuniq, int gridsize, double BayesmConstantA, int BayesmConstantnuInc, double BayesmConstantDPalpha){ // Wayne Taylor 2/21/2015 //Initialize variable placeholders int mkeep, Istar; vec betabar, q0v; mat rootpi, ucholinv, incroot, V; List compdraw(R/keep), nmix; DPOut mgout_struct; mnlMetropOnceOut metropout_struct; murooti thetaStarLgt_struct; int nz = Z.n_cols; int nlgt = lgtdata.size(); // convert List to std::vector of struct List lgtdatai; std::vector lgtdata_vector; moments lgtdatai_struct; for (int lgt = 0; lgt(lgtdatai["y"]); lgtdatai_struct.X = as(lgtdatai["X"]); lgtdatai_struct.hess = as(lgtdatai["hess"]); lgtdata_vector.push_back(lgtdatai_struct); } //initialize indicator vector, delta, thetaStar, thetaNp10, alpha, oldprob ivec indic = ones(nlgt); mat olddelta; if (drawdelta) olddelta = zeros(nz*nvar); std::vector thetaStar_vector(1); murooti thetaNp10_struct, thetaStar0_struct; thetaStar0_struct.mu = zeros(nvar); thetaStar0_struct.rooti = eye(nvar,nvar); thetaStar_vector[0] = thetaStar0_struct; double alpha = BayesmConstantDPalpha; //fix oldprob (only one comp) double oldprob = 1.0; //convert Prioralpha from List to struct priorAlpha priorAlpha_struct; priorAlpha_struct.power = PrioralphaList["power"]; priorAlpha_struct.alphamin = PrioralphaList["alphamin"]; priorAlpha_struct.alphamax = PrioralphaList["alphamax"]; priorAlpha_struct.n = PrioralphaList["n"]; //initialize lambda lambda lambda_struct; lambda_struct.mubar = zeros(nvar); lambda_struct.Amu = BayesmConstantA; lambda_struct.nu = nvar+BayesmConstantnuInc; lambda_struct.V = lambda_struct.nu*eye(nvar,nvar); //allocate space for draws mat Deltadraw(1,1); if(drawdelta) Deltadraw.zeros(R/keep, nz*nvar);//enlarge Deltadraw only if the space is required cube betadraw(nlgt, nvar, R/keep); vec probdraw = zeros(R/keep); vec oldll = zeros(nlgt); vec loglike = zeros(R/keep); vec Istardraw = zeros(R/keep); vec alphadraw = zeros(R/keep); vec nudraw = zeros(R/keep); vec vdraw = zeros(R/keep); vec adraw = zeros(R/keep); if (nprint>0) startMcmcTimer(); //start main iteration loop for(int rep = 0; rep0) if((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw.slice(mkeep-1) = oldbetas; probdraw[mkeep-1] = oldprob; alphadraw[mkeep-1] = alpha; Istardraw[mkeep-1] = Istar; adraw[mkeep-1] = lambda_struct.Amu; nudraw[mkeep-1] = lambda_struct.nu; V = lambda_struct.V; vdraw[mkeep-1] = V(0,0)/(lambda_struct.nu+0.0); loglike[mkeep-1] = sum(oldll); if(drawdelta) Deltadraw(mkeep-1, span::all) = trans(vectorise(olddelta)); thetaNp10_struct = mgout_struct.thetaNp1_vector[0]; //we have to convert to a NumericVector for the plotting functions to work compdraw[mkeep-1] = List::create(List::create(Named("mu") = NumericVector(thetaNp10_struct.mu.begin(),thetaNp10_struct.mu.end()),Named("rooti") = thetaNp10_struct.rooti)); } } if (nprint>0) endMcmcTimer(); nmix = List::create(Named("probdraw") = probdraw, Named("zdraw") = R_NilValue, //sets the value to NULL in R Named("compdraw") = compdraw); if(drawdelta){ return(List::create( Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("nmix") = nmix, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw, Named("loglike") = loglike)); } else { return(List::create( Named("betadraw") = betadraw, Named("nmix") = nmix, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw, Named("loglike") = loglike)); } } bayesm/NAMESPACE0000644000176000001440000000214712536133564013015 0ustar ripleyusersuseDynLib(bayesm) importFrom(Rcpp, evalCpp) ## exportPattern("^[[:alpha:]]+") this line is automatically created when using package.skeleton but should be removed to prevent the _loop functions from exporting. Instead use the export() function (as is done here) export(breg,createX,eMixMargDen,mixDen,fsh,llmnl,llmnp,llnhlogit, lndIChisq,lndIWishart,lndMvn,lndMvst,mnlHess,momMix,nmat,numEff,rdirichlet, rmixture,rmultireg, rwishart,rmvst,rtrun,rbprobitGibbs,runireg, runiregGibbs,simnhlogit,rmnpGibbs,rmixGibbs,rnmixGibbs, rmvpGibbs,rhierLinearModel,rhierMnlRwMixture,rivGibbs, rmnlIndepMetrop,rscaleUsage,ghkvec,condMom,logMargDenNR, rhierBinLogit,rnegbinRw,rhierNegbinRw,rbiNormGibbs,clusterMix,rsurGibbs, mixDenBi,mnpProb,rhierLinearMixture, summary.bayesm.mat,summary.bayesm.nmix,summary.bayesm.var, plot.bayesm.mat,plot.bayesm.hcoef,plot.bayesm.nmix, rordprobitGibbs,rivGibbs,rivDP,rDPGibbs, rhierMnlDP,cgetC,rbayesBLP) ## register S3 methods S3method(plot, bayesm.mat) S3method(plot, bayesm.nmix) S3method(plot, bayesm.hcoef) S3method(summary, bayesm.mat) S3method(summary, bayesm.var) S3method(summary, bayesm.nmix) bayesm/data/0000755000176000001440000000000012535644767012516 5ustar ripleyusersbayesm/data/detailing.rda0000644000176000001440000010076412516003354015132 0ustar ripleyusersý7zXZi"Þ6!ÏXÌê|²¶])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÍV6$iú«eË'ª‰d8Xïš«/8U£BS!Ì¥ ‚ªÞ› {/„/Þã‚VÄ^¶²¾@æ< ö¼CkpšJÚ.˜/RÀÃ̆„Ñ@s,«4<úwŸˆ èŽf:8ÜÇôˆºY‹‡•{'ó˜Åà¨î‹ **ä£2Ïq%_¯¨—¤!„3Êa5E5·Ö´ß×D7žnužïÿsÁ{uYØ kAÒ»åNœ©5E‚áÅ[Å[ꜹƒߢÜp/5&¬UPD•ã¯S·õæ„Ò|&,ê±+x÷ˆ°r‘ÓÀqª±û‚_ºSná->%YX[¢ ‚]»’Òž²™!uÓŠÑ‹,áp_•V ´ŸÔ§À!ò#¡áØ£œˆƒ@rÏÉ—a.f>c›Éߟ ÖýÊÍË ]¡º¹_ ñöA™{ørnE®}šÂŒÞ˜Ga7;ŽÿMY—C w]Eæø×)ÿo¹¶ûE¿éYžòÔÐ:DPb¦.¸ Y¤@›‰‚r¯A˜L½v‹FØvn ½ï™éƸá4×%Æ~Íž´ó ÿ_Û0˜f’#-¶‚rf¢ŽWô´3àÙõÖNG dü.î ûËCJ*M€¦ÀÎX½õä±R«Hv=OÑw’”@oK=í$2ð8¢¬zÃ[„Ý&R=,­8uáœo×0Òý&‰ÿ» ;ú®á£–Ó!†³G8¥öë–-8´Û%H ¨òˆ>„µ+ }–m'6—›>â!¸Ö¡£ÁûGv8E©ÿª‘Uð¨ÿÎè‚nÔÈvcC%¥þתœñ‰Â+ìiulìÀy€ú=ì!Eä” î¾(ðeßp~_âÐæÒ —âx²ÙjrËúÁ_›øw¨ûÑ×”‡ׇь=|X/À‘vp:˜,ª¿êï–;Œ^–k¥± ñŸ‹ÑSgX¯,¡Úí¸¡7‡ÝZ›_Ò½žÏv|RâQ+°ÃÄ#ØÌT¶?ékçAå­Ö3c×g²¬ÝFõ åkq{ë …6ehó’Ò1’Ù<Ý…?Zs;Çè“ÔC^Ö–$¬Ј@Åýn´¬N Ÿ̘«õÖ WÏÙ‰ßÅ&¨7ðч!œ~. <¬ï :‹B²/ß¶~Ç Š¢Àt ßéGÍÌñˆ¤ÕA<‚3HVI¬Õ•öKKÚÎ׉f’š½_!~cMRÍPðÆ•Ýí÷ôÕŒj] •pGöjK\Ž…‰z¬Ž“±ºÜÂJCºeƒ,|_ô='”YáoN2&ÆÀ± ñЦÅï&’xß$ù€o¹FË_[ðþä‰Ó,áxh½–és°”Scqת¶Ž [âmF pÆD½Îú1.!jã+œQ+ܰÁ­üNðUã‹ôíly`zΕ8æ;cZÒíK2®4»afô5¢Ü7Ü)cþT`!©I^@òŒÓbŸOÓ}êÚõ' úN@7<™7áRÔ¼:¯pÚ²Ø*´¯˜EýÎ?Q'•Ä^žxFÓ RcA:ÄóÐñQ¡‰¹c%¨f¨cÈ1¨KϸÏó†$…ÔÄtpU””°Ì^O]¼ƒ«m‚4ù@2ªCÍùoßÓb$™»²N›P"Ë´†“æÊÍEA*o œÛá¥Å®´ù€™’oó¦„êÐå¥Ë­›1¶t~DRRìüèFœSîyU—{Á«TtC5aP†¯®sÉri¦¸Ì]ZL(©þÇ[ÊQ)é¬1Pp êÞjÂÑÐ2ž«mæ` L\ñX´Gj#pyŠToòsKÇ:¨k¯!9Œ¡YUR®{ÁªÕ4ýííÔóñ«G{™a¹Ô¨ÖüÍNŸò5@™c“Y9·|JIv²7ÓX)£fÇ)dNN@Íy„ÛüM¼ ƒ1²{>sǤ!œûv;TŒ:¼²éÂp»w€ÿ•Ä‹ž5–@hU¾1~kqaÞºÀ{˜¬¿ïsÌ?x=vA½žj»Wtª\%üðu˜7¼ðòxÝ¿Žö?€îók:¾öWŒ¸Ã'0ïÕdžÜ…¤î÷q9t›yÐäüj9­2P!{Ùz¤þEa¥‹Emí ¸&õ«±ô+õãZ±:†™à[œ|¹æLlP1»ÿB¯â(Éc³évL_ì·Œ_tR¾ŸuyHаÆ(„µ}ìASÐñvXvl<ÙÜ$¤'ŒÓp¢;mMüwí;åxIòÏM-kçn/ãÙâ]úÑ'ƒÏ§â­É`þÅJ³˜íJè¡ÞÉØwx®ˆžMÆòùD¸G¹ ”oòн•…µÁײàÏ¡ñ¹ÀÇ À¨ƒ/óØ Æ–v·*68.D‚aà¶ß<6òIî_~ŸÙÙAh]´œ—K* ‘æz4ˆ¡ÝÚûNÇQ5¥”¸HA·“„ƒÃy4Ï9jðè¼iƒ)!N|öðÉ.¼fÁ>O`ñU­¹…D´#𢣫© Ùòîž°L.¶Ì›¡ jŒ£:UÌ6âŒáYôÁ<ÒÊ?Z))K q|(ãÒúD÷š"ÔHÆ € Êi78ßÚUkGõ$ÿ}}A£zo NZI鄚ˆ# W ôÞù ”KeR•3oOY•ûÌ”µ·Ïn ¼Ù'=§ÌnE~7®wÇ…p´ùûœðA3ã…èþÿ-C;`TÊù IÓ5E^¢— f}.‰Î…¤4æ œE´}Ò@ñϛߡ„Q?³uÕå{—Ã’õñégÌL·²|¿ûrcøçHƒÊ›ÊªðÏë|dKrϨ3G”ƒD„ڕꔫ:4*@ºfœÇob<°7ëRŽÈâ—flFÌé-7–¿q¶*)V£ù8ÁttÞ¦âY4ŸÅô8ü­'/¥…ÀŸrvycAV¿¼²o—¹Zÿ ÄmŸ¦ÏOÏŽmˆUíÊe"P›1mUžL*Vá\GwžYû,á„. tX¶9Pbì@Ë’€wžpk”m×çhi3ÑspüHŠÅLô^:{µÃÆŸâøfu•ÓcšU|aId A…Üç±¥iin6¨…Ÿ :yöaË]ȳ§‰681H»¬ÛØ«J} DCˆ.FëË/¸$ðéðtÞ>›ˆ€¨Ñ JëªÿԀηÙwøÄäÕç5·oÅó"žGÚ³[˜b€§¼|÷{î 8n¿¤è‚«bl…ø YBÖ4GÓÁ1Hpmr:2£€æ+ ÞûÉ}¾A[àå@°I«½]Ø\Ü0 £1®™åG}3=Xƻɭ™0zl‰½øvÍèò¼¼©÷¤Õô§+Ì¢`Þ^ 7?ò}• ×±H12Öñ<×>e­IéŽùqǶýýÒ/ÆgeäN*›7Žkp/Ý ñ‹D æGý‰ëÓ…Ñ Òç„L¨úÓ¸`g]‚81¤‘G[﹌ýŽLænÿâ°Õª Ù)FY¸‚ì 1,·Ô€ÙzVV¾MÛ€““£B˸³ÇVÄÈJ÷…a4K¶L”2³d§&¾§û[k BI¼L²í¸3#í“’@ÀEŒÉÒÅ]j¶ß=/£ÞœÓÏñ§”»§¹Êã±Ýµœ|5 ê p=¼¸¡Äæd«çý»Ý@G™DÀ‹k´/<ă"øÞ dÚõëp*P¡GÀ&ÅZ†ðãOñZ ÿ ®tâ ¢ f,ÝwG(T䛯eÝAlÁ%¬Ø¾#œÄ½IßB»>xAOjØ'ç)Ì>9¿€Ž÷ÁnèL:©Ì®²½z‹Û£‰ïDv®æY•*Lò|7ú;àl>ç»Õ³’Ø-k°}#£a$û½G&¯ÃovÈ2xû9=&zJãî|Ü>C¸7¾¨÷¬?,ã—ZÝÜ»EšÕµØg>|b ¾éášÒ»—z À½'S@wœ¹=Žî ªÓ§®wyjÜI\-„µB¢á±öc YɸuTFç7–ÊDqðÉ…ã3èÆÍíú*ATC•IÖ6~#õ¦\O ¸Çì‚ëÛˆï—Ög°^Ÿ©uŽhÐ÷zÑ„=I±ê߆ÙTv¸BÈXû­-!}ˆJTÙí[»ûk_ÅÒB¡F½¥‡¸qQ\»F_<—¸&å¾´‚ö:ü¤y1Ozr†Ëx¶Åø2ß®÷LÝB¸_—-òµ„8 *l¡ 01¹‚€¨ùÛDu·]Åé=ÍŽPmÍeñ%Î ri@Ÿt×ßßílJ P;}0uQ ¼¢•Ìãæ øJ>JsSðsiÞvO,x• @Èœ+t ¡7hnª 4ŸŠûýH)¿ù‰™¿{Þ@C/”ùDìó öÔ°9EwÎt¼rÏŘíÈ.èÍ_ÊÒ ½9Oàö?6B†žeÝÕ', U‚¤z`€o>Z¬•–€\,4U? 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Rossi 1/17/05 # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # Purpose: # perform Gibbs iterations for Univ Regression Model using # prior with beta, sigma-sq indep # # Arguments: # Data -- list of data # y,X # Prior -- list of prior hyperparameters # betabar,A prior mean, prior precision # nu, ssq prior on sigmasq # Mcmc -- list of MCMC parms # sigmasq=initial value for sigmasq # R number of draws # keep -- thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of beta, sigmasq # # Model: # y = Xbeta + e e ~N(0,sigmasq) # y is n x 1 # X is n x k # beta is k x 1 vector of coefficients # # Priors: beta ~ N(betabar,A^-1) # sigmasq ~ (nu*ssq)/chisq_nu # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y nvar=ncol(X) nobs=length(y) # # check data for validity # if(nobs != nrow(X) ) {pandterm("length(y) ne nrow(X)")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=.01*diag(nvar); nu=3; ssq=var(y)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=.01*diag(nvar)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=3} else {nu=Prior$nu} if(is.null(Prior$ssq)) {ssq=var(y)} else {ssq=Prior$ssq} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=1} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=100} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$sigmasq)) {sigmasq=var(y)} else {sigmasq=Mcmc$sigmasq} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Univariate Regression Model",fill=TRUE) cat(" with ",nobs," observations",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu = ",nu," ssq= ",ssq,fill=TRUE) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Keunwoo Kim # 08/05/2014 ################################################################### draws = runiregGibbs_rcpp_loop(y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$sigmasqdraw)$class=c("bayesm.mat","mcmc") attributes(draws$sigmasqdraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/nmat.R0000755000176000001440000000042710225356251013054 0ustar ripleyusersnmat=function(vec) { # # function to take var-cov matrix in vector form and create correlation matrix # and store in vector form # p=as.integer(sqrt(length(vec))) sigma=matrix(vec,ncol=p) nsig=1/sqrt(diag(sigma)) return(as.vector(nsig*(t(nsig*sigma)))) } bayesm/R/condMom.R0000755000176000001440000000117610227517711013515 0ustar ripleyuserscondMom= function(x,mu,sigi,i) { # # revision history: # rossi modified allenby code 4/05 # # purpose:compute moments of conditional distribution of ith element of normal given # all others # # arguments: # x: vector of values to condition on # mu: mean vector of length(x)-dim MVN # sigi: inverse of covariance matrix # i: element to condition on # # output: # list with conditional mean and variance # # Model: x ~MVN(mu,Sigma) # computes moments of x_i given x_{-1} # sig=1./sigi[i,i] m=mu[i] - as.vector(x[-i]-mu[-i])%*%as.vector(sigi[-i,i])*sig return(list(cmean=as.vector(m),cvar=sig)) } bayesm/R/runireg_rcpp.r0000644000176000001440000000655212524506034014656 0ustar ripleyusersrunireg= function(Data,Prior,Mcmc) { # # revision history: # P. Rossi 1/17/05 # revised 9/05 to put in Data,Prior,Mcmc calling convention # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # Purpose: # perform iid draws from posterior of regression model using # conjugate prior # # Arguments: # Data -- list of data # y,X # Prior -- list of prior hyperparameters # betabar,A prior mean, prior precision # nu, ssq prior on sigmasq # Mcmc -- list of MCMC parms # R number of draws # keep -- thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of beta, sigmasq # # Model: # y = Xbeta + e e ~N(0,sigmasq) # y is n x 1 # X is n x k # beta is k x 1 vector of coefficients # # Priors: beta ~ N(betabar,sigmasq*A^-1) # sigmasq ~ (nu*ssq)/chisq_nu # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y nvar=ncol(X) nobs=length(y) # # check data for validity # if(nobs != nrow(X) ) {pandterm("length(y) ne nrow(X)")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar); nu=BayesmConstant.nu; ssq=var(y)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=BayesmConstant.nu} else {nu=Prior$nu} if(is.null(Prior$ssq)) {ssq=var(y)} else {ssq=Prior$ssq} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting IID Sampler for Univariate Regression Model",fill=TRUE) cat(" with ",nobs," observations",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu = ",nu," ssq= ",ssq,fill=TRUE) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Keunwoo Kim # 08/05/2014 ################################################################### draws = runireg_rcpp_loop(y, X, betabar, A, nu, ssq, R, keep, nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$sigmasqdraw)$class=c("bayesm.mat","mcmc") attributes(draws$sigmasqdraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/momMix.R0000755000176000001440000000413110307453451013360 0ustar ripleyusersmomMix= function(probdraw,compdraw) { # # Revision History: # R. McCulloch 11/04 # P. Rossi 3/05 put in backsolve fixed documentation # P. Rossi 9/05 fixed error in mom -- return var not sigma # # purpose: compute moments of normal mixture averaged over MCMC draws # # arguments: # probdraw -- ith row is ith draw of probabilities of mixture comp # compdraw -- list of lists of draws of mixture comp moments (each sublist is from mixgibbs) # # output: # a list with the mean vector, covar matrix, vector of std deve, and corr matrix # # ---------------------------------------------------------------------------------- # define function needed mom=function(prob,comps){ # purpose: obtain mu and cov from list of normal components # # arguments: # prob: vector of mixture probs # comps: list, each member is a list comp with ith normal component ~N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # returns: # a list with [[1]]=$mu a vector # [[2]]=$sigma a matrix # nc = length(comps) dim = length(comps[[1]][[1]]) mu = double(dim) sigma = matrix(0.0,dim,dim) for(i in 1:nc) { mu = mu+ prob[i]*comps[[i]][[1]] } var=matrix(double(dim*dim),ncol=dim) for(i in 1:nc) { mui=comps[[i]][[1]] # root = solve(comps[[i]][[2]]) root=backsolve(comps[[i]][[2]],diag(rep(1,dim))) sigma=t(root)%*%root var=var+prob[i]*sigma+prob[i]*(mui-mu)%o%(mui-mu) } list(mu=mu,sigma=var) } #--------------------------------------------------------------------------------------- dim=length(compdraw[[1]][[1]][[1]]) nc=length(compdraw[[1]]) dim(probdraw)=c(length(compdraw),nc) mu=double(dim) sigma=matrix(double(dim*dim),ncol=dim) sd=double(dim) corr=matrix(double(dim*dim),ncol=dim) for(i in 1:length(compdraw)) { out=mom(probdraw[i,],compdraw[[i]]) sd=sd+sqrt(diag(out$sigma)) corr=corr+matrix(nmat(out$sigma),ncol=dim) mu=mu+out$mu sigma=sigma+out$sigma } mu=mu/length(compdraw) sigma=sigma/length(compdraw) sd=sd/length(compdraw) corr=corr/length(compdraw) return(list(mu=mu,sigma=sigma,sd=sd,corr=corr)) } bayesm/R/rnegbinrw_rcpp.r0000755000176000001440000001353412524506076015207 0ustar ripleyusersrnegbinRw=function(Data, Prior, Mcmc){ # Revision History # Sridhar Narayanan - 05/2005 # P. Rossi 6/05 # 3/07 added classes # Keunwoo Kim 11/2014 # 1. added "alphafix" argument # 2. corrected code in more clear way (in Cpp) # W. Taylor 4/15 - added nprint option to MCMC argument # # Model # (y|lambda,alpha) ~ Negative Binomial(Mean = lambda, Overdispersion par = alpha) # # ln(lambda) = X * beta # # Priors # beta ~ N(betabar, A^-1) # alpha ~ Gamma(a,b) where mean = a/b and variance = a/(b^2) # # Arguments # Data = list of y, X # e.g. regdata[[i]]=list(y=y,X=X) # X has nvar columns including a first column of ones # # Prior - list containing the prior parameters # betabar, A - mean of beta prior, inverse of variance covariance of beta prior # a, b - parameters of alpha prior # # Mcmc - list containing # R is number of draws # keep is thinning parameter (def = 1) # nprint - print estimated time remaining on every nprint'th draw (def = 100) # s_beta - scaling parameter for beta RW (def = 2.93/sqrt(nvar)) # s_alpha - scaling parameter for alpha RW (def = 2.93) # beta0 - initial guesses for parameters, if not supplied default values are used # alpha - value of alpha fixed. If it is given, draw only beta # # # Definitions of functions used within rhierNegbinRw # llnegbin = function(par,X,y, nvar) { # Computes the log-likelihood beta = par[1:nvar] alpha = exp(par[nvar+1])+1.0e-50 mean=exp(X%*%beta) prob=alpha/(alpha+mean) prob=ifelse(prob<1.0e-100,1.0e-100,prob) out=dnbinom(y,size=alpha,prob=prob,log=TRUE) return(sum(out)) } # # Error Checking # if(missing(Data)) {pandterm("Requires Data argument -- list of X and y")} if(is.null(Data$X)) {pandterm("Requires Data element X")} else {X=Data$X} if(is.null(Data$y)) {pandterm("Requires Data element y")} else {y=Data$y} nvar = ncol(X) if (length(y) != nrow(X)) {pandterm("Mismatch in the number of observations in X and y")} nobs=length(y) # # check for prior elements # if(missing(Prior)) { betabar=rep(0,nvar); A=BayesmConstant.A*diag(nvar) ; a=BayesmConstant.agammaprior; b=BayesmConstant.bgammaprior; } else { if(is.null(Prior$betabar)) {betabar=rep(0,nvar)} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$a)) {a=BayesmConstant.agammaprior} else {a=Prior$a} if(is.null(Prior$b)) {b=BayesmConstant.bgammaprior} else {b=Prior$b} } if(length(betabar) != nvar) pandterm("betabar is of incorrect dimension") if(sum(dim(A)==c(nvar,nvar)) != 2) pandterm("A is of incorrect dimension") if((length(a) != 1) | (a <=0)) pandterm("a should be a positive number") if((length(b) != 1) | (b <=0)) pandterm("b should be a positive number") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$beta0)) {beta0=rep(0,nvar)} else {beta0=Mcmc$beta0} if(length(beta0) !=nvar) pandterm("beta0 is not of dimension nvar") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$s_alpha)) {cat("Using default s_alpha = 2.93",fill=TRUE); s_alpha=BayesmConstant.RRScaling} else {s_alpha = Mcmc$s_alpha} if(is.null(Mcmc$s_beta)) {cat("Using default s_beta = 2.93/sqrt(nvar)",fill=TRUE); s_beta=BayesmConstant.RRScaling/sqrt(nvar)} else {s_beta = Mcmc$s_beta} # Keunwoo Kim 11/2014 ############################################# if(is.null(Mcmc$alpha)) {fixalpha=FALSE} else {fixalpha=TRUE; alpha=Mcmc$alpha} if(fixalpha & alpha<=0) pandterm("alpha is not positive") ################################################################### # # print out problem # cat(" ",fill=TRUE) cat("Starting Random Walk Metropolis Sampler for Negative Binomial Regression",fill=TRUE) cat(" ",nobs," obs; ",nvar," covariates (including intercept); ",fill=TRUE) cat("Prior Parameters:",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("a",fill=TRUE) print(a) cat("b",fill=TRUE) print(b) cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat("s_alpha = ",s_alpha,fill=TRUE) cat("s_beta = ",s_beta,fill=TRUE) cat(" ",fill=TRUE) par = rep(0,(nvar+1)) cat(" Initializing RW Increment Covariance Matrix...",fill=TRUE) fsh() mle = optim(par,llnegbin, X=X, y=y, nvar=nvar, method="L-BFGS-B", upper=c(Inf,Inf,Inf,log(100000000)), hessian=TRUE, control=list(fnscale=-1)) fsh() beta_mle=mle$par[1:nvar] alpha_mle = exp(mle$par[nvar+1]) varcovinv = -mle$hessian beta = beta0 betacvar = s_beta*solve(varcovinv[1:nvar,1:nvar]) betaroot = t(chol(betacvar)) if(!fixalpha) {alpha = alpha_mle} alphacvar = s_alpha/varcovinv[nvar+1,nvar+1] alphacroot = sqrt(alphacvar) cat("beta_mle = ",beta_mle,fill=TRUE) cat("alpha_mle = ",alpha_mle, fill = TRUE) fsh() ################################################################### # Keunwoo Kim # 09/03/2014 ################################################################### if (fixalpha) {alpha=Mcmc$alpha} draws=rnegbinRw_rcpp_loop(y, X, betabar, chol(A), a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$alphadraw)$class=c("bayesm.mat","mcmc") attributes(draws$alphadraw)$mcpar=c(1,R,keep) return(list(betadraw=draws$betadraw,alphadraw=draws$alphadraw, acceptrbeta=draws$nacceptbeta/R*keep,acceptralpha=draws$nacceptalpha/R*keep)) } bayesm/R/simnhlogit.R0000755000176000001440000000241612524673031014274 0ustar ripleyuserssimnhlogit=function(theta,lnprices,Xexpend) { # function to simulate non-homothetic logit model # creates y a n x 1 vector with indicator of choice (1,...,m) # lnprices is n x m array of log-prices faced # Xexpend is n x d array of variables predicting expenditure # # non-homothetic model specifies ln(psi_i(u))= alpha_i - exp(k_i)u # # structure of theta vector: # alpha (m x 1) # k (m x1 ) # gamma (k x 1) expenditure function coefficients # tau -- scaling of v # m=ncol(lnprices) n=nrow(lnprices) d=ncol(Xexpend) alpha=theta[1:m] k=theta[(m+1):(2*m)] gamma=theta[(2*m+1):(2*m+d)] tau=theta[length(theta)] iotam=c(rep(1,m)) c1=as.vector(Xexpend%*%gamma)%x%iotam-as.vector(t(lnprices))+alpha c2=c(rep(exp(k),n)) u=callroot(c1,c2,.0000001,20) v=alpha - u*exp(k)-as.vector(t(lnprices)) vmat=matrix(v,ncol=m,byrow=TRUE) vmat=tau*vmat Prob=exp(vmat) denom=Prob%*%iotam Prob=Prob/as.vector(denom) # draw y y=vector("double",n) ind=1:m for (i in 1:n) { yvec=rmultinom(1,1,Prob[i,]) y[i]=ind%*%yvec } return(list(y=y,Xexpend=Xexpend,lnprices=lnprices,theta=theta,prob=Prob)) }bayesm/R/llmnp.R0000755000176000001440000000373010316322312013227 0ustar ripleyusersllmnp= function(beta,Sigma,X,y,r) { # # revision history: # edited by rossi 2/8/05 # adde 1.0e-50 before taking log to avoid -Inf 6/05 # changed order of arguments to put beta first 9/05 # # purpose: # function to evaluate MNP likelihood using GHK # # arguments: # X is n*(p-1) x k array of covariates (including intercepts) # note: X is from the "differenced" system # y is vector of n indicators of multinomial response # beta is k x 1 with k = ncol(X) # Sigma is p-1 x p-1 # r is the number of random draws to use in GHK # # output -- value of log-likelihood # for each observation w = Xbeta + e e ~N(0,Sigma) # if y=j (j max(w_-j) and w_j >0 # if y=p, w < 0 # # to use GHK we must transform so that these are rectangular regions # e.g. if y=1, w_1 > 0 and w_1 - w_-1 > 0 # # define Aj such that if j=1,..,p-1, Ajw = Ajmu + Aje > 0 is equivalent to y=j # implies Aje > -Ajmu # lower truncation is -Ajmu and cov = AjSigma t(Aj) # # for p, e < - mu # # # define functions needed # ghkvec = function(L,trunpt,above,r){ dim=length(above) n=length(trunpt)/dim .C('ghk_vec',as.integer(n),as.double(L),as.double(trunpt),as.integer(above),as.integer(dim), as.integer(r),res=double(n))$res} # # compute means for each observation # pm1=ncol(Sigma) k=length(beta) mu=matrix(X%*%beta,nrow=pm1) logl=0.0 above=rep(0,pm1) for (j in 1:pm1) { muj=mu[,y==j] Aj=-diag(pm1) Aj[,j]=rep(1,pm1) trunpt=as.vector(-Aj%*%muj) Lj=t(chol(Aj%*%Sigma%*%t(Aj))) # note: rob's routine expects lower triangular root logl=logl + sum(log(ghkvec(Lj,trunpt,above,r)+1.0e-50)) # note: ghkvec does an entire vector of n probs each with different truncation points but the # same cov matrix. } # # now do obs for y=p # trunpt=as.vector(-mu[,y==(pm1+1)]) Lj=t(chol(Sigma)) above=rep(1,pm1) logl=logl+sum(log(ghkvec(Lj,trunpt,above,r)+1.0e-50)) return(logl) } bayesm/R/rordprobitgibbs_rcpp.r0000755000176000001440000001355412524506101016376 0ustar ripleyusersrordprobitGibbs=function(Data,Prior,Mcmc){ # # revision history: # 3/07 Hsiu-Wen Liu # 3/07 fixed naming of dstardraw rossi # # purpose: # draw from posterior for ordered probit using Gibbs Sampler # and metropolis RW # # Arguments: # Data - list of X,y,k # X is nobs x nvar, y is nobs vector of 1,2,.,k (ordinal variable) # Prior - list of A, betabar # A is nvar x nvar prior preci matrix # betabar is nvar x 1 prior mean # Ad is ndstar x ndstar prior preci matrix of dstar (ncut is number of cut-offs being estimated) # dstarbar is ndstar x 1 prior mean of dstar # Mcmc # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # s is scale parameter of random work Metropolis # # Output: # list of betadraws and cutdraws # # Model: # z=Xbeta + e < 0 e ~N(0,1) # y=1,..,k, if z~c(c[k], c[k+1]) # # cutoffs = c[1],..,c[k+1] # dstar = dstar[1],dstar[k-2] # set c[1]=-100, c[2]=0, ...,c[k+1]=100 # # c[3]=exp(dstar[1]),c[4]=c[3]+exp(dstar[2]),..., # c[k]=c[k-1]+exp(datsr[k-2]) # # Note: 1. length of dstar = length of cutoffs - 3 # 2. Be careful in assessing prior parameter, Ad. .1 is too small for many applications. # # Prior: beta ~ N(betabar,A^-1) # dstar ~ N(dstarbar, Ad^-1) # # # ---------------------------------------------------------------------- # define functions needed # dstartoc is a fuction to transfer dstar to its cut-off value dstartoc=function(dstar) {c(-100, 0, cumsum(exp(dstar)), 100)} # compute conditional likelihood of data given cut-offs # lldstar=function(dstar,y,mu){ gamma=dstartoc(dstar) arg = pnorm(gamma[y+1]-mu)-pnorm(gamma[y]-mu) epsilon=1.0e-50 arg=ifelse(arg < epsilon,epsilon,arg) return(sum(log(arg))) } # # ---------------------------------------------------------------------- # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y if(is.null(Data$k)) {pandterm("Requires Data element k")} k=Data$k nvar=ncol(X) nobs=length(y) ndstar = k-2 # number of dstar being estimated ncuts = k+1 # number of cut-offs (including zero and two ends) ncut = ncuts-3 # number of cut-offs being estimated c[1]=-100, c[2]=0, c[k+1]=100 # # check data for validity # if(length(y) != nrow(X) ) {pandterm("y and X not of same row dim")} if( sum(unique(y) %in% (1:k) ) < length(unique(y)) ) {pandterm("some value of y is not vaild")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar); Ad=diag(ndstar); dstarbar=c(rep(0,ndstar))} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$Ad)) {Ad=diag(ndstar)} else {Ad=Prior$Ad} if(is.null(Prior$dstarbar)) {dstarbar=c(rep(0,ndstar))} else {dstarbar=Prior$dstarbar} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} if(ncol(Ad) != nrow(Ad) || ncol(Ad) != ndstar || nrow(Ad) != ndstar) {pandterm(paste("bad dimensions for Ad",dim(Ad)))} if(length(dstarbar) != ndstar) {pandterm(paste("dstarbar wrong length, length= ",length(dstarbar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$s)) {s=BayesmConstant.RRScaling/sqrt(ndstar)} else {s=Mcmc$s} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Ordered Probit Model",fill=TRUE) cat(" with ",nobs,"observations",fill=TRUE) cat(" ", fill=TRUE) cat("Table of y values",fill=TRUE) print(table(y)) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat(" ", fill=TRUE) cat("A",fill=TRUE) print(A) cat(" ", fill=TRUE) cat("dstarbar",fill=TRUE) print(dstarbar) cat(" ", fill=TRUE) cat("Ad",fill=TRUE) print(Ad) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,"s= ",s, fill=TRUE) cat(" ",fill=TRUE) # use (-Hessian+Ad)^(-1) evaluated at betahat as the basis of the # covariance matrix for the random walk Metropolis increments betahat = chol2inv(chol(crossprod(X,X)))%*% crossprod(X,y) dstarini = c(cumsum(c( rep(0.1, ndstar)))) # set initial value for dstar dstarout = optim(dstarini, lldstar, method = "BFGS", hessian=T, control = list(fnscale = -1,maxit=500, reltol = 1e-06, trace=0), mu=X%*%betahat, y=y) inc.root=chol(chol2inv(chol((-dstarout$hessian+Ad)))) # chol((H+Ad)^-1) ################################################################### # Keunwoo Kim # 08/20/2014 ################################################################### draws=rordprobitGibbs_rcpp_loop(y,X,k,A,betabar,Ad,s,inc.root,dstarbar,betahat,R,keep,nprint) ################################################################### draws$cutdraw=draws$cutdraw[,2:k] attributes(draws$cutdraw)$class="bayesm.mat" attributes(draws$betadraw)$class="bayesm.mat" attributes(draws$dstardraw)$class="bayesm.mat" attributes(draws$cutdraw)$mcpar=c(1,R,keep) attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$dstardraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/rhiernegbinrw_rcpp.r0000644000176000001440000002154012536144661016051 0ustar ripleyusersrhierNegbinRw= function(Data, Prior, Mcmc) { # Revision History # Sridhar Narayanan - 05/2005 # P. Rossi 6/05 # fixed error with nobs not specified and changed llnegbinFract 9/05 # 3/07 added classes # 3/08 fixed fractional likelihood # W. Taylor 4/15 - added nprint option to MCMC argument # # Model # (y_i|lambda_i,alpha) ~ Negative Binomial(Mean = lambda_i, Overdispersion par = alpha) # # ln(lambda_i) = X_i * beta_i # # beta_i = Delta'*z_i + nu_i # nu_i~N(0,Vbeta) # # Priors # vec(Delta|Vbeta) ~ N(vec(Deltabar), Vbeta (x) (Adelta^-1)) # Vbeta ~ Inv Wishart(nu, V) # alpha ~ Gamma(a,b) where mean = a/b and variance = a/(b^2) # # Arguments # Data = list of regdata,Z # regdata is a list of lists each list with members y, X # e.g. regdata[[i]]=list(y=y,X=X) # X has nvar columns including a first column of ones # Z is nreg=length(regdata) x nz with a first column of ones # # Prior - list containing the prior parameters # Deltabar, Adelta - mean of Delta prior, inverse of variance covariance of Delta prior # nu, V - parameters of Vbeta prior # a, b - parameters of alpha prior # # Mcmc - list containing # R is number of draws # keep is thinning parameter (def = 1) # nprint - print estimated time remaining on every nprint'th draw (def = 100) # s_beta - scaling parameter for beta RW (def = 2.93/sqrt(nvar)) # s_alpha - scaling parameter for alpha RW (def = 2.93) # w - fractional weighting parameter (def = .1) # Vbeta0, Delta0 - initial guesses for parameters, if not supplied default values are used # # # Definitions of functions used within rhierNegbinRw (but outside of Rcpp loop) # llnegbinR = function(par,X,y, nvar) { # Computes the log-likelihood beta = par[1:nvar] alpha = exp(par[nvar+1])+1.0e-50 mean=exp(X%*%beta) prob=alpha/(alpha+mean) prob=ifelse(prob<1.0e-100,1.0e-100,prob) out=dnbinom(y,size=alpha,prob=prob,log=TRUE) return(sum(out)) } llnegbinFract = function(par,X,y,Xpooled, ypooled, w,wgt, nvar,lnalpha) { # Computes the fractional log-likelihood at the unit level theta = c(par,lnalpha) (1-w)*llnegbinR(theta,X,y,nvar) + w*wgt*llnegbinR(theta,Xpooled,ypooled, nvar) } # # Error Checking # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata and (possibly) Z")} if(is.null(Data$regdata)) { pandterm("Requires Data element regdata -- list of data for each unit : y and X") } regdata=Data$regdata nreg = length(regdata) if (is.null(Data$Z)) { cat("Z not specified - using a column of ones instead", fill = TRUE) Z = matrix(rep(1,nreg),ncol=1) } else { if (nrow(Data$Z) != nreg) { pandterm(paste("Nrow(Z) ", nrow(Z), "ne number units ",nreg)) } else { Z = Data$Z } } nz = ncol(Z) dimfun = function(l) { c(length(l$y),dim(l$X)) } dims=sapply(regdata,dimfun) dims = t(dims) nvar = quantile(dims[,3],prob=0.5) for (i in 1:nreg) { if (dims[i, 1] != dims[i, 2] || dims[i, 3] != nvar) { pandterm(paste("Bad Data dimensions for unit ", i, " dims(y,X) =", dims[i, ])) } } ypooled = NULL Xpooled = NULL for (i in 1:nreg) { ypooled = c(ypooled,regdata[[i]]$y) Xpooled = rbind(Xpooled,regdata[[i]]$X) } nobs= length(ypooled) nvar=ncol(Xpooled) # # check for prior elements # if(missing(Prior)) { Deltabar=matrix(rep(0,nvar*nz),nrow=nz) ; Adelta=BayesmConstant.A*diag(nz) ; nu=nvar+BayesmConstant.nuInc; V=nu*diag(nvar); a=0.5; b=0.1; } else { if(is.null(Prior$Deltabar)) {Deltabar=matrix(rep(0,nvar*nz),nrow=nz)} else {Deltabar=Prior$Deltabar} if(is.null(Prior$Adelta)) {Adelta=BayesmConstant.A*diag(nz)} else {Adelta=Prior$Adelta} if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V} if(is.null(Prior$a)) {a=BayesmConstant.agammaprior} else {a=Prior$a} if(is.null(Prior$b)) {b=BayesmConstant.bgammaprior} else {b=Prior$b} } if(sum(dim(Deltabar) == c(nz,nvar)) != 2) pandterm("Deltabar is of incorrect dimension") if(sum(dim(Adelta)==c(nz,nz)) != 2) pandterm("Adelta is of incorrect dimension") if(nu < nvar) pandterm("invalid nu value") if(sum(dim(V)==c(nvar,nvar)) != 2) pandterm("V is of incorrect dimension") if((length(a) != 1) | (a <=0)) pandterm("a should be a positive number") if((length(b) != 1) | (b <=0)) pandterm("b should be a positive number") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$Vbeta0)) {Vbeta0=diag(nvar)} else {Vbeta0=Mcmc$Vbeta0} if(sum(dim(Vbeta0) == c(nvar,nvar)) !=2) pandterm("Vbeta0 is not of dimension nvar") if(is.null(Mcmc$Delta0)) {Delta0=matrix(rep(0,nz*nvar),nrow=nz)} else {Delta0=Mcmc$Delta0} if(sum(dim(Delta0) == c(nz,nvar)) !=2) pandterm("Delta0 is not of dimension nvar by nz") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$s_alpha)) { s_alpha=BayesmConstant.RRScaling} else {s_alpha= Mcmc$s_alpha } if(is.null(Mcmc$s_beta)) { s_beta=BayesmConstant.RRScaling/sqrt(nvar)} else {s_beta=Mcmc$s_beta } if(is.null(Mcmc$w)) { w=BayesmConstant.w} else {w = Mcmc$w} # Wayne Taylor 12/2014 ############################################# if(is.null(Mcmc$alpha)) {fixalpha=FALSE} else {fixalpha=TRUE; alpha=Mcmc$alpha} if(fixalpha & alpha<=0) pandterm("alpha is not positive") ################################################################### # print out problem # cat(" ",fill=TRUE) cat("Starting Random Walk Metropolis Sampler for Hierarchical Negative Binomial Regression",fill=TRUE) cat(" ",nobs," obs; ",nvar," covariates (including the intercept); ",fill=TRUE) cat(" ",nz," individual characteristics (including the intercept) ",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parameters:",fill=TRUE) cat("Deltabar",fill=TRUE) print(Deltabar) cat("Adelta",fill=TRUE) print(Adelta) cat("nu",fill=TRUE) print(nu) cat("V",fill=TRUE) print(V) cat("a",fill=TRUE) print(a) cat("b",fill=TRUE) print(b) cat(" ",fill=TRUE) cat("MCMC Parameters:",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat("s_alpha = ",s_alpha,fill=TRUE) cat("s_beta = ",s_beta,fill=TRUE) cat("Fractional Likelihood Weight Parameter = ",w,fill=TRUE) cat(" ",fill=TRUE) par = rep(0,(nvar+1)) cat("initializing Metropolis candidate densities for ",nreg,"units ...",fill=TRUE) fsh() mle = optim(par,llnegbinR, X=Xpooled, y=ypooled, nvar=nvar, method="L-BFGS-B", upper=c(Inf,Inf,Inf,log(100000000)), hessian=TRUE, control=list(fnscale=-1)) fsh() beta_mle=mle$par[1:nvar] alpha_mle = exp(mle$par[nvar+1]) varcovinv = -mle$hessian Delta = Delta0 Beta = t(matrix(rep(beta_mle,nreg),ncol=nreg)) Vbetainv = chol2inv(chol(Vbeta0)) #Wayne: replaced "solve" function Vbeta = Vbeta0 alpha = alpha_mle alphacvar = s_alpha/varcovinv[nvar+1,nvar+1] alphacroot = sqrt(alphacvar) cat("beta_mle = ",beta_mle,fill=TRUE) cat("alpha_mle = ",alpha_mle, fill = TRUE) fsh() hess_i=NULL if(nobs > 1000){ sind=sample(c(1:nobs),size=1000) ypooleds=ypooled[sind] Xpooleds=Xpooled[sind,] } else{ ypooleds=ypooled Xpooleds=Xpooled } # Find the individual candidate hessian for (i in 1:nreg) { wgt = length(regdata[[i]]$y)/length(ypooleds) mle2 = optim(mle$par[1:nvar],llnegbinFract, X=regdata[[i]]$X, y=regdata[[i]]$y, Xpooled=Xpooleds, ypooled=ypooleds, w=w,wgt=wgt, nvar=nvar, lnalpha=mle$par[nvar+1], method="BFGS", hessian=TRUE, control=list(fnscale=-1, trace=0,reltol=1e-6)) if (mle2$convergence==0) hess_i[[i]] = list(hess=-mle2$hessian) else hess_i[[i]] = diag(rep(1,nvar)) if(i%%50 ==0) cat(" completed unit #",i,fill=TRUE) fsh() } ################################################################### # Wayne Taylor # 12/01/2014 ################################################################### if (fixalpha) {alpha=Mcmc$alpha} rootA = chol(Vbetainv) draws=rhierNegbinRw_rcpp_loop(regdata, hess_i, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, s_beta, alphacroot, 1000, rootA, alpha, fixalpha) ################################################################### attributes(draws$alphadraw)$class=c("bayesm.mat","mcmc") attributes(draws$alphadraw)$mcpar=c(1,R,keep) attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep) attributes(draws$Vbetadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Vbetadraw)$mcpar=c(1,R,keep) attributes(draws$Betadraw)$class=c("bayesm.hcoef") return(draws) }bayesm/R/mixDenBi.R0000755000176000001440000000426610545276104013624 0ustar ripleyusersmixDenBi= function(i,j,xi,xj,pvec,comps) { # Revision History: # P. Rossi 6/05 # vectorized evaluation of bi-variate normal density 12/06 # # purpose: compute marg bivariate density implied by mixture of multivariate normals specified # by pvec,comps # # arguments: # i,j: index of two variables # xi specifies a grid of points for var i # xj specifies a grid of points for var j # pvec: prior probabilities of normal components # comps: list, each member is a list comp with ith normal component ~ N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # Output: # matrix with values of density on grid # # --------------------------------------------------------------------------------------------- # define function needed # bivcomps=function(i,j,comps) { # purpose: obtain marginal means and standard deviations from list of normal components # arguments: # i,j: index of elements for bivariate marginal # comps: list, each member is a list comp with ith normal component ~N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # returns: # a list with relevant mean vectors and rooti for each compenent # [[2]]=$sigma a matrix whose ith row is the standard deviations for the ith component # result=NULL nc = length(comps) dim = length(comps[[1]][[1]]) ind=matrix(c(i,j,i,j,i,i,j,j),ncol=2) for(comp in 1:nc) { mu = comps[[comp]][[1]][c(i,j)] root= backsolve(comps[[comp]][[2]],diag(dim)) Sigma=crossprod(root) sigma=matrix(Sigma[ind],ncol=2) rooti=backsolve(chol(sigma),diag(2)) result[[comp]]=list(mu=mu,rooti=rooti) } return(result) } # ---------------------------------------------------------------------------------------------- nc = length(comps) marmoms=bivcomps(i,j,comps) ngridxi=length(xi); ngridxj=length(xj) z=cbind(rep(xi,ngridxj),rep(xj,each=ngridxi)) den = matrix(0.0,nrow=ngridxi,ncol=ngridxj) for(comp in 1:nc) { quads=colSums((crossprod(marmoms[[comp]]$rooti,(t(z)-marmoms[[comp]]$mu)))^2) dencomp=exp(-(2/2)*log(2*pi)+sum(log(diag(marmoms[[comp]]$rooti)))-.5*quads) dim(dencomp)=c(ngridxi,ngridxj) den=den+dencomp*pvec[comp] } return(den) } bayesm/R/createX.R0000755000176000001440000000502512524505423013510 0ustar ripleyuserscreateX= function(p,na,nd,Xa,Xd,INT=TRUE,DIFF=FALSE,base=p) { # # Revision History: # P. Rossi 3/05 # # purpose: # function to create X array in format needed MNL and MNP routines # # Arguments: # p is number of choices # na is number of choice attribute variables (choice-specific characteristics) # nd is number of "demo" variables or characteristics of choosers # Xa is a n x (nx*p) matrix of choice attributes. First p cols are # values of attribute #1 for each of p chocies, second p for attribute # # 2 ... # Xd is an n x nd matrix of values of "demo" variables # INT is a logical flag for intercepts # DIFF is a logical flag for differencing wrt to base alternative # (required for MNP) # base is base alternative (default is p) # # note: if either you don't have any attributes or "demos", set # corresponding na, XA or nd,XD to NULL # YOU must specify p,na,nd,XA,XD for the function to work # # Output: # modified X matrix with n*p rows and INT*(p-1)+nd*(p-1) + na cols # # # check arguments # if(missing(p)) pandterm("requires p (# choice alternatives)") if(missing(na)) pandterm("requires na arg (use na=NULL if none)") if(missing(nd)) pandterm("requires nd arg (use nd=NULL if none)") if(missing(Xa)) pandterm("requires Xa arg (use Xa=NULL if none)") if(missing(Xd)) pandterm("requires Xd arg (use Xd=NULL if none)") if(is.null(Xa) && is.null(Xd)) pandterm("both Xa and Xd NULL -- requires one non-null") if(!is.null(na) && !is.null(Xa)) {if(ncol(Xa) != p*na) pandterm(paste("bad Xa dim, dim=",dim(Xa)))} if(!is.null(nd) && !is.null(Xd)) {if(ncol(Xd) != nd) pandterm(paste("ncol(Xd) ne nd, ncol(Xd)=",ncol(Xd)))} if(!is.null(Xa) && !is.null(Xd)) {if(nrow(Xa) != nrow(Xd)) {pandterm(paste("nrow(Xa) ne nrow(Xd),nrow(Xa)= ",nrow(Xa)," nrow(Xd)= ",nrow(Xd)))}} if(is.null(Xa)) {n=nrow(Xd)} else {n=nrow(Xa)} if(INT) {Xd=cbind(c(rep(1,n)),Xd)} if(DIFF) {Imod=diag(p-1)} else {Imod=matrix(0,p,p-1); Imod[-base,]=diag(p-1)} if(!is.null(Xd)) Xone=Xd %x%Imod else Xone=NULL Xtwo=NULL if(!is.null(Xa)) {if(DIFF) {tXa=matrix(t(Xa),nrow=p) Idiff=diag(p); Idiff[,base]=c(rep(-1,p));Idiff=Idiff[-base,] tXa=Idiff%*%tXa Xa=matrix(as.vector(tXa),ncol=(p-1)*na,byrow=TRUE) for (i in 1:na) {Xext=Xa[,((i-1)*(p-1)+1):((i-1)*(p-1)+p-1)] Xtwo=cbind(Xtwo,as.vector(t(Xext)))} } else { for (i in 1:na) { Xext=Xa[,((i-1)*p+1):((i-1)*p+p)] Xtwo=cbind(Xtwo,as.vector(t(Xext)))} } } return(cbind(Xone,Xtwo)) } bayesm/R/plot.bayesm.nmix.R0000755000176000001440000000773211754251324015335 0ustar ripleyusersplot.bayesm.nmix=function(x,names,burnin=trunc(.1*nrow(probdraw)),Grid,bi.sel,nstd=2,marg=TRUE, Data,ngrid=50,ndraw=200,...){ # # S3 method to plot normal mixture marginal and bivariate densities # nmixlist is a list of 3 components, nmixlist[[1]]: array of mix comp prob draws, # mmixlist[[2]] is not used, nmixlist[[3]] is list of draws of components # P. Rossi 2/07 # P. Rossi 3/07 fixed problem with dropping dimensions on probdraw (if ncomp=1) # P. Rossi 2/08 added marg flag to plot marginals # P. Rossi 3/08 added Data argument to paint histograms on the marginal plots # nmixlist=x if(mode(nmixlist) != "list") stop(" Argument must be a list \n") probdraw=nmixlist[[1]]; compdraw=nmixlist[[3]] if(!is.matrix(probdraw)) stop(" First element of list (probdraw) must be a matrix \n") if(mode(compdraw) != "list") stop(" Third element of list (compdraw) must be a list \n") op=par(no.readonly=TRUE) on.exit(par(op)) R=nrow(probdraw) if(R < 100) {cat(" fewer than 100 draws submitted \n"); return(invisible())} datad=length(compdraw[[1]][[1]]$mu) OneDimData=(datad==1) if(missing(bi.sel)) bi.sel=list(c(1,2)) # default to the first pair of variables ind=as.integer(seq(from=(burnin+1),to=R,length.out=max(ndraw,trunc(.05*R)))) if(missing(names)) {names=as.character(1:datad)} if(!missing(Data)){ if(!is.matrix(Data)) stop("Data argument must be a matrix \n") if(ncol(Data)!= datad) stop("Data matrix is of wrong dimension \n") } if(mode(bi.sel) != "list") stop("bi.sel must be as list, e.g. bi.sel=list(c(1,2),c(3,4)) \n") if(missing(Grid)){ Grid=matrix(0,nrow=ngrid,ncol=datad) if(!missing(Data)) {for(i in 1:datad) Grid[,i]=c(seq(from=range(Data[,i])[1],to=range(Data[,i])[2],length=ngrid))} else { out=momMix(probdraw[ind,,drop=FALSE],compdraw[ind]) mu=out$mu sd=out$sd for(i in 1:datad ) Grid[,i]=c(seq(from=(mu[i]-nstd*sd[i]), to=(mu[i]+nstd*sd[i]),length=ngrid)) } } # # plot posterior mean of marginal densities # if(marg){ mden=eMixMargDen(Grid,probdraw[ind,,drop=FALSE],compdraw[ind]) nx=datad if(nx==1) par(mfrow=c(1,1)) if(nx==2) par(mfrow=c(2,1)) if(nx==3) par(mfrow=c(3,1)) if(nx==4) par(mfrow=c(2,2)) if(nx>=5) par(mfrow=c(3,2)) for(index in 1:nx){ if(index == 2) par(ask=dev.interactive()) plot(range(Grid[,index]),c(0,1.1*max(mden[,index])),type="n",xlab="",ylab="density") title(names[index]) if(!missing(Data)){ deltax=(range(Grid[,index])[2]-range(Grid[,index])[1])/nrow(Grid) hist(Data[,index],xlim=range(Grid[,index]), freq=FALSE,col="yellow",breaks=max(20,.1*nrow(Data)),add=TRUE) lines(Grid[,index],mden[,index]/(sum(mden[,index])*deltax),col="red",lwd=2)} else {lines(Grid[,index],mden[,index],col="black",lwd=2) polygon(c(Grid[1,index],Grid[,index],Grid[nrow(Grid),index]),c(0,mden[,index],0),col="magenta")} } } # # now plot bivariates in list bi.sel # if(!OneDimData){ par(ask=dev.interactive()) nsel=length(bi.sel) den=array(0,dim=c(ngrid,ngrid,nsel)) lstxixj=NULL for(sel in 1:nsel){ i=bi.sel[[sel]][1] j=bi.sel[[sel]][2] xi=Grid[,i] xj=Grid[,j] lstxixj[[sel]]=list(xi,xj) for(elt in ind){ den[,,sel]=den[,,sel]+mixDenBi(i,j,xi,xj,probdraw[elt,,drop=FALSE],compdraw[[elt]]) } den[,,sel]=den[,,sel]/sum(den[,,sel]) } nx=nsel par(mfrow=c(1,1)) for(index in 1:nx){ xi=unlist(lstxixj[[index]][1]) xj=unlist(lstxixj[[index]][2]) xlabtxt=names[bi.sel[[index]][1]] ylabtxt=names[bi.sel[[index]][2]] image(xi,xj,den[,,index],col=terrain.colors(100),xlab=xlabtxt,ylab=ylabtxt) contour(xi,xj,den[,,index],add=TRUE,drawlabels=FALSE) } } invisible() } bayesm/R/logMargDenNR.R0000755000176000001440000000071110227516062014370 0ustar ripleyuserslogMargDenNR = function(ll) { # # purpose: compute log marginal density using Newton-Raftery # importance sampling estimator: 1/ (1/g sum_g exp(-log like) ) # where log like is the likelihood of the model evaluated as the # posterior draws (x). # # arguments: # ll -- vector of log-likelihood values evaluated at posterior draws # # output: # estimated log-marginal density med=median(ll) return(med-log(mean(exp(-ll+med)))) } bayesm/R/mnpProb.R0000644000176000001440000000275312533663350013540 0ustar ripleyusersmnpProb= function(beta,Sigma,X,r=100) { # # revision history: # written by Rossi 9/05 # W. Taylor 4/15 - replaced ghkvec call with rcpp version # # purpose: # function to MNP probabilities for a given X matrix (corresponding # to "one" observation # # arguments: # X is p-1 x k array of covariates (including intercepts) # note: X is from the "differenced" system # beta is k x 1 with k = ncol(X) # Sigma is p-1 x p-1 # r is the number of random draws to use in GHK # # output -- probabilities # for each observation w = Xbeta + e e ~N(0,Sigma) # if y=j (j max(w_-j) and w_j >0 # if y=p, w < 0 # # to use GHK we must transform so that these are rectangular regions # e.g. if y=1, w_1 > 0 and w_1 - w_-1 > 0 # # define Aj such that if j=1,..,p-1, Ajw = Ajmu + Aje > 0 is equivalent to y=j # implies Aje > -Ajmu # lower truncation is -Ajmu and cov = AjSigma t(Aj) # # for p, e < - mu # # pm1=ncol(Sigma) k=length(beta) mu=matrix(X%*%beta,nrow=pm1) above=rep(0,pm1) prob=double(pm1+1) for (j in 1:pm1) { Aj=-diag(pm1) Aj[,j]=rep(1,pm1) trunpt=as.vector(-Aj%*%mu) Lj=t(chol(Aj%*%Sigma%*%t(Aj))) # note: rob's routine expects lower triangular root prob[j]=ghkvec(Lj,trunpt,above,r) # note: ghkvec does an entire vector of n probs each with different truncation points but the # same cov matrix. } # # now do pth alternative # prob[pm1+1]=1-sum(prob[1:pm1]) return(prob) } bayesm/R/BayesmFunctions.R0000644000176000001440000000007012524505375015224 0ustar ripleyuserspandterm=function(message) { stop(message,call.=FALSE) }bayesm/R/rdpgibbs_rcpp.r0000644000176000001440000001566712524506307015011 0ustar ripleyusersrDPGibbs=function(Prior,Data,Mcmc){ # # Revision History: # 5/06 add rthetaDP # 7/06 include rthetaDP in main body to avoid copy overhead # 1/08 add scaling # 2/08 add draw of lambda # 3/08 changed nu prior support to dim(y) + exp(unif gird on nulim[1],nulim[2]) # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: do Gibbs sampling for density estimation using Dirichlet process model # # arguments: # Data is a list of y which is an n x k matrix of data # Prior is a list of (alpha,lambda,Prioralpha) # alpha: starting value # lambda_hyper: hyperparms of prior on lambda # Prioralpha: hyperparms of alpha prior; a list of (Istarmin,Istarmax,power) # if elements of the prior don't exist, defaults are assumed # Mcmc is a list of (R,keep,maxuniq) # R: number of draws # keep: thinning parameter # maxuniq: the maximum number of unique thetaStar values # nprint - print estimated time remaining on every nprint'th draw # # Output: # list with elements # alphadraw: vector of length R/keep, [i] is ith draw of alpha # Istardraw: vector of length R/keep, [i] is the number of unique theta's drawn from ith iteration # adraw # nudraw # vdraw # thetaNp1draws: list, [[i]] is ith draw of theta_{n+1} # inddraw: R x n matrix, [,i] is indicators of identity for each obs in ith iteration # # Model: # y_i ~ f(y|thetai) # thetai|G ~ G # G|lambda,alpha ~ DP(G|G0(lambda),alpha) # # Priors: # alpha: starting value # # lambda: # G0 ~ N(mubar,Sigma (x) Amu^-1) # mubar=vec(mubar) # Sigma ~ IW(nu,nu*v*I) note: mode(Sigma)=nu/(nu+2)*v*I # mubar=0 # amu is uniform on grid specified by alim # nu is log uniform, nu=d-1+exp(Z) z is uniform on seq defined bvy nulim # v is uniform on sequence specificd by vlim # # Prioralpha: # alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power # alphamin=exp(digamma(Istarmin)-log(gamma+log(N))) # alphamax=exp(digamma(Istarmax)-log(gamma+log(N))) # gamma= .5772156649015328606 # # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y")} if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y # # check data for validity # if(!is.matrix(y)) {pandterm("y must be a matrix")} nobs=nrow(y) dimy=ncol(y) # # check for Prior # alimdef=BayesmConstant.DPalimdef nulimdef=BayesmConstant.DPnulimdef vlimdef=BayesmConstant.DPvlimdef if(missing(Prior)) {pandterm("requires Prior argument ")} else { if(is.null(Prior$lambda_hyper)) {lambda_hyper=list(alim=alimdef,nulim=nulimdef,vlim=vlimdef)} else {lambda_hyper=Prior$lambda_hyper; if(is.null(lambda_hyper$alim)) {lambda_hyper$alim=alimdef} if(is.null(lambda_hyper$nulim)) {lambda_hyper$nulim=nulimdef} if(is.null(lambda_hyper$vlim)) {lambda_hyper$vlim=vlimdef} } if(is.null(Prior$Prioralpha)) {Prioralpha=list(Istarmin=BayesmConstant.DPIstarmin,Istarmax=min(50,0.1*nobs),power=BayesmConstant.DPpower)} else {Prioralpha=Prior$Prioralpha; if(is.null(Prioralpha$Istarmin)) {Prioralpha$Istarmin=BayesmConstant.DPIstarmin} else {Prioralpha$Istarmin=Prioralpha$Istarmin} if(is.null(Prioralpha$Istarmax)) {Prioralpha$Istarmax=min(50,0.1*nobs)} else {Prioralpha$Istarmax=Prioralpha$Istarmax} if(is.null(Prioralpha$power)) {Prioralpha$power=BayesmConstant.DPpower} } } gamma= BayesmConstant.gamma Prioralpha$alphamin=exp(digamma(Prioralpha$Istarmin)-log(gamma+log(nobs))) Prioralpha$alphamax=exp(digamma(Prioralpha$Istarmax)-log(gamma+log(nobs))) Prioralpha$n=nobs # # check Prior arguments for valdity # if(lambda_hyper$alim[1]<0) {pandterm("alim[1] must be >0")} if(lambda_hyper$nulim[1]<0) {pandterm("nulim[1] must be >0")} if(lambda_hyper$vlim[1]<0) {pandterm("vlim[1] must be >0")} if(Prioralpha$Istarmin <1){pandterm("Prioralpha$Istarmin must be >= 1")} if(Prioralpha$Istarmax <= Prioralpha$Istarmin){pandterm("Prioralpha$Istarmin must be > Prioralpha$Istarmax")} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$maxuniq)) {maxuniq=BayesmConstant.DPmaxuniq} else {maxuniq=Mcmc$maxuniq} if(is.null(Mcmc$SCALE)) {SCALE=BayesmConstant.DPSCALE} else {SCALE=Mcmc$SCALE} if(is.null(Mcmc$gridsize)) {gridsize=BayesmConstant.DPgridsize} else {gridsize=Mcmc$gridsize} } # # print out the problem # cat(" Starting Gibbs Sampler for Density Estimation Using Dirichlet Process Model",fill=TRUE) cat(" ",nobs," observations on ",dimy," dimensional data",fill=TRUE) cat(" ",fill=TRUE) cat(" SCALE=",SCALE,fill=TRUE) cat(" ",fill=TRUE) cat(" Prior Parms: ",fill=TRUE) cat(" G0 ~ N(mubar,Sigma (x) Amu^-1)",fill=TRUE) cat(" mubar = ",0,fill=TRUE) cat(" Sigma ~ IW(nu,nu*v*I)",fill=TRUE) cat(" Amu ~ uniform[",lambda_hyper$alim[1],",",lambda_hyper$alim[2],"]",fill=TRUE) cat(" nu ~ uniform on log grid on [",dimy-1+exp(lambda_hyper$nulim[1]), ",",dimy-1+exp(lambda_hyper$nulim[2]),"]",fill=TRUE) cat(" v ~ uniform[",lambda_hyper$vlim[1],",",lambda_hyper$vlim[2],"]",fill=TRUE) cat(" ",fill=TRUE) cat(" alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power",fill=TRUE) cat(" Istarmin = ",Prioralpha$Istarmin,fill=TRUE) cat(" Istarmax = ",Prioralpha$Istarmax,fill=TRUE) cat(" alphamin = ",Prioralpha$alphamin,fill=TRUE) cat(" alphamax = ",Prioralpha$alphamax,fill=TRUE) cat(" power = ",Prioralpha$power,fill=TRUE) cat(" ",fill=TRUE) cat(" Mcmc Parms: R= ",R," keep= ",keep," nprint= ",nprint," maxuniq= ",maxuniq," gridsize for lambda hyperparms= ",gridsize, fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 1/29/2015 ################################################################### out = rDPGibbs_rcpp_loop(R,keep,nprint, y, lambda_hyper, SCALE, maxuniq, Prioralpha, gridsize, BayesmConstant.A,BayesmConstant.nuInc,BayesmConstant.DPalpha) ################################################################### nmix=list(probdraw=matrix(c(rep(1,nrow(out$inddraw))),ncol=1),zdraw=out$inddraw,compdraw=out$thetaNp1draw) attributes(nmix)$class="bayesm.nmix" attributes(out$alphadraw)$class=c("bayesm.mat","mcmc") attributes(out$Istardraw)$class=c("bayesm.mat","mcmc") attributes(out$adraw)$class=c("bayesm.mat","mcmc") attributes(out$nudraw)$class=c("bayesm.mat","mcmc") attributes(out$vdraw)$class=c("bayesm.mat","mcmc") return(list(alphadraw=out$alphadraw,Istardraw=out$Istardraw,adraw=out$adraw,nudraw=out$nudraw, vdraw=out$vdraw,nmix=nmix)) } bayesm/R/rmnlIndepMetrop_rcpp.R0000644000176000001440000001021512524506073016254 0ustar ripleyusersrmnlIndepMetrop=function(Data,Prior,Mcmc){ # # revision history: # p. rossi 1/05 # 2/9/05 fixed error in Metrop eval # changed to reflect new argument order in llmnl,mnlHess 9/05 # added return for log-like 11/05 # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: # draw from posterior for MNL using Independence Metropolis # # Arguments: # Data - list of p,y,X # p is number of alternatives # X is nobs*p x nvar matrix # y is nobs vector of values from 1 to p # Prior - list of A, betabar # A is nvar x nvar prior preci matrix # betabar is nvar x 1 prior mean # Mcmc # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # nu degrees of freedom parameter for independence # sampling density # # Output: # list of betadraws # # Model: Pr(y=j) = exp(x_j'beta)/sum(exp(x_k'beta) # # Prior: beta ~ N(betabar,A^-1) # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p, y, X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y if(is.null(Data$p)) {pandterm("Requires Data element p")} p=Data$p nvar=ncol(X) nobs=length(y) # # check data for validity # if(length(y) != (nrow(X)/p) ) {pandterm("length(y) ne nrow(X)/p")} if(sum(y %in% (1:p)) < nobs) {pandterm("invalid values in y vector -- must be integers in 1:p")} cat(" table of y values",fill=TRUE) print(table(y)) # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$nu)) {nu=6} else {nu=Mcmc$nu} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Independence Metropolis Sampler for Multinomial Logit Model",fill=TRUE) cat(" ",length(y)," obs with ",p," alternatives",fill=TRUE) cat(" ", fill=TRUE) cat("Table of y Values",fill=TRUE) print(table(y)) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint," nu (df for st candidates) = ",nu,fill=TRUE) cat(" ",fill=TRUE) # # compute required quantities for indep candidates # beta=c(rep(0,nvar)) mle=optim(beta,llmnl,X=X,y=y,method="BFGS",hessian=TRUE,control=list(fnscale=-1)) beta=mle$par betastar=mle$par mhess=mnlHess(beta,y,X) candcov=chol2inv(chol(mhess)) root=chol(candcov) rooti=backsolve(root,diag(nvar)) priorcov=chol2inv(chol(A)) rootp=chol(priorcov) rootpi=backsolve(rootp,diag(nvar)) oldloglike=llmnl(beta,y,X) oldlpost=oldloglike+lndMvn(beta,betabar,rootpi) oldlimp=lndMvst(beta,nu,betastar,rooti) # note: we don't need the determinants as they cancel in # computation of acceptance prob ################################################################### # Wayne Taylor # 08/21/2014 ################################################################### loopout = rmnlIndepMetrop_rcpp_loop(R,keep,nu,betastar,root,y,X,betabar,rootpi,rooti,oldlimp,oldlpost,nprint); ################################################################### attributes(loopout$betadraw)$class=c("bayesm.mat","mcmc") attributes(loopout$betadraw)$mcpar=c(1,R,keep) return(list(betadraw=loopout$betadraw,loglike=loopout$loglike,acceptr=loopout$naccept/R)) } bayesm/R/rbayesBLP_rcpp.R0000644000176000001440000002344612536135165014775 0ustar ripleyusersrbayesBLP=function(Data, Prior, Mcmc){ # # Keunwoo Kim 02/06/2014 # # Purpose: # draw theta_bar and Sigma via hybrid Gibbs sampler (Jiang, Manchanda, and Rossi, 2009) # # Arguments: # Data # X: J*T by H (if IV is used, the last column is endogeneous variable.) # share: vector of length J*T # J: number of alternatives (excluding outside option) # Z: instrumental variables (optional) # # Prior # sigmasqR # theta_hat # A # deltabar # Ad # nu0 # s0_sq # VOmega # # Mcmc # R: number of MCMC draws # H: number of draws for Monte-Carlo integration # # s: scaling parameter of MH increment # cand_cov: var-cov matrix of MH increment # (minaccep: lower bound of target range of acceptance rate) # (maxaccep: upper bound of target range of acceptance rate) # # theta_bar_initial # r_initial # tau_sq_initial # Omega_initial # delta_initial # # tol: convergence tolerance for the contraction mapping # # Output: # a List of tau_sq (or Omega and delta), # theta_bar, r (equivalent to Sigma) draws, Sigma draws, # relative numerical efficiency of r draws, tunned parameters for MH, and # acceptance rate # pandterm=function(message) {stop(message,call.=FALSE)} # # check for data # if(missing(Data)) {pandterm("Requires Data argument -- list of X and share")} if(is.null(Data$X)) {pandterm("Requires Data element X")} else {X=Data$X} if(is.null(Data$share)) {pandterm("Requires Data element share")} else {share=Data$share} if(is.null(Data$J)) {pandterm("Requires Data element J")} else {J=Data$J} if(is.null(Data$Z)) {IV=FALSE; Z=matrix(0); I=1} else {IV=TRUE; I=ncol(Z)} K=ncol(X) if (length(share) != nrow(X)) {pandterm("Mismatch in the number of observations in X and share")} T=length(share)/J # # check for prior # if(missing(Prior)) { c=50 sigmasqRoff=1 sigmasqRdiag=log((1+sqrt(1-4*(2*(c(1:K)-1)*sigmasqRoff-c)))/2)/4 sigmasqR=c(sigmasqRdiag, rep(1, K*(K-1)/2)) A=BayesmConstant.A*diag(K) theta_hat=rep(0,K) nu0=K+1 s0_sq=1 deltabar=rep(0,I) Ad=BayesmConstant.A*diag(I) VOmega=BayesmConstant.BLPVOmega } else { if(is.null(Prior$sigmasqR)) { c=50 sigmasqRoff=1 sigmasqRdiag=log((1+sqrt(1-4*(2*(c(1:K)-1)*sigmasqRoff-c)))/2)/4 sigmasqR=c(sigmasqRdiag, rep(1, K*(K-1)/2)) } else { sigmasqR=Prior$sigmasqR } if(is.null(Prior$A)) {A=BayesmConstant.A*diag(K)} else {A=Prior$A} if(is.null(Prior$theta_hat)) {theta_hat=rep(0,K)} else {theta_hat=Prior$theta_hat} if(is.null(Prior$nu0)) {nu0=K+1} else {nu0=Prior$nu0} if(is.null(Prior$s0_sq)) {s0_sq=1} else {s0_sq=Prior$s0_sq} if(is.null(Prior$deltabar)) {deltabar=rep(0,I)} else {deltabar=Prior$deltabar} if(is.null(Prior$Ad)) {Ad=BayesmConstant.A*diag(I)} else {Ad=Prior$Ad} if(is.null(Prior$VOmega)) {VOmega=BayesmConstant.BLPVOmega} else {VOmega=Prior$VOmega} } if(length(sigmasqR) != K*(K+1)/2) pandterm("sigmasqR is of incorrect dimension") if(sum(dim(A)==c(K,K)) != 2) pandterm("A is of incorrect dimension") if(length(theta_hat) != K) pandterm("theta_hat is of incorrect dimension") if((length(nu0) != 1) | (nu0 <=0)) pandterm("nu0 should be a positive number") if((length(s0_sq) != 1) | (s0_sq <=0)) pandterm("s0_sq should be a positive number") if(length(deltabar) != I) pandterm("deltabar is of incorrect dimension") if(sum(dim(Ad)==c(I,I)) != 2) pandterm("Ad is of incorrect dimension") if(sum(dim(VOmega)==c(2,2)) != 2) pandterm("VOmega is of incorrect dimension") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R and H") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$H)) {pandterm("Requires element H of Mcmc")} else {H=Mcmc$H} if(is.null(Mcmc$initial_theta_bar)) {initial_theta_bar=rep(0,K)} else {initial_theta_bar=Mcmc$initial_theta_bar} if(is.null(Mcmc$initial_r)) {initial_r=rep(0,K*(K+1)/2)} else {initial_r=Mcmc$initial_r} if(is.null(Mcmc$initial_tau_sq)) {initial_tau_sq=0.1} else {initial_tau_sq=Mcmc$initial_tau_sq} if(is.null(Mcmc$initial_Omega)) {initial_Omega=diag(2)} else {initial_Omega=Mcmc$initial_Omega} if(is.null(Mcmc$initial_delta)) {initial_delta=rep(0,I)} else {initial_delta=Mcmc$initial_tau_sq} if(is.null(Mcmc$keep)) {keep=1} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(is.null(Mcmc$s)+is.null(Mcmc$cand_cov)==0){ s=Mcmc$s cand_cov=Mcmc$cand_cov tuning_auto=FALSE } if(is.null(Mcmc$s)+is.null(Mcmc$cand_cov)==1) pandterm("If you want to control tuning parameters, write both parameters.") if(is.null(Mcmc$s)+is.null(Mcmc$cand_cov)==2){ s=BayesmConstant.RRScaling/sqrt(K*(K+1)/2) cand_cov=diag(c(rep(0.1,K),rep(1,K*(K-1)/2))) tuning_auto=TRUE } if(is.null(Mcmc$tol)) {tol=BayesmConstant.BLPtol} else {tol=Mcmc$tol} minaccep=0.3 maxaccep=0.5 if(length(initial_theta_bar)!=K) pandterm("initial_theta_bar is of incorrect dimension") if(length(initial_r)!=(K*(K+1)/2)) pandterm("initial_r is of incorrect dimension") if(initial_tau_sq<0) pandterm("initial_tau_sq should be positive") if(sum(dim(initial_Omega)==c(2,2))!=2) pandterm("initial_Omega is of incorrect dimension") if(length(initial_delta)!=I) pandterm("initial_delta is of incorrect dimension") if(nprint<0) { pandterm('nprint must be >=0') } # # print out problem # cat(" ",fill=TRUE) cat("Data Dimensions:",fill=TRUE) cat(" ",T," market(time); ",J+1," alternatives (including outside option); ",fill=TRUE) cat(" ",fill=TRUE) if (IV==TRUE){ cat(" ",I," instrumental variable(s) ",fill=TRUE) cat(" ",fill=TRUE) } cat("Prior Parameters:",fill=TRUE) cat(" thetahat",fill=TRUE) print(theta_hat) cat(" A",fill=TRUE) print(A) cat(" sigmasqR",fill=TRUE) print(sigmasqR) cat(" nu0",fill=TRUE) print(nu0) if (IV==TRUE){ cat(" VOmega",fill=TRUE) print(VOmega) cat(" deltabar",fill=TRUE) print(deltabar) cat(" Ad",fill=TRUE) print(Ad) } if (IV==FALSE){ cat(" s0_sq",fill=TRUE) print(s0_sq) } cat(" ",fill=TRUE) cat("MCMC Parmameters: ",fill=TRUE) cat(" ",R," reps; keeping every ",keep,"th draw; printing every ",nprint,"th draw",fill=TRUE) cat(" ",H," draws for Monte-Carlo integration",fill=TRUE) cat(" ",fill=TRUE) cat("Contraction Mapping Tolerance: ",fill=TRUE) cat(" until max(abs((mu1-mu0)/mu0)) <",tol,fill=TRUE) cat(" ",fill=TRUE) if (tuning_auto){ cat(" automatically tuning parameters of RW M-H increment",fill=TRUE) cat(" ",fill=TRUE) cat(" target acceptance rate is between ",minaccep*100,"% and ",maxaccep*100,"%",fill=TRUE) cat(" ",fill=TRUE) } else{ cat(" scaling parameter of RW M-H increment is given as",fill=TRUE) print(s) cat(" ",fill=TRUE) cat(" var-cov matrix of RW M-H increment is given as",fill=TRUE) print(cand_cov) cat(" ",fill=TRUE) } # draw for MC integration draw <- matrix(rnorm(K*H), K, H) # # tuning RW Metropolis-Hastings # # if auto-tuning complete1 <- 0 initial_theta_bar2 <- initial_theta_bar initial_r2 <- initial_r initial_tau_sq2 <- initial_tau_sq initial_Omega2 <- initial_Omega initial_delta2 <- initial_delta rdraws <- NULL if (tuning_auto){ cat("Tuning RW Metropolis-Hastings Increment...",fill=TRUE) cat(" ",fill=TRUE) cat("-If acceptance rate < ",minaccep*100,"% => s/5",fill=TRUE) cat("-If acceptance rate > ",maxaccep*100,"% => s*3",fill=TRUE) cat("-If acceptance rate is ",minaccep*100,"~",maxaccep*100,"% => complete tuning",fill=TRUE) cat(" ",fill=TRUE) while (complete1==0){ cat(" try s=",s,fill=TRUE) out1 <- bayesBLP_rcpp_loop(IV, X, Z, share, J, T, draw, 500, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, s^2, cand_cov, initial_theta_bar2, initial_r2, initial_tau_sq2, initial_Omega2, initial_delta2, tol, 1, 0) initial_theta_bar2 <- as.vector(out1$thetabardraw[,500]) initial_r2 <- as.vector(out1$rdraw[,500]) initial_tau_sq2 <- out1$tausqdraw[500] if (IV==TRUE) {initial_Omega2 <- matrix(out1$Omegadraw[,500],2,2)} if (IV==TRUE) {initial_delta2 <- as.vector(out1$deltadraw[,500])} cat(" acceptance rate is ",out1$acceptrate,fill=TRUE) if (out1$acceptrate>0.20 & out1$acceptrate<0.80){ rdraws <- cbind(rdraws, out1$rdraw) cat(" (r draws stored)",fill=TRUE) } if (out1$acceptratemaxaccep){ s <- s*3 }else{ complete1 <- 1 cat(" ",fill=TRUE) cat(" (tuning completed.)",fill=TRUE) } } # scaling tunned var-cov matrix from r draws scale_opt <- s*sqrt(diag(cand_cov)) Omega <- cov(t(rdraws)) scale_Omega <- sqrt(diag(Omega)) corr_opt <- Omega / (scale_Omega%*%t(scale_Omega)) s <- 1 cand_cov <- corr_opt * (scale_opt%*%t(scale_opt)) cat(" ",fill=TRUE) cat("Tuning Completed:",fill=TRUE) cat(" s=",s,fill=TRUE) cat(" var-cov=",fill=TRUE) print(cand_cov) cat(" ",fill=TRUE) } # # main run # cat("Starting Random Walk Metropolis-Hastings Sampler for BLP",fill=TRUE) out <- bayesBLP_rcpp_loop(IV, X, Z, share, J, T, draw, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, s^2, cand_cov, initial_theta_bar, initial_r, initial_tau_sq, initial_Omega, initial_delta, tol, keep, nprint) out$s <- s out$cand_cov <- cand_cov attributes(out$tausqdraw)$class=c("bayesm.mat","mcmc") attributes(out$tausqdraw)$mcpar=c(1,R,keep) attributes(out$thetabardraw)$class=c("bayesm.mat","mcmc") attributes(out$thetabardraw)$mcpar=c(1,R,keep) attributes(out$rdraw)$class=c("bayesm.mat","mcmc") attributes(out$rdraw)$mcpar=c(1,R,keep) attributes(out$Sigmadraw)$class=c("bayesm.mat","mcmc") attributes(out$Sigmadraw)$mcpar=c(1,R,keep) attributes(out$Omegadraw)$class=c("bayesm.mat","mcmc") attributes(out$Omegadraw)$mcpar=c(1,R,keep) attributes(out$deltadraw)$class=c("bayesm.mat","mcmc") attributes(out$deltadraw)$mcpar=c(1,R,keep) return(out) } bayesm/R/numEff.R0000755000176000001440000000114110240734574013334 0ustar ripleyusersnumEff= function(x,m=as.integer(min(length(x),(100/sqrt(5000))*sqrt(length(x))))) { # # P. Rossi # revision history: 3/27/05 # # purpose: # compute N-W std error and relative numerical efficiency # # Arguments: # x is vector of draws # m is number of lags to truncate acf # def is such that m=100 if length(x)= 5000 and grows with sqrt(length) # # Output: # list with numerical std error and variance multiple (f) # wgt=as.vector(seq(m,1,-1))/(m+1) z=acf(x,lag.max=m,plot=FALSE) f=1+2*wgt%*%as.vector(z$acf[-1]) stderr=sqrt(var(x)*f/length(x)) list(stderr=stderr,f=f,m=m) } bayesm/R/eMixMargDen.R0000755000176000001440000000123410224401101014232 0ustar ripleyuserseMixMargDen= function(grid,probdraw,compdraw) { # # Revision History: # R. McCulloch 11/04 # # purpose: plot the marginal density of a normal mixture averaged over MCMC draws # # arguments: # grid -- array of grid points, grid[,i] are ordinates for ith component # probdraw -- ith row is ith draw of probabilities of mixture comp # compdraw -- list of lists of draws of mixture comp moments (each sublist is from mixgibbs) # # output: # array of same dim as grid with density values # # den=matrix(0,nrow(grid),ncol(grid)) for(i in 1:length(compdraw)) den=den+mixDen(grid,probdraw[i,],compdraw[[i]]) return(den/length(compdraw)) } bayesm/R/mixDen.R0000755000176000001440000000352510554234436013350 0ustar ripleyusersmixDen= function(x,pvec,comps) { # Revision History: # R. McCulloch 11/04 # P. Rossi 3/05 -- put in backsolve # P. Rossi 1/06 -- put in crossprod # # purpose: compute marginal densities for multivariate mixture of normals (given by p and comps) at x # # arguments: # x: ith columns gives evaluations for density of ith variable # pvec: prior probabilities of normal components # comps: list, each member is a list comp with ith normal component ~ N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # Output: # matrix with same shape as input, x, ith column gives margial density of ith variable # # --------------------------------------------------------------------------------------------- # define function needed # ums=function(comps) { # purpose: obtain marginal means and standard deviations from list of normal components # arguments: # comps: list, each member is a list comp with ith normal component ~N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # returns: # a list with [[1]]=$mu a matrix whose ith row is the means for ith component # [[2]]=$sigma a matrix whose ith row is the standard deviations for the ith component # nc = length(comps) dim = length(comps[[1]][[1]]) mu = matrix(0.0,nc,dim) sigma = matrix(0.0,nc,dim) for(i in 1:nc) { mu[i,] = comps[[i]][[1]] # root = solve(comps[[i]][[2]]) root= backsolve(comps[[i]][[2]],diag(rep(1,dim))) sigma[i,] = sqrt(diag(crossprod(root))) } return(list(mu=mu,sigma=sigma)) } # ---------------------------------------------------------------------------------------------- nc = length(comps) mars = ums(comps) den = matrix(0.0,nrow(x),ncol(x)) for(i in 1:ncol(x)) { for(j in 1:nc) den[,i] = den[,i] + dnorm(x[,i],mean = mars$mu[j,i],sd=mars$sigma[j,i])*pvec[j] } return(den) } bayesm/R/fsh.R0000755000176000001440000000031510225316753012674 0ustar ripleyusersfsh=function() { # # P. Rossi # revision history: 3/27/05 # # Purpose: # function to flush console (needed only under windows) # if (Sys.info()[1] == "Windows") flush.console() return() } bayesm/R/rbprobitgibbs_rcpp.r0000644000176000001440000000631712524506054016036 0ustar ripleyusersrbprobitGibbs= function(Data,Prior,Mcmc) { # # revision history: # p. rossi 1/05 # 3/07 added validity check of values of y and classes # 3/07 fixed error with betabar supplied # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: # draw from posterior for binary probit using Gibbs Sampler # # Arguments: # Data - list of X,y # X is nobs x nvar, y is nobs vector of 0,1 # Prior - list of A, betabar # A is nvar x nvar prior preci matrix # betabar is nvar x 1 prior mean # Mcmc # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of betadraws # # Model: y = 1 if w=Xbeta + e > 0 e ~N(0,1) # # Prior: beta ~ N(betabar,A^-1) # # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y nvar=ncol(X) nobs=length(y) # # check data for validity # if(length(y) != nrow(X) ) {pandterm("y and X not of same row dim")} if(sum(unique(y) %in% c(0:1)) < length(unique(y))) {pandterm("Invalid y, must be 0,1")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Binary Probit Model",fill=TRUE) cat(" with ",length(y)," observations",fill=TRUE) cat("Table of y Values",fill=TRUE) print(table(y)) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) beta=c(rep(0,nvar)) sigma=c(rep(1,nrow(X))) root=chol(chol2inv(chol((crossprod(X,X)+A)))) Abetabar=crossprod(A,betabar) a=ifelse(y == 0,-100, 0) b=ifelse(y == 0, 0, 100) ################################################################### # Keunwoo Kim # 08/05/2014 ################################################################### draws=rbprobitGibbs_rcpp_loop(y,X,Abetabar,root,beta,sigma,a,b,R,keep,nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/summary.bayesm.mat.R0000755000176000001440000000356011754537723015667 0ustar ripleyuserssummary.bayesm.mat=function(object,names,burnin=trunc(.1*nrow(X)),tvalues,QUANTILES=TRUE,TRAILER=TRUE,...){ # # S3 method to compute and print posterior summaries for a matrix of draws # P. Rossi 2/07 # X=object if(mode(X) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(mode(X) !="numeric") stop("Requires numeric argument \n") if(is.null(attributes(X)$dim)) X=as.matrix(X) nx=ncol(X) if(missing(names)) names=as.character(1:nx) if(nrow(X) < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} X=X[(burnin+1):nrow(X),,drop=FALSE] mat=matrix(apply(X,2,mean),nrow=1) mat=rbind(mat,sqrt(matrix(apply(X,2,var),nrow=1))) num_se=double(nx); rel_eff=double(nx); eff_s_size=double(nx) for(i in 1:nx) {out=numEff(X[,i]) if(is.nan(out$stderr)) {num_se[i]=-9999; rel_eff[i]=-9999; eff_s_size[i]=-9999} else {num_se[i]=out$stderr; rel_eff[i]=out$f; eff_s_size[i]=nrow(X)/ceiling(out$f)} } mat=rbind(mat,num_se,rel_eff,eff_s_size) colnames(mat)=names rownames(mat)[1]="mean" rownames(mat)[2]="std dev" rownames(mat)[3]="num se" rownames(mat)[4]="rel eff" rownames(mat)[5]="sam size" if(!missing(tvalues)) {if(mode(tvalues)!="numeric") stop("true values arguments must be numeric \n") if(length(tvalues) != nx) stop("true values argument is wrong length \n") mat=rbind(tvalues,mat) } cat("Summary of Posterior Marginal Distributions ") cat("\nMoments \n") print(t(mat),digits=2) if(QUANTILES){ qmat=apply(X,2,quantile,probs=c(.025,.05,.5,.95,.975)) colnames(qmat)=names if(!missing(tvalues)) { qmat=rbind(tvalues,qmat)} cat("\nQuantiles \n") print(t(qmat),digits=2)} if(TRAILER) cat(paste(" based on ",nrow(X)," valid draws (burn-in=",burnin,") \n",sep="")) invisible(t(mat)) } bayesm/R/rsurgibbs_rcpp.r0000644000176000001440000001141512524506103015174 0ustar ripleyusersrsurGibbs= function(Data,Prior,Mcmc) { # # revision history: # P. Rossi 9/05 # 3/07 added classes # 9/14 changed to improve computations by avoiding Kronecker products # W. Taylor 4/15 - added nprint option to MCMC argument # Purpose: # implement Gibbs Sampler for SUR # # Arguments: # Data -- regdata # regdata is a list of lists of data for each regression # regdata[[i]] contains data for regression equation i # regdata[[i]]$y is y, regdata[[i]]$X is X # note: each regression can have differing numbers of X vars # but you must have same no of obs in each equation. # Prior -- list of prior hyperparameters # betabar,A prior mean, prior precision # nu, V prior on Sigma # Mcmc -- list of MCMC parms # R number of draws # keep -- thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of betadraw,Sigmadraw # # Model: # y_i = X_ibeta + e_i # y is nobs x 1 # X is nobs x k_i # beta is k_i x 1 vector of coefficients # i=1,nreg total regressions # # (e_1,k,...,e_nreg,k) ~ N(0,Sigma) k=1,...,nobs # # we can also write as stacked regression # y = Xbeta+e # y is nobs*nreg x 1,X is nobs*nreg x (sum(k_i)) # routine draws beta -- the stacked vector of all coefficients # # Priors: beta ~ N(betabar,A^-1) # Sigma ~ IW(nu,V) # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata")} if(is.null(Data$regdata)) {pandterm("Requires Data element regdata")} regdata=Data$regdata # # check regdata for validity # nreg=length(regdata) nobs=length(regdata[[1]]$y) nvar=0 indreg=double(nreg+1) y=NULL for (reg in 1:nreg) { if(length(regdata[[reg]]$y) != nobs || nrow(regdata[[reg]]$X) != nobs) {pandterm(paste("incorrect dimensions for regression",reg))} else {indreg[reg]=nvar+1 nvar=nvar+ncol(regdata[[reg]]$X); y=c(y,regdata[[reg]]$y)} } indreg[nreg+1]=nvar+1 # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar); nu=nreg+BayesmConstant.nuInc; V=nu*diag(nreg)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=nreg+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(nreg)} else {ssq=Prior$V} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for SUR Regression Model",fill=TRUE) cat(" with ",nreg," regressions",fill=TRUE) cat(" and ",nobs," observations for each regression",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu = ",nu,fill=TRUE) cat("V = ",fill=TRUE) print(V) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) # # set initial value of Sigma # E=matrix(double(nobs*nreg),ncol=nreg) for (reg in 1:nreg) { E[,reg]=lm(y~.-1,data=data.frame(y=regdata[[reg]]$y,regdata[[reg]]$X))$residuals } Sigma=(crossprod(E)+diag(.01,nreg))/nobs Sigmainv=chol2inv(chol(Sigma)) # # precompute various moments and indices into moment matrix and Abetabar nk=integer(nreg) Xstar=NULL Y=NULL for(i in 1:nreg){ nk[i]=ncol(regdata[[i]]$X) Xstar=cbind(Xstar,regdata[[i]]$X) Y=cbind(Y,regdata[[i]]$y) } cumnk=cumsum(nk) XspXs=crossprod(Xstar) Abetabar=A%*%betabar ################################################################### # Keunwoo Kim # 09/19/2014 ################################################################### draws=rsurGibbs_rcpp_loop(regdata,indreg,cumnk,nk,XspXs,Sigmainv,A,Abetabar,nu,V,nvar,E,Y,R,keep,nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$Sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Sigmadraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/rivGibbs_rcpp.R0000755000176000001440000001176012524506071014713 0ustar ripleyusersrivGibbs=function(Data,Prior,Mcmc) { # # revision history: # R. McCulloch original version 2/05 # p. rossi 3/05 # p. rossi 1/06 -- fixed error in nins # p. rossi 1/06 -- fixed def Prior settings for nu,V # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # # purpose: # draw from posterior for linear I.V. model # # Arguments: # Data -- list of z,w,x,y # y is vector of obs on lhs var in structural equation # x is "endogenous" var in structural eqn # w is matrix of obs on "exogenous" vars in the structural eqn # z is matrix of obs on instruments # Prior -- list of md,Ad,mbg,Abg,nu,V # md is prior mean of delta # Ad is prior prec # mbg is prior mean vector for beta,gamma # Abg is prior prec of same # nu,V parms for IW on Sigma # # Mcmc -- list of R,keep # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of draws of delta,beta,gamma and Sigma # # Model: # # x=z'delta + e1 # y=beta*x + w'gamma + e2 # e1,e2 ~ N(0,Sigma) # # Priors # delta ~ N(md,Ad^-1) # vec(beta,gamma) ~ N(mbg,Abg^-1) # Sigma ~ IW(nu,V) # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of z,w,x,y")} if(is.null(Data$z)) {pandterm("Requires Data element z")} z=Data$z if(is.null(Data$w)) {pandterm("Requires Data element w")} w=Data$w if(is.null(Data$x)) {pandterm("Requires Data element x")} x=Data$x if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y # # check data for validity # if(!is.vector(x)) {pandterm("x must be a vector")} if(!is.vector(y)) {pandterm("y must be a vector")} n=length(y) if(!is.matrix(w)) {pandterm("w is not a matrix")} if(!is.matrix(z)) {pandterm("z is not a matrix")} dimd=ncol(z) dimg=ncol(w) if(n != length(x) ) {pandterm("length(y) ne length(x)")} if(n != nrow(w) ) {pandterm("length(y) ne nrow(w)")} if(n != nrow(z) ) {pandterm("length(y) ne nrow(z)")} # # check for Prior # if(missing(Prior)) { md=c(rep(0,dimd));Ad=BayesmConstant.A*diag(dimd); mbg=c(rep(0,(1+dimg))); Abg=BayesmConstant.A*diag((1+dimg)); nu=3; V=diag(2)} else { if(is.null(Prior$md)) {md=c(rep(0,dimd))} else {md=Prior$md} if(is.null(Prior$Ad)) {Ad=BayesmConstant.A*diag(dimd)} else {Ad=Prior$Ad} if(is.null(Prior$mbg)) {mbg=c(rep(0,(1+dimg)))} else {mbg=Prior$mbg} if(is.null(Prior$Abg)) {Abg=BayesmConstant.A*diag((1+dimg))} else {Abg=Prior$Abg} if(is.null(Prior$nu)) {nu=3} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(2)} else {V=Prior$V} } # # check dimensions of Priors # if(ncol(Ad) != nrow(Ad) || ncol(Ad) != dimd || nrow(Ad) != dimd) {pandterm(paste("bad dimensions for Ad",dim(Ad)))} if(length(md) != dimd) {pandterm(paste("md wrong length, length= ",length(md)))} if(ncol(Abg) != nrow(Abg) || ncol(Abg) != (1+dimg) || nrow(Abg) != (1+dimg)) {pandterm(paste("bad dimensions for Abg",dim(Abg)))} if(length(mbg) != (1+dimg)) {pandterm(paste("mbg wrong length, length= ",length(mbg)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out model # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for Linear IV Model",fill=TRUE) cat(" ",fill=TRUE) cat(" nobs= ",n,"; ",ncol(z)," instruments; ",ncol(w)," included exog vars",fill=TRUE) cat(" Note: the numbers above include intercepts if in z or w",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("mean of delta ",fill=TRUE) print(md) cat("Adelta",fill=TRUE) print(Ad) cat("mean of beta/gamma",fill=TRUE) print(mbg) cat("Abeta/gamma",fill=TRUE) print(Abg) cat("Sigma Prior Parms",fill=TRUE) cat("nu= ",nu," V=",fill=TRUE) print(V) cat(" ",fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Keunwoo Kim # 09/03/2014 ################################################################### draws=rivGibbs_rcpp_loop(y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint) ################################################################### attributes(draws$deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$deltadraw)$mcpar=c(1,R,keep) attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$gammadraw)$class=c("bayesm.mat","mcmc") attributes(draws$gammadraw)$mcpar=c(1,R,keep) attributes(draws$Sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Sigmadraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/rmnpgibbs_rcpp.r0000644000176000001440000001103312524506074015160 0ustar ripleyusersrmnpGibbs=function(Data,Prior,Mcmc) { # # Revision History: # modified by rossi 12/18/04 to include error checking # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: Gibbs MNP model with full covariance matrix # # Arguments: # Data contains # p the number of choice alternatives # y -- a vector of length n with choices (takes on values from 1, .., p) # X -- n(p-1) x k matrix of covariates (including intercepts) # note: X is the differenced matrix unlike MNL X=stack(X_1,..,X_n) # each X_i is (p-1) x nvar # # Prior contains a list of (betabar, A, nu, V) # if elements of prior do not exist, defaults are used # # Mcmc is a list of (beta0,sigma0,R,keep) # beta0,sigma0 are intial values, if not supplied defaults are used # R is number of draws # keep is thinning parm, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # Output: a list of every keepth betadraw and sigmsdraw # # model: # w_i = X_ibeta + e e~N(0,Sigma) note w_i,e are (p-1) x 1 # y_i = j if w_ij > w_i-j j=1,...,p-1 # y_i = p if all w_i < 0 # # priors: # beta ~ N(betabar,A^-1) # Sigma ~ IW(nu,V) # # Check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p, y, X")} if(is.null(Data$p)) {pandterm("Requires Data element p -- number of alternatives")} p=Data$p if(is.null(Data$y)) {pandterm("Requires Data element y -- number of alternatives")} y=Data$y if(is.null(Data$X)) {pandterm("Requires Data element X -- matrix of covariates")} X=Data$X # # check data for validity # levely=as.numeric(levels(as.factor(y))) if(length(levely) != p) {pandterm(paste("y takes on ",length(levely), " values -- must be ",p))} bady=FALSE for (i in 1:p) { if(levely[i] != i) bady=TRUE } cat("Table of y values",fill=TRUE) print(table(y)) if (bady) {pandterm("Invalid y")} n=length(y) k=ncol(X) pm1=p-1 if(nrow(X)/n != pm1) {pandterm(paste("X has ",nrow(X)," rows; must be = (p-1)n"))} # # check for prior elements # if(missing(Prior)) { betabar=rep(0,k) ; A=BayesmConstant.A*diag(k) ; nu=pm1+3; V=nu*diag(pm1)} else {if(is.null(Prior$betabar)) {betabar=rep(0,k)} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(k)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=pm1+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(pm1)} else {V=Prior$V}} if(length(betabar) != k) pandterm("length betabar ne k") if(sum(dim(A)==c(k,k)) != 2) pandterm("A is of incorrect dimension") if(nu < 1) pandterm("invalid nu value") if(sum(dim(V)==c(pm1,pm1)) != 2) pandterm("V is of incorrect dimension") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R must be included") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$beta0)) {beta0=rep(0,k)} else {beta0=Mcmc$beta0} if(is.null(Mcmc$sigma0)) {sigma0=diag(pm1)} else {sigma0=Mcmc$sigma0} if(length(beta0) != k) pandterm("beta0 is not of length k") if(sum(dim(sigma0) == c(pm1,pm1)) != 2) pandterm("sigma0 is of incorrect dimension") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} # # print out problem # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for MNP",fill=TRUE) cat(" ",n," obs; ",p," choice alternatives; ",k," indep vars (including intercepts)",fill=TRUE) cat(" ",fill=TRUE) cat("Table of y values",fill=TRUE) print(table(y)) cat("Prior Parms:",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu",fill=TRUE) print(nu) cat("V",fill=TRUE) print(V) cat(" ",fill=TRUE) cat("MCMC Parms:",fill=TRUE) cat(" ",R," reps; keeping every ",keep,"th draw"," nprint= ",nprint,fill=TRUE) cat("initial beta= ",beta0,fill=TRUE) cat("initial sigma= ",sigma0,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 09/03/2014 ################################################################### loopout = rmnpGibbs_rcpp_loop(R,keep,nprint,pm1,y,X,beta0,sigma0,V,nu,betabar,A); ################################################################### attributes(loopout$betadraw)$class=c("bayesm.mat","mcmc") attributes(loopout$betadraw)$mcpar=c(1,R,keep) attributes(loopout$sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(loopout$sigmadraw)$mcpar=c(1,R,keep) return(loopout) } bayesm/R/BayesmConstants.R0000644000176000001440000000345212536135167015240 0ustar ripleyusers#MCMC BayesmConstant.keep = 1 #keep every keepth draw for MCMC routines BayesmConstant.nprint = 100 #print the remaining time on every nprint'th draw BayesmConstant.RRScaling = 2.38 #Roberts and Rosenthal optimal scaling constant BayesmConstant.w = .1 #fractional likelihood weighting parameter #Priors BayesmConstant.A = .01 #scaling factor for the prior precision matrix BayesmConstant.nuInc = 3 #Increment for nu BayesmConstant.a = 5 #Dirichlet parameter for mixture models BayesmConstant.nu.e = 3 #degrees of freedom parameter for regression error variance prior BayesmConstant.nu = 3 #degrees of freedom parameter for Inverted Wishart prior BayesmConstant.agammaprior = .5 #Gamma prior parameter BayesmConstant.bgammaprior = .1 #Gamma prior parameter #DP BayesmConstant.DPalimdef=c(.01,10) #defines support of 'a' distribution BayesmConstant.DPnulimdef=c(.01,3) #defines support of nu distribution BayesmConstant.DPvlimdef=c(.1,4) #defines support of v distribution BayesmConstant.DPIstarmin = 1 #expected number of components at lower bound of support of alpha BayesmConstant.DPpower = .8 #power parameter for alpha prior BayesmConstant.DPalpha = 1.0 #intitalized value for alpha draws BayesmConstant.DPmaxuniq = 200 #storage constraint on the number of unique components BayesmConstant.DPSCALE = TRUE #should data be scaled by mean,std deviation before posterior draws BayesmConstant.DPgridsize = 20 #number of discrete points for hyperparameter priors #Mathematical Constants BayesmConstant.gamma = .5772156649015328606 #BayesBLP BayesmConstant.BLPVOmega = matrix(c(1,0.5,0.5,1),2,2) #IW prior parameter of correlated shocks in IV bayesBLP BayesmConstant.BLPtol = 1e-6bayesm/R/rcppexports.r0000644000176000001440000001762012535627526014562 0ustar ripleyusers# This file was generated by Rcpp::compileAttributes # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 bayesBLP_rcpp_loop <- function(IV, X, Z, share, J, T, v, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, ssq, cand_cov, theta_bar_initial, r_initial, tau_sq_initial, Omega_initial, delta_initial, tol, keep, nprint) { .Call('bayesm_bayesBLP_rcpp_loop', PACKAGE = 'bayesm', IV, X, Z, share, J, T, v, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, ssq, cand_cov, theta_bar_initial, r_initial, tau_sq_initial, Omega_initial, delta_initial, tol, keep, nprint) } breg <- function(y, X, betabar, A) { .Call('bayesm_breg', PACKAGE = 'bayesm', y, X, betabar, A) } cgetC <- function(e, k) { .Call('bayesm_cgetC', PACKAGE = 'bayesm', e, k) } clusterMix_rcpp_loop <- function(zdraw, cutoff, SILENT, nprint) { .Call('bayesm_clusterMix_rcpp_loop', PACKAGE = 'bayesm', zdraw, cutoff, SILENT, nprint) } ghkvec <- function(L, trunpt, above, r, HALTON = TRUE, pn = as.integer( c(0))) { .Call('bayesm_ghkvec', PACKAGE = 'bayesm', L, trunpt, above, r, HALTON, pn) } llmnl <- function(beta, y, X) { .Call('bayesm_llmnl', PACKAGE = 'bayesm', beta, y, X) } lndIChisq <- function(nu, ssq, X) { .Call('bayesm_lndIChisq', PACKAGE = 'bayesm', nu, ssq, X) } lndIWishart <- function(nu, V, IW) { .Call('bayesm_lndIWishart', PACKAGE = 'bayesm', nu, V, IW) } lndMvn <- function(x, mu, rooti) { .Call('bayesm_lndMvn', PACKAGE = 'bayesm', x, mu, rooti) } lndMvst <- function(x, nu, mu, rooti, NORMC = FALSE) { .Call('bayesm_lndMvst', PACKAGE = 'bayesm', x, nu, mu, rooti, NORMC) } rbprobitGibbs_rcpp_loop <- function(y, X, Abetabar, root, beta, sigma, a, b, R, keep, nprint) { .Call('bayesm_rbprobitGibbs_rcpp_loop', PACKAGE = 'bayesm', y, X, Abetabar, root, beta, sigma, a, b, R, keep, nprint) } rdirichlet <- function(alpha) { .Call('bayesm_rdirichlet', PACKAGE = 'bayesm', alpha) } rDPGibbs_rcpp_loop <- function(R, keep, nprint, y, lambda_hyper, SCALE, maxuniq, PrioralphaList, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) { .Call('bayesm_rDPGibbs_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, y, lambda_hyper, SCALE, maxuniq, PrioralphaList, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) } rhierLinearMixture_rcpp_loop <- function(regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, drawdelta, olddelta, a, oldprob, ind, tau) { .Call('bayesm_rhierLinearMixture_rcpp_loop', PACKAGE = 'bayesm', regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, drawdelta, olddelta, a, oldprob, ind, tau) } rhierLinearModel_rcpp_loop <- function(regdata, Z, Deltabar, A, nu, V, nu_e, ssq, tau, Delta, Vbeta, R, keep, nprint) { .Call('bayesm_rhierLinearModel_rcpp_loop', PACKAGE = 'bayesm', regdata, Z, Deltabar, A, nu, V, nu_e, ssq, tau, Delta, Vbeta, R, keep, nprint) } rhierMnlDP_rcpp_loop <- function(R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) { .Call('bayesm_rhierMnlDP_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) } rhierMnlRwMixture_rcpp_loop <- function(lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind) { .Call('bayesm_rhierMnlRwMixture_rcpp_loop', PACKAGE = 'bayesm', lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind) } rhierNegbinRw_rcpp_loop <- function(regdata, hessdata, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, sbeta, alphacroot, nprint, rootA, alpha, fixalpha) { .Call('bayesm_rhierNegbinRw_rcpp_loop', PACKAGE = 'bayesm', regdata, hessdata, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, sbeta, alphacroot, nprint, rootA, alpha, fixalpha) } rivDP_rcpp_loop <- function(R, keep, nprint, dimd, mbg, Abg, md, Ad, y, isgamma, z, x, w, delta, PrioralphaList, gridsize, SCALE, maxuniq, scalex, scaley, lambda_hyper, BayesmConstantA, BayesmConstantnu) { .Call('bayesm_rivDP_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, dimd, mbg, Abg, md, Ad, y, isgamma, z, x, w, delta, PrioralphaList, gridsize, SCALE, maxuniq, scalex, scaley, lambda_hyper, BayesmConstantA, BayesmConstantnu) } rivGibbs_rcpp_loop <- function(y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint) { .Call('bayesm_rivGibbs_rcpp_loop', PACKAGE = 'bayesm', y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint) } rmixGibbs <- function(y, Bbar, A, nu, V, a, p, z) { .Call('bayesm_rmixGibbs', PACKAGE = 'bayesm', y, Bbar, A, nu, V, a, p, z) } rmixture <- function(n, pvec, comps) { .Call('bayesm_rmixture', PACKAGE = 'bayesm', n, pvec, comps) } rmnlIndepMetrop_rcpp_loop <- function(R, keep, nu, betastar, root, y, X, betabar, rootpi, rooti, oldlimp, oldlpost, nprint) { .Call('bayesm_rmnlIndepMetrop_rcpp_loop', PACKAGE = 'bayesm', R, keep, nu, betastar, root, y, X, betabar, rootpi, rooti, oldlimp, oldlpost, nprint) } rmnpGibbs_rcpp_loop <- function(R, keep, nprint, pm1, y, X, beta0, sigma0, V, nu, betabar, A) { .Call('bayesm_rmnpGibbs_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, pm1, y, X, beta0, sigma0, V, nu, betabar, A) } rmultireg <- function(Y, X, Bbar, A, nu, V) { .Call('bayesm_rmultireg', PACKAGE = 'bayesm', Y, X, Bbar, A, nu, V) } rmvpGibbs_rcpp_loop <- function(R, keep, nprint, p, y, X, beta0, sigma0, V, nu, betabar, A) { .Call('bayesm_rmvpGibbs_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, p, y, X, beta0, sigma0, V, nu, betabar, A) } rmvst <- function(nu, mu, root) { .Call('bayesm_rmvst', PACKAGE = 'bayesm', nu, mu, root) } rnegbinRw_rcpp_loop <- function(y, X, betabar, rootA, a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint) { .Call('bayesm_rnegbinRw_rcpp_loop', PACKAGE = 'bayesm', y, X, betabar, rootA, a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint) } rnmixGibbs_rcpp_loop <- function(y, Mubar, A, nu, V, a, p, z, R, keep, nprint) { .Call('bayesm_rnmixGibbs_rcpp_loop', PACKAGE = 'bayesm', y, Mubar, A, nu, V, a, p, z, R, keep, nprint) } rordprobitGibbs_rcpp_loop <- function(y, X, k, A, betabar, Ad, s, inc_root, dstarbar, betahat, R, keep, nprint) { .Call('bayesm_rordprobitGibbs_rcpp_loop', PACKAGE = 'bayesm', y, X, k, A, betabar, Ad, s, inc_root, dstarbar, betahat, R, keep, nprint) } rscaleUsage_rcpp_loop <- function(k, x, p, n, R, keep, ndghk, nprint, y, mu, Sigma, tau, sigma, Lambda, e, domu, doSigma, dosigma, dotau, doLambda, doe, nu, V, mubar, Am, gsigma, gl11, gl22, gl12, nuL, VL, ge) { .Call('bayesm_rscaleUsage_rcpp_loop', PACKAGE = 'bayesm', k, x, p, n, R, keep, ndghk, nprint, y, mu, Sigma, tau, sigma, Lambda, e, domu, doSigma, dosigma, dotau, doLambda, doe, nu, V, mubar, Am, gsigma, gl11, gl22, gl12, nuL, VL, ge) } rsurGibbs_rcpp_loop <- function(regdata, indreg, cumnk, nk, XspXs, Sigmainv, A, Abetabar, nu, V, nvar, E, Y, R, keep, nprint) { .Call('bayesm_rsurGibbs_rcpp_loop', PACKAGE = 'bayesm', regdata, indreg, cumnk, nk, XspXs, Sigmainv, A, Abetabar, nu, V, nvar, E, Y, R, keep, nprint) } rtrun <- function(mu, sigma, a, b) { .Call('bayesm_rtrun', PACKAGE = 'bayesm', mu, sigma, a, b) } runireg_rcpp_loop <- function(y, X, betabar, A, nu, ssq, R, keep, nprint) { .Call('bayesm_runireg_rcpp_loop', PACKAGE = 'bayesm', y, X, betabar, A, nu, ssq, R, keep, nprint) } runiregGibbs_rcpp_loop <- function(y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint) { .Call('bayesm_runiregGibbs_rcpp_loop', PACKAGE = 'bayesm', y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint) } rwishart <- function(nu, V) { .Call('bayesm_rwishart', PACKAGE = 'bayesm', nu, V) } callroot <- function(c1, c2, tol, iterlim) { .Call('bayesm_callroot', PACKAGE = 'bayesm', c1, c2, tol, iterlim) } bayesm/R/plot.bayesm.hcoef.R0000755000176000001440000000312012533647242015435 0ustar ripleyusersplot.bayesm.hcoef=function(x,names,burnin=trunc(.1*R),...){ # # S3 method to plot arrays of draws of coefs in hier models # 3 dimensional arrays: unit x var x draw # P. Rossi 2/07 # X=x if(mode(X) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(mode(X) !="numeric") stop("Requires numeric argument \n") d=dim(X) if(length(d) !=3) stop("Requires 3-dim array \n") op=par(no.readonly=TRUE) on.exit(par(op)) on.exit(devAskNewPage(FALSE),add=TRUE) nunits=d[1] nvar=d[2] R=d[3] if(missing(names)) {names=as.character(1:nvar)} if(R < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} # # plot posterior distributions of nvar coef for 30 rand units # rsam=sort(sample(c(1:nunits),30)) # randomly sample 30 cross-sectional units par(mfrow=c(1,1)) par(las=3) # horizontal labeling devAskNewPage(TRUE) for(var in 1:nvar){ ext=X[rsam,var,(burnin+1):R]; ext=data.frame(t(ext)) colnames(ext)=as.character(rsam) out=boxplot(ext,plot=FALSE,...) out$stats=apply(ext,2,quantile,probs=c(0,.05,.95,1)) bxp(out,xlab="Cross-sectional Unit",main=paste("Coefficients on Var ",names[var],sep=""),boxfill="magenta",...) } # # plot posterior means for each var # par(las=1) pmeans=matrix(0,nrow=nunits,ncol=nvar) for(i in 1:nunits) pmeans[i,]=apply(X[i,,(burnin+1):R],1,mean) attributes(pmeans)$class="bayesm.mat" for(var in 1:nvar) names[var]=paste("Posterior Means of Coef ",names[var],sep="") plot(pmeans,names,TRACEPLOT=FALSE,INT=FALSE,DEN=FALSE,CHECK_NDRAWS=FALSE,...) invisible() } bayesm/R/plot.bayesm.mat.R0000755000176000001440000000430412533647040015133 0ustar ripleyusersplot.bayesm.mat=function(x,names,burnin=trunc(.1*nrow(X)),tvalues,TRACEPLOT=TRUE,DEN=TRUE,INT=TRUE, CHECK_NDRAWS=TRUE,...){ # # S3 method to print matrices of draws the object X is of class "bayesm.mat" # # P. Rossi 2/07 # X=x if(mode(X) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(mode(X) !="numeric") stop("Requires numeric argument \n") op=par(no.readonly=TRUE) on.exit(par(op)) on.exit(devAskNewPage(FALSE),add=TRUE) if(is.null(attributes(X)$dim)) X=as.matrix(X) nx=ncol(X) if(nrow(X) < 100 & CHECK_NDRAWS) {cat("fewer than 100 draws submitted \n"); return(invisible())} if(!missing(tvalues)){ if(mode(tvalues) !="numeric") {stop("tvalues must be a numeric vector \n")} else {if(length(tvalues)!=nx) stop("tvalues are wrong length \n")} } if(nx==1) par(mfrow=c(1,1)) if(nx==2) par(mfrow=c(2,1)) if(nx==3) par(mfrow=c(3,1)) if(nx==4) par(mfrow=c(2,2)) if(nx>=5) par(mfrow=c(3,2)) if(missing(names)) {names=as.character(1:nx)} if (DEN) ylabtxt="density" else ylabtxt="freq" devAskNewPage(TRUE) for(index in 1:nx){ hist(X[(burnin+1):nrow(X),index],xlab="",ylab=ylabtxt,main=names[index],freq=!DEN,col="magenta",...) if(!missing(tvalues)) abline(v=tvalues[index],lwd=2,col="blue") if(INT){ quants=quantile(X[(burnin+1):nrow(X),index],prob=c(.025,.975)) mean=mean(X[(burnin+1):nrow(X),index]) semean=numEff(X[(burnin+1):nrow(X),index])$stderr text(quants[1],0,"|",cex=3.0,col="green") text(quants[2],0,"|",cex=3.0,col="green") text(mean,0,"|",cex=3.0,col="red") text(mean-2*semean,0,"|",cex=2,col="yellow") text(mean+2*semean,0,"|",cex=2,col="yellow") } } if(TRACEPLOT){ if(nx==1) par(mfrow=c(1,2)) if(nx==2) par(mfrow=c(2,2)) if(nx>=3) par(mfrow=c(3,2)) for(index in 1:nx){ plot(as.vector(X[,index]),xlab="",ylab="",main=names[index],type="l",col="red") if(!missing(tvalues)) abline(h=tvalues[index],lwd=2,col="blue") if(var(X[,index])>1.0e-20) {acf(as.vector(X[,index]),xlab="",ylab="",main="")} else {plot.default(X[,index],xlab="",ylab="",type="n",main="No ACF Produced")} } } invisible() } bayesm/R/rhierLinearModel_rcpp.R0000644000176000001440000001504412524560171016366 0ustar ripleyusersrhierLinearModel= function(Data,Prior,Mcmc) { # # Revision History # 1/17/05 P. Rossi # 10/05 fixed error in setting prior if Prior argument is missing # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # Purpose: # run hiearchical regression model # # Arguments: # Data list of regdata,Z # regdata is a list of lists each list with members y, X # e.g. regdata[[i]]=list(y=y,X=X) # X has nvar columns # Z is nreg=length(regdata) x nz # Prior list of prior hyperparameters # Deltabar,A, nu.e,ssq,nu,V # note: ssq is a nreg x 1 vector! # Mcmc # list of Mcmc parameters # R is number of draws # keep is thining parameter -- keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of # betadraw -- nreg x nvar x R/keep array of individual regression betas # taudraw -- R/keep x nreg array of error variances for each regression # Deltadraw -- R/keep x nz x nvar array of Delta draws # Vbetadraw -- R/keep x nvar*nvar array of Vbeta draws # # Model: # nreg regression equations # y_i = X_ibeta_i + epsilon_i # epsilon_i ~ N(0,tau_i) # nvar X vars in each equation # # Priors: # tau_i ~ nu.e*ssq_i/chisq(nu.e) tau_i is the variance of epsilon_i # beta_i ~ N(ZDelta[i,],V_beta) # Note: ZDelta is the matrix Z * Delta; [i,] refers to ith row of this product! # # vec(Delta) | V_beta ~ N(vec(Deltabar),Vbeta (x) A^-1) # V_beta ~ IW(nu,V) or V_beta^-1 ~ W(nu,V^-1) # Delta, Deltabar are nz x nvar # A is nz x nz # Vbeta is nvar x nvar # # NOTE: if you don't have any z vars, set Z=iota (nreg x 1) # # # create needed functions # #------------------------------------------------------------------------------ append=function(l) { l=c(l,list(XpX=crossprod(l$X),Xpy=crossprod(l$X,l$y)))} # getvar=function(l) { v=var(l$y) if(is.na(v)) return(1) if(v>0) return (v) else return (1)} # #------------------------------------------------------------------------------ # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata and Z")} if(is.null(Data$regdata)) {pandterm("Requires Data element regdata")} regdata=Data$regdata nreg=length(regdata) if(is.null(Data$Z)) { cat("Z not specified -- putting in iota",fill=TRUE); fsh() ; Z=matrix(rep(1,nreg),ncol=1)} else {if (nrow(Data$Z) != nreg) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number regressions ",nreg))} else {Z=Data$Z}} nz=ncol(Z) # # check data for validity # dimfun=function(l) {c(length(l$y),dim(l$X))} dims=sapply(regdata,dimfun) dims=t(dims) nvar=quantile(dims[,3],prob=.5) for (i in 1:nreg) { if(dims[i,1] != dims[i,2] || dims[i,3] !=nvar) {pandterm(paste("Bad Data dimensions for unit ",i," dims(y,X) =",dims[i,]))} } # # check for Prior # if(missing(Prior)) { Deltabar=matrix(rep(0,nz*nvar),ncol=nvar); A=BayesmConstant.A*diag(nz); nu.e=BayesmConstant.nu.e; ssq=sapply(regdata,getvar) ; nu=nvar+BayesmConstant.nuInc ; V= nu*diag(nvar)} else { if(is.null(Prior$Deltabar)) {Deltabar=matrix(rep(0,nz*nvar),ncol=nvar)} else {Deltabar=Prior$Deltabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nz)} else {A=Prior$A} if(is.null(Prior$nu.e)) {nu.e=BayesmConstant.nu.e} else {nu.e=Prior$nu.e} if(is.null(Prior$ssq)) {ssq=sapply(regdata,getvar)} else {ssq=Prior$ssq} if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nz || nrow(A) != nz) {pandterm(paste("bad dimensions for A",dim(A)))} if(nrow(Deltabar) != nz || ncol(Deltabar) != nvar) {pandterm(paste("bad dimensions for Deltabar ",dim(Deltabar)))} if(length(ssq) != nreg) {pandterm(paste("bad length for ssq ",length(ssq)))} if(ncol(V) != nvar || nrow(V) != nvar) {pandterm(paste("bad dimensions for V ",dim(V)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Linear Hierarchical Model",fill=TRUE) cat(" ",nreg," Regressions",fill=TRUE) cat(" ",ncol(Z)," Variables in Z (if 1, then only intercept)",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("Deltabar",fill=TRUE) print(Deltabar) cat("A",fill=TRUE) print(A) cat("nu.e (d.f. parm for regression error variances)= ",nu.e,fill=TRUE) cat("Vbeta ~ IW(nu,V)",fill=TRUE) cat("nu = ",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) # # allocate space for the draws and set initial values of Vbeta and Delta # tau=double(nreg) Delta=matrix(0,nz,nvar) Vbeta=diag(nvar) # # set up fixed parms for the draw of Vbeta,Delta # # note: in the notation of the MVR Y = X B # n x m n x k k x m # "n" = nreg # "m" = nvar # "k" = nz # general model: Beta = Z Delta + U # # Create XpX elements of regdata and initialize tau # regdata=lapply(regdata,append) tau=sapply(regdata,getvar) ################################################################### # Keunwoo Kim # 08/20/2014 ################################################################### draws=rhierLinearModel_rcpp_loop(regdata,Z,Deltabar,A,nu,V,nu.e,ssq,tau,Delta,Vbeta,R,keep,nprint) ################################################################### attributes(draws$taudraw)$class=c("bayesm.mat","mcmc") attributes(draws$taudraw)$mcpar=c(1,R,keep) attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep) attributes(draws$Vbetadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Vbetadraw)$mcpar=c(1,R,keep) attributes(draws$betadraw)$class=c("bayesm.hcoef") return(draws) }bayesm/R/rnmixgibbs_rcpp.r0000644000176000001440000001315612524506077015354 0ustar ripleyusersrnmixGibbs= function(Data,Prior,Mcmc){ # # Revision History: # P. Rossi 3/05 # add check to see if Mubar is a vector 9/05 # fixed bug in saving comps draw comps[[mkeep]]= 9/05 # fixed so that ncomp can be =1; added check that nobs >= 2*ncomp 12/06 # 3/07 added classes # added log-likelihood 9/08 # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: do Gibbs sampling inference for a mixture of multivariate normals # # arguments: # Data is a list of y which is an n x k matrix of data -- each row # is an iid draw from the normal mixture # Prior is a list of (Mubar,A,nu,V,a,ncomp) # ncomp is required # if elements of the prior don't exist, defaults are assumed # Mcmc is a list of R, keep (thinning parameter), and nprint # Output: # list with elements # pdraw -- R/keep x ncomp array of mixture prob draws # zdraw -- R/keep x nobs array of indicators of mixture comp identity for each obs # compsdraw -- list of R/keep lists of lists of comp parm draws # e.g. compsdraw[[i]] is ith draw -- list of ncomp lists # compsdraw[[i]][[j]] is list of parms for jth normal component # if jcomp=compsdraw[[i]][j]] # ~N(jcomp[[1]],Sigma), Sigma = t(R)%*%R, R^{-1} = jcomp[[2]] # # Model: # y_i ~ N(mu_ind,Sigma_ind) # ind ~ iid multinomial(p) p is a 1x ncomp vector of probs # Priors: # mu_j ~ N(mubar,Sigma (x) A^-1) # mubar=vec(Mubar) # Sigma_j ~ IW(nu,V) # note: this is the natural conjugate prior -- a special case of multivariate # regression # p ~ Dirchlet(a) # # check arguments # # # ----------------------------------------------------------------------------------------- llnmix=function(Y,z,comps){ # # evaluate likelihood for mixture of normals # zu=unique(z) ll=0.0 for(i in 1:length(zu)){ Ysel=Y[z==zu[i],,drop=FALSE] ll=ll+sum(apply(Ysel,1,lndMvn,mu=comps[[zu[i]]]$mu,rooti=comps[[zu[i]]]$rooti)) } return(ll) } # ----------------------------------------------------------------------------------------- if(missing(Data)) {pandterm("Requires Data argument -- list of y")} if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y # # check data for validity # if(!is.matrix(y)) {pandterm("y must be a matrix")} nobs=nrow(y) dimy=ncol(y) # # check for Prior # if(missing(Prior)) {pandterm("requires Prior argument ")} else { if(is.null(Prior$ncomp)) {pandterm("requires number of mix comps -- Prior$ncomp")} else {ncomp=Prior$ncomp} if(is.null(Prior$Mubar)) {Mubar=matrix(rep(0,dimy),nrow=1)} else {Mubar=Prior$Mubar; if(is.vector(Mubar)) {Mubar=matrix(Mubar,nrow=1)}} if(is.null(Prior$A)) {A=matrix(BayesmConstant.A,ncol=1)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=dimy+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(dimy)} else {V=Prior$V} if(is.null(Prior$a)) {a=c(rep(BayesmConstant.a,ncomp))} else {a=Prior$a} } # # check for adequate no. of observations # if(nobs<2*ncomp) {pandterm("too few obs, nobs should be >= 2*ncomp")} # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != 1) {pandterm(paste("bad dimensions for A",dim(A)))} if(!is.matrix(Mubar)) {pandterm("Mubar must be a matrix")} if(nrow(Mubar) != 1 || ncol(Mubar) != dimy) {pandterm(paste("bad dimensions for Mubar",dim(Mubar)))} if(ncol(V) != nrow(V) || ncol(V) != dimy) {pandterm(paste("bad dimensions for V",dim(V)))} if(length(a) != ncomp) {pandterm(paste("a wrong length, length= ",length(a)))} bada=FALSE for(i in 1:ncomp){if(a[i] < 0) bada=TRUE} if(bada) pandterm("invalid values in a vector") # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$LogLike)) {LogLike=FALSE} else {LogLike=Mcmc$LogLike} } # # print out the problem # cat(" Starting Gibbs Sampler for Mixture of Normals",fill=TRUE) cat(" ",nobs," observations on ",dimy," dimensional data",fill=TRUE) cat(" using ",ncomp," mixture components",fill=TRUE) cat(" ",fill=TRUE) cat(" Prior Parms: ",fill=TRUE) cat(" mu_j ~ N(mubar,Sigma (x) A^-1)",fill=TRUE) cat(" mubar = ",fill=TRUE) print(Mubar) cat(" precision parm for prior variance of mu vectors (A)= ",A,fill=TRUE) cat(" Sigma_j ~ IW(nu,V) nu= ",nu,fill=TRUE) cat(" V =",fill=TRUE) print(V) cat(" Dirichlet parameters ",fill=TRUE) print(a) cat(" ",fill=TRUE) cat(" Mcmc Parms: R= ",R," keep= ",keep," nprint= ",nprint," LogLike= ",LogLike,fill=TRUE) # pdraw=matrix(double(floor(R/keep)*ncomp),ncol=ncomp) # zdraw=matrix(double(floor(R/keep)*nobs),ncol=nobs) # compdraw=list() compsd=list() if(LogLike) ll=double(floor(R/keep)) # # set initial values of z # z=rep(c(1:ncomp),(floor(nobs/ncomp)+1)) z=z[1:nobs] cat(" ",fill=TRUE) cat("starting value for z",fill=TRUE) print(table(z)) cat(" ",fill=TRUE) p=c(rep(1,ncomp))/ncomp # note this is not used fsh() #Wayne Taylor 8/18/14##################################################### nmix = rnmixGibbs_rcpp_loop(y, Mubar, A, nu, V, a, p, z, R, keep, nprint); ########################################################################## attributes(nmix)$class="bayesm.nmix" if(LogLike){ zdraw = nmix$zdraw compdraw = nmix$compdraw ll = lapply(seq_along(compdraw), function(i) llnmix(y, zdraw[i,], compdraw[[i]])) return(list(ll=ll,nmix=nmix)) }else{ return(list(nmix=nmix)) } }bayesm/R/rbiNormGibbs.R0000755000176000001440000000617212524506047014503 0ustar ripleyusersrbiNormGibbs=function(initx=2,inity=-2,rho,burnin=100,R=500) { # # revision history: # P. Rossi 1/05 # # purpose: # illustrate the function of bivariate normal gibbs sampler # # arguments: # initx,inity initial values for draw sequence # rho correlation # burnin draws to be discarded in final paint # R -- number of draws # # output: # opens graph window and paints all moves and normal contours # list containing draw matrix # # model: # theta is bivariate normal with zero means, unit variances and correlation rho # # define needed functions # kernel= function(x,mu,rooti){ # function to evaluate -.5*log of MV NOrmal density kernel with mean mu, var Sigma # and with sigma^-1=rooti%*%t(rooti) # rooti is in the inverse of upper triangular chol root of sigma # note: this is the UL decomp of sigmai not LU! # Sigma=root'root root=inv(rooti) z=as.vector(t(rooti)%*%(x-mu)) (z%*%z) } # # check input arguments # if(missing(rho)) {pandterm("Requires rho argument ")} # # print out settings # cat("Bivariate Normal Gibbs Sampler",fill=TRUE) cat("rho= ",rho,fill=TRUE) cat("initial x,y coordinates= (",initx,",",inity,")",fill=TRUE) cat("burn-in= ",burnin," R= ",R,fill=TRUE) cat(" ",fill=TRUE) cat(" ",fill=TRUE) sd=(1-rho**2)**(.5) sigma=matrix(c(1,rho,rho,1),ncol=2) rooti=backsolve(chol(sigma),diag(2)) mu=c(0,0) x=seq(-3.5,3.5,length=100) y=x z=matrix(double(100*100),ncol=100) for (i in 1:length(x)) { for(j in 1:length(y)) { z[i,j]=kernel(c(x[i],y[j]),mu,rooti) } } prob=c(.1,.3,.5,.7,.9,.99) lev=qchisq(prob,2) par(mfrow=c(1,1)) contour(x,y,z,levels=lev,labels=prob, xlab="theta1",ylab="theta2",drawlabels=TRUE,col="green",labcex=1.3,lwd=2.0) title(paste("Gibbs Sampler with Intermediate Moves: Rho =",rho)) points(initx,inity,pch="B",cex=1.5) oldx=initx oldy=inity continue="y" r=0 draws=matrix(double(R*2),ncol=2) draws[1,]=c(initx,inity) cat(" ") cat("Starting Gibbs Sampler ....",fill=TRUE) cat("(hit enter or y to display moves one-at-a-time)",fill=TRUE) cat("('go' to paint all moves without stopping to prompt)",fill=TRUE) cat(" ",fill=TRUE) while(continue != "n"&& r < R) { if(continue != "go") continue=readline("cont?") newy=sd*rnorm(1) + rho*oldx lines(c(oldx,oldx),c(oldy,newy),col="magenta",lwd=1.5) newx=sd*rnorm(1)+rho*newy lines(c(oldx,newx),c(newy,newy),col="magenta",lwd=1.5) oldy=newy oldx=newx r=r+1 draws[r,]=c(newx,newy) } continue=readline("Show Comparison to iid Sampler?") if(continue != "n" & continue != "No" & continue != "no"){ par(mfrow=c(1,2)) contour(x,y,z,levels=lev, xlab="theta1",ylab="theta2",drawlabels=TRUE,labels=prob,labcex=1.1,col="green",lwd=2.0) title(paste("Gibbs Draws: Rho =",rho)) points(draws[(burnin+1):R,],pch=20,col="magenta",cex=.7) idraws=t(chol(sigma))%*%matrix(rnorm(2*(R-burnin)),nrow=2) idraws=t(idraws) contour(x,y,z,levels=lev, xlab="theta1",ylab="theta2",drawlabels=TRUE,labels=prob,labcex=1.1,col="green",lwd=2.0) title(paste("IID draws: Rho =",rho)) points(idraws,pch=20,col="magenta",cex=.7) } attributes(draws)$class=c("bayesm.mat","mcmc") attributes(draws)$mcpar=c(1,R,1) return(draws) } bayesm/R/summary.bayesm.var.R0000755000176000001440000000272412135054024015655 0ustar ripleyuserssummary.bayesm.var=function(object,names,burnin=trunc(.1*nrow(Vard)),tvalues,QUANTILES=FALSE,...){ # # S3 method to summarize draws of var-cov matrix (stored as a vector) # Vard is R x d**2 array of draws # P. Rossi 2/07 # Vard=object if(mode(Vard) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(!is.matrix(Vard)) stop("Requires matrix argument \n") if(trunc(sqrt(ncol(Vard)))!=sqrt(ncol(Vard))) stop("Argument cannot be draws from a square matrix \n") if(nrow(Vard) < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} d=sqrt(ncol(Vard)) corrd=t(apply(Vard[(burnin+1):nrow(Vard),],1,nmat)) pmeancorr=apply(corrd,2,mean) dim(pmeancorr)=c(d,d) indexdiag=(0:(d-1))*d+1:d var=Vard[(burnin+1):nrow(Vard),indexdiag] sdd=sqrt(var) pmeansd=apply(sdd,2,mean) mat=cbind(pmeansd,pmeancorr) if(missing(names)) names=as.character(1:d) cat("Posterior Means of Std Deviations and Correlation Matrix \n") rownames(mat)=names colnames(mat)=c("Std Dev",names) print(mat,digits=2) cat("\nUpper Triangle of Var-Cov Matrix \n") ind=as.vector(upper.tri(matrix(0,ncol=d,nrow=d),diag=TRUE)) labels=cbind(rep(c(1:d),d),rep(c(1:d),each=d)) labels=labels[ind,] plabels=paste(labels[,1],labels[,2],sep=",") uppertri=as.matrix(Vard[,ind]) attributes(uppertri)$class="bayesm.mat" summary(uppertri,names=plabels,burnin=burnin,tvalues=tvalues,QUANTILES=QUANTILES) invisible() } bayesm/R/rhierLinearMixture_rcpp.r0000644000176000001440000001753612524560151017031 0ustar ripleyusersrhierLinearMixture=function(Data,Prior,Mcmc){ # # revision history: # changed 12/17/04 by rossi to fix bug in drawdelta when there is zero/one unit # in a mixture component # adapted to linear model by Vicky Chen 6/06 # put in classes 3/07 # changed a check 9/08 # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: run hierarchical linear model with mixture of normals # # Arguments: # Data contains a list of (regdata, and possibly Z) # regdata is a list of lists (one list per unit) # regdata[[i]]=list(y,X) # y is a vector of observations # X is a length(y) x nvar matrix of values of # X vars including intercepts # Z is an nreg x nz matrix of values of variables # note: Z should NOT contain an intercept # Prior contains a list of (nu.e,ssq,deltabar,Ad,mubar,Amu,nu,V,ncomp,a) # ncomp is the number of components in normal mixture # if elements of Prior (other than ncomp) do not exist, defaults are used # Mcmc contains a list of (s,c,R,keep,nprint) # # Output: as list containing # taodraw is R/keep x nreg array of error variances for each regression # Deltadraw R/keep x nz*nvar matrix of draws of Delta, first row is initial value # betadraw is nreg x nvar x R/keep array of draws of betas # probdraw is R/keep x ncomp matrix of draws of probs of mixture components # compdraw is a list of list of lists (length R/keep) # compdraw[[rep]] is the repth draw of components for mixtures # # Priors: # tau_i ~ nu.e*ssq_i/chisq(nu.e) tau_i is the variance of epsilon_i # beta_i = delta %*% z[i,] + u_i # u_i ~ N(mu_ind[i],Sigma_ind[i]) # ind[i] ~multinomial(p) # p ~ dirichlet (a) # a: Dirichlet parameters for prior on p # delta is a k x nz array # delta= vec(D) ~ N(deltabar,A_d^-1) # mu_j ~ N(mubar,A_mu^-1(x)Sigma_j) # Sigma_j ~ IW(nu,V^-1) # ncomp is number of components # # MCMC parameters # R is number of draws # keep is thinning parameter, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # check arguments # #-------------------------------------------------------------------------------------------------- # # create functions needed # append=function(l) { l=c(l,list(XpX=crossprod(l$X),Xpy=crossprod(l$X,l$y)))} # getvar=function(l) { v=var(l$y) if(is.na(v)) return(1) if(v>0) return (v) else return (1)} # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata, and (possibly) Z")} if(is.null(Data$regdata)) {pandterm("Requires Data element regdata (list of data for each unit)")} regdata=Data$regdata nreg=length(regdata) drawdelta=TRUE if(is.null(Data$Z)) { cat("Z not specified",fill=TRUE); fsh() ; drawdelta=FALSE} else {if (nrow(Data$Z) != nreg) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number regressions ",nreg))} else {Z=Data$Z}} if(drawdelta) { nz=ncol(Z) colmeans=apply(Z,2,mean) if(sum(colmeans) > .00001) {pandterm(paste("Z does not appear to be de-meaned: colmeans= ",colmeans))} } # # check regdata for validity # dimfun=function(l) {c(length(l$y),dim(l$X))} dims=sapply(regdata,dimfun) dims=t(dims) nvar=quantile(dims[,3],prob=.5) for (i in 1:nreg) { if(dims[i,1] != dims[i,2] || dims[i,3] !=nvar) {pandterm(paste("Bad Data dimensions for unit ",i," dims(y,X) =",dims[i,]))} } # # check on prior # if(missing(Prior)) {pandterm("Requires Prior list argument (at least ncomp)")} if(is.null(Prior$nu.e)) {nu.e=BayesmConstant.nu.e} else {nu.e=Prior$nu.e} if(is.null(Prior$ssq)) {ssq=sapply(regdata,getvar)} else {ssq=Prior$ssq} if(is.null(Prior$ncomp)) {pandterm("Requires Prior element ncomp (num of mixture components)")} else {ncomp=Prior$ncomp} if(is.null(Prior$mubar)) {mubar=matrix(rep(0,nvar),nrow=1)} else { mubar=matrix(Prior$mubar,nrow=1)} if(ncol(mubar) != nvar) {pandterm(paste("mubar must have ncomp cols, ncol(mubar)= ",ncol(mubar)))} if(is.null(Prior$Amu)) {Amu=matrix(BayesmConstant.A,ncol=1)} else {Amu=matrix(Prior$Amu,ncol=1)} if(ncol(Amu) != 1 | nrow(Amu) != 1) {pandterm("Am must be a 1 x 1 array")} if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu} if(nu < 1) {pandterm("invalid nu value")} if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V} if(sum(dim(V)==c(nvar,nvar)) !=2) pandterm("Invalid V in prior") if(is.null(Prior$Ad) & drawdelta) {Ad=BayesmConstant.A*diag(nvar*nz)} else {Ad=Prior$Ad} if(drawdelta) {if(ncol(Ad) != nvar*nz | nrow(Ad) != nvar*nz) {pandterm("Ad must be nvar*nz x nvar*nz")}} if(is.null(Prior$deltabar)& drawdelta) {deltabar=rep(0,nz*nvar)} else {deltabar=Prior$deltabar} if(drawdelta) {if(length(deltabar) != nz*nvar) {pandterm("deltabar must be of length nvar*nz")}} if(is.null(Prior$a)) { a=rep(BayesmConstant.a,ncomp)} else {a=Prior$a} if(length(a) != ncomp) {pandterm("Requires dim(a)= ncomp (no of components)")} bada=FALSE for(i in 1:ncomp) { if(a[i] < 0) bada=TRUE} if(bada) pandterm("invalid values in a vector") # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ",fill=TRUE) cat("Starting MCMC Inference for Hierarchical Linear Model:",fill=TRUE) cat(" Normal Mixture with",ncomp,"components for first stage prior",fill=TRUE) cat(paste(" for ",nreg," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("nu.e =",nu.e,fill=TRUE) cat("nu =",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat("mubar ",fill=TRUE) print(mubar) cat("Amu ", fill=TRUE) print(Amu) cat("a ",fill=TRUE) print(a) if(drawdelta) { cat("deltabar",fill=TRUE) print(deltabar) cat("Ad",fill=TRUE) print(Ad) } cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat("",fill=TRUE) # initialize values # # Create XpX elements of regdata and initialize tau # regdata=lapply(regdata,append) tau=sapply(regdata,getvar) # # set initial values for the indicators # ind is of length(nreg) and indicates which mixture component this obs # belongs to. # ind=NULL ninc=floor(nreg/ncomp) for (i in 1:(ncomp-1)) {ind=c(ind,rep(i,ninc))} if(ncomp != 1) {ind = c(ind,rep(ncomp,nreg-length(ind)))} else {ind=rep(1,nreg)} # #initialize delta # if (drawdelta){ olddelta = rep(0,nz*nvar) } else { #send placeholders to the _loop function if there is no Z matrix olddelta = 0 Z = matrix(0) deltabar = 0 Ad = matrix(0) } # # initialize probs # oldprob=rep(1/ncomp,ncomp) ################################################################### # Wayne Taylor # 09/19/2014 ################################################################### draws = rhierLinearMixture_rcpp_loop(regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu.e, ssq, R, keep, nprint, drawdelta, as.matrix(olddelta), a, oldprob, ind, tau) #################################################################### attributes(draws$taudraw)$class=c("bayesm.mat","mcmc") attributes(draws$taudraw)$mcpar=c(1,R,keep) if(drawdelta){ attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep)} attributes(draws$betadraw)$class=c("bayesm.hcoef") attributes(draws$nmix)$class="bayesm.nmix" return(draws) }bayesm/R/llnhlogit.R0000755000176000001440000000213712524673066014123 0ustar ripleyusersllnhlogit=function(theta,choice,lnprices,Xexpend) { # function to evaluate non-homothetic logit likelihood # choice is a n x 1 vector with indicator of choice (1,...,m) # lnprices is n x m array of log-prices faced # Xexpend is n x d array of variables predicting expenditure # # non-homothetic model specifies ln(psi_i(u))= alpha_i - exp(k_i)u # # structure of theta vector: # alpha (m x 1) # k (m x 1) # gamma (k x 1) expenditure function coefficients # tau scaling of v # m=ncol(lnprices) n=length(choice) d=ncol(Xexpend) alpha=theta[1:m] k=theta[(m+1):(2*m)] gamma=theta[(2*m+1):(2*m+d)] tau=theta[length(theta)] iotam=c(rep(1,m)) c1=as.vector(Xexpend%*%gamma)%x%iotam-as.vector(t(lnprices))+alpha c2=c(rep(exp(k),n)) u=callroot(c1,c2,.0000001,20) v=alpha - u*exp(k)-as.vector(t(lnprices)) vmat=matrix(v,ncol=m,byrow=TRUE) vmat=tau*vmat ind=seq(1,n) vchosen=vmat[cbind(ind,choice)] lnprob=vchosen-log((exp(vmat))%*%iotam) return(sum(lnprob)) } bayesm/R/rmvpgibbs_rcpp.r0000644000176000001440000001055312524506075015177 0ustar ripleyusersrmvpGibbs=function(Data,Prior,Mcmc){ # # Revision History: # modified by rossi 12/18/04 to include error checking # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # # purpose: Gibbs MVP model with full covariance matrix # # Arguments: # Data contains # p the number of alternatives (could be time or could be from pick j of p survey) # y -- a vector of length n*p of indicators (1 if "chosen" if not) # X -- np x k matrix of covariates (including intercepts) # each X_i is p x nvar # # Prior contains a list of (betabar, A, nu, V) # if elements of prior do not exist, defaults are used # # Mcmc is a list of (beta0,sigma0,R,keep) # beta0,sigma0 are intial values, if not supplied defaults are used # R is number of draws # keep is thinning parm, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # Output: a list of every keepth betadraw and sigmsdraw # # model: # w_i = X_ibeta + e e~N(0,Sigma) note w_i,e are p x 1 # y_ij = 1 if w_ij > 0 else y_ij = 0 # # priors: # beta ~ N(betabar,A^-1) in prior # Sigma ~ IW(nu,V) # # Check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p, y, X")} if(is.null(Data$p)) {pandterm("Requires Data element p -- number of binary indicators")} p=Data$p if(is.null(Data$y)) {pandterm("Requires Data element y -- values of binary indicators")} y=Data$y if(is.null(Data$X)) {pandterm("Requires Data element X -- matrix of covariates")} X=Data$X # # check data for validity # levely=as.numeric(levels(as.factor(y))) bady=FALSE for (i in 0:1) { if(levely[i+1] != i) {bady=TRUE} } cat("Table of y values",fill=TRUE) print(table(y)) if (bady) {pandterm("Invalid y")} if (length(y)%%p !=0) {pandterm("length of y is not a multiple of p")} n=length(y)/p k=ncol(X) if(nrow(X) != (n*p)) {pandterm(paste("X has ",nrow(X)," rows; must be = p*n"))} # # check for prior elements # if(missing(Prior)) { betabar=rep(0,k) ; A=BayesmConstant.A*diag(k) ; nu=p+BayesmConstant.nuInc; V=nu*diag(p)} else {if(is.null(Prior$betabar)) {betabar=rep(0,k)} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(k)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=p+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(p)} else {V=Prior$V}} if(length(betabar) != k) pandterm("length betabar ne k") if(sum(dim(A)==c(k,k)) != 2) pandterm("A is of incorrect dimension") if(nu < 1) pandterm("invalid nu value") if(sum(dim(V)==c(p,p)) != 2) pandterm("V is of incorrect dimension") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R must be included") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$beta0)) {beta0=rep(0,k)} else {beta0=Mcmc$beta0} if(is.null(Mcmc$sigma0)) {sigma0=diag(p)} else {sigma0=Mcmc$sigma0} if(length(beta0) != k) pandterm("beta0 is not of length k") if(sum(dim(sigma0) == c(p,p)) != 2) pandterm("sigma0 is of incorrect dimension") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} # # print out problem # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for MVP",fill=TRUE) cat(" ",n," obs of ",p," binary indicators; ",k," indep vars (including intercepts)",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms:",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu",fill=TRUE) print(nu) cat("V",fill=TRUE) print(V) cat(" ",fill=TRUE) cat("MCMC Parms:",fill=TRUE) cat(" ",R," reps; keeping every ",keep,"th draw"," nprint= ",nprint,fill=TRUE) cat("initial beta= ",beta0,fill=TRUE) cat("initial sigma= ",fill=TRUE) print(sigma0) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 09/03/2014 ################################################################### loopout = rmvpGibbs_rcpp_loop(R,keep,nprint,p,y,X,beta0,sigma0,V,nu,betabar,A); ################################################################### attributes(loopout$betadraw)$class=c("bayesm.mat","mcmc") attributes(loopout$betadraw)$mcpar=c(1,R,keep) attributes(loopout$sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(loopout$sigmadraw)$mcpar=c(1,R,keep) return(loopout) }bayesm/R/rhierBinLogit.R0000755000176000001440000001575512524506060014667 0ustar ripleyusers# # ----------------------------------------------------------------------------- # rhierBinLogit= function(Data,Prior,Mcmc){ # # revision history: # changed 5/12/05 by Rossi to add error checking # 1/07 removed init.rmultiregfp # 3/07 added classes # # purpose: run binary heterogeneous logit model # # Arguments: # Data contains a list of (lgtdata[[i]],Z) # lgtdata[[i]]=list(y,X) # y is index of brand chosen, y=1 is exp[X'beta]/(1+exp[X'beta]) # X is a matrix that is n_i x by nvar # Z is a matrix of demographic variables nlgt*nz that have been # mean centered so that the intercept is interpretable # Prior contains a list of (nu,V,Deltabar,ADelta) # beta_i ~ N(Z%*%Delta,Vbeta) # vec(Delta) ~ N(vec(Deltabar),Vbeta (x) ADelta^-1) # Vbeta ~ IW(nu,V) # Mcmc is a list of (sbeta,R,keep) # sbeta is scale factor for RW increment for beta_is # R is number of draws # keep every keepth draw # # Output: # a list of Deltadraw (R/keep x nvar x nz), Vbetadraw (R/keep x nvar**2), # llike (R/keep), betadraw is a nlgt x nvar x nz x R/keep array of draws of betas # nunits=length(lgtdata) # # define functions needed # # ------------------------------------------------------------------------ # loglike= function(y,X,beta) { # function computer log likelihood of data for binomial logit model # Pr(y=1) = 1 - Pr(y=0) = exp[X'beta]/(1+exp[X'beta]) prob = exp(X%*%beta)/(1+exp(X%*%beta)) prob = prob*y + (1-prob)*(1-y) sum(log(prob)) } # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of m,lgtdata, and (possibly) Z")} if(is.null(Data$lgtdata)) {pandterm("Requires Data element lgtdata (list of data for each unit)")} lgtdata=Data$lgtdata nlgt=length(lgtdata) if(is.null(Data$Z)) { cat("Z not specified -- putting in iota",fill=TRUE); fsh() ; Z=matrix(rep(1,nlgt),ncol=1)} else {if (nrow(Data$Z) != nlgt) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number logits ",nlgt))} else {Z=Data$Z}} nz=ncol(Z) # # check lgtdata for validity # m=2 # set two choice alternatives for Greg's code ypooled=NULL Xpooled=NULL if(!is.null(lgtdata[[1]]$X)) {oldncol=ncol(lgtdata[[1]]$X)} for (i in 1:nlgt) { if(is.null(lgtdata[[i]]$y)) {pandterm(paste("Requires element y of lgtdata[[",i,"]]"))} if(is.null(lgtdata[[i]]$X)) {pandterm(paste("Requires element X of lgtdata[[",i,"]]"))} ypooled=c(ypooled,lgtdata[[i]]$y) nrowX=nrow(lgtdata[[i]]$X) if((nrowX) !=length(lgtdata[[i]]$y)) {pandterm(paste("nrow(X) ne length(yi); exception at unit",i))} newncol=ncol(lgtdata[[i]]$X) if(newncol != oldncol) {pandterm(paste("All X elements must have same # of cols; exception at unit",i))} Xpooled=rbind(Xpooled,lgtdata[[i]]$X) oldncol=newncol } nvar=ncol(Xpooled) levely=as.numeric(levels(as.factor(ypooled))) if(length(levely) != m) {pandterm(paste("y takes on ",length(levely)," values -- must be = m"))} bady=FALSE for (i in 0:1 ) { if(levely[i+1] != i) bady=TRUE } cat("Table of Y values pooled over all units",fill=TRUE) print(table(ypooled)) if (bady) {pandterm("Invalid Y")} # # check on prior # if(missing(Prior)){ nu=nvar+3 V=nu*diag(nvar) Deltabar=matrix(rep(0,nz*nvar),ncol=nvar) ADelta=.01*diag(nz) } else { if(is.null(Prior$nu)) {nu=nvar+3} else {nu=Prior$nu} if(nu < 1) {pandterm("invalid nu value")} if(is.null(Prior$V)) {V=nu*diag(rep(1,nvar))} else {V=Prior$V} if(sum(dim(V)==c(nvar,nvar)) !=2) pandterm("Invalid V in prior") if(is.null(Prior$ADelta) ) {ADelta=.01*diag(nz)} else {ADelta=Prior$ADelta} if(ncol(ADelta) != nz | nrow(ADelta) != nz) {pandterm("ADelta must be nz x nz")} if(is.null(Prior$Deltabar) ) {Deltabar=matrix(rep(0,nz*nvar),ncol=nvar)} else {Deltabar=Prior$Deltabar} } # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$sbeta)) {sbeta=.2} else {sbeta=Mcmc$sbeta} if(is.null(Mcmc$keep)) {keep=1} else {keep=Mcmc$keep} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} } # # print out problem # cat(" ",fill=TRUE) cat("Attempting MCMC Inference for Hierarchical Binary Logit:",fill=TRUE) cat(paste(" ",nvar," variables in X"),fill=TRUE) cat(paste(" ",nz," variables in Z"),fill=TRUE) cat(paste(" for ",nlgt," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("nu =",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat("Deltabar",fill=TRUE) print(Deltabar) cat("ADelta",fill=TRUE) print(ADelta) cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat(paste("sbeta=",round(sbeta,3)," R= ",R," keep= ",keep),fill=TRUE) cat("",fill=TRUE) nlgt=length(lgtdata) nvar=ncol(lgtdata[[1]]$X) nz=ncol(Z) # # initialize storage for draws # Vbetadraw=matrix(double(floor(R/keep)*nvar*nvar),ncol=nvar*nvar) betadraw=array(double(floor(R/keep)*nlgt*nvar),dim=c(nlgt,nvar,floor(R/keep))) Deltadraw=matrix(double(floor(R/keep)*nvar*nz),ncol=nvar*nz) oldbetas=matrix(double(nlgt*nvar),ncol=nvar) oldVbeta=diag(nvar) oldVbetai=diag(nvar) oldDelta=matrix(double(nvar*nz),ncol=nvar) betad = array(0,dim=c(nvar)) betan = array(0,dim=c(nvar)) reject = array(0,dim=c(R/keep)) llike=array(0,dim=c(R/keep)) itime=proc.time()[3] cat("MCMC Iteration (est time to end - min)",fill=TRUE) fsh() for (j in 1:R) { rej = 0 logl = 0 sV = sbeta*oldVbeta root=t(chol(sV)) # Draw B-h|B-bar, V for (i in 1:nlgt) { betad = oldbetas[i,] betan = betad + root%*%rnorm(nvar) # data lognew = loglike(lgtdata[[i]]$y,lgtdata[[i]]$X,betan) logold = loglike(lgtdata[[i]]$y,lgtdata[[i]]$X,betad) # heterogeneity logknew = -.5*(t(betan)-Z[i,]%*%oldDelta) %*% oldVbetai %*% (betan-t(Z[i,]%*%oldDelta)) logkold = -.5*(t(betad)-Z[i,]%*%oldDelta) %*% oldVbetai %*% (betad-t(Z[i,]%*%oldDelta)) # MH step alpha = exp(lognew + logknew - logold - logkold) if(alpha=="NaN") alpha=-1 u = runif(n=1,min=0, max=1) if(u < alpha) { oldbetas[i,] = betan logl = logl + lognew } else { logl = logl + logold rej = rej+1 } } # Draw B-bar and V as a multivariate regression out=rmultireg(oldbetas,Z,Deltabar,ADelta,nu,V) oldDelta=out$B oldVbeta=out$Sigma oldVbetai=chol2inv(chol(oldVbeta)) if((j%%100)==0) { ctime=proc.time()[3] timetoend=((ctime-itime)/j)*(R-j) cat(" ",j," (",round(timetoend/60,1),")",fill=TRUE) fsh() } mkeep=j/keep if(mkeep*keep == (floor(mkeep)*keep)) {Deltadraw[mkeep,]=as.vector(oldDelta) Vbetadraw[mkeep,]=as.vector(oldVbeta) betadraw[,,mkeep]=oldbetas llike[mkeep]=logl reject[mkeep]=rej/nlgt } } ctime=proc.time()[3] cat(" Total Time Elapsed: ",round((ctime-itime)/60,2),fill=TRUE) attributes(betadraw)$class=c("bayesm.hcoef") attributes(Deltadraw)$class=c("bayesm.mat","mcmc") attributes(Deltadraw)$mcpar=c(1,R,keep) attributes(Vbetadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(Vbetadraw)$mcpar=c(1,R,keep) return(list(betadraw=betadraw,Vbetadraw=Vbetadraw,Deltadraw=Deltadraw,llike=llike,reject=reject)) } bayesm/R/rscaleusage_rcpp.r0000644000176000001440000002271712524506102015476 0ustar ripleyusersrscaleUsage= function(Data,Prior,Mcmc) { # # purpose: run scale-usage mcmc # draws y,Sigma,mu,tau,sigma,Lambda,e # R. McCulloch 12/28/04 # added classes 3/07 # # arguments: # Data: # all components are required: # k: integer giving the scale of the responses, each observation is an integer from 1,2,...k # x: data, num rows=number of respondents, num columns = number of questions # Prior: # all components are optional # nu,V: Sigma ~ IW(nu,V) # mubar,Am: mu ~N(mubar,Am^{-1}) # gsigma: grid for sigma # gl11,gl22,gl12: grids for ij element of Lambda # Lambdanu,LambdaV: Lambda ~ IW(Lambdanu,LambdaV) # ge: grid for e # Mcmc: # all components are optional (but you would typically want to specify R= number of draws) # R: number of mcmc iterations # keep: frequency with which draw is kept # ndghk: number of draws for ghk # nprint - print estimated time remaining on every nprint'th draw # e,y,mu,Sigma,sigma,tau,Lambda: initial values for the state # doe, ...doLambda: indicates whether draw should be made # output: # List with draws of each of Sigma,mu,tau,sigma,Lambda,e # eg. result$Sigma is the draws of Sigma # Each component is a matrix expept e, which is a vector # for the matrices Sigma and Lambda each row transpose of the Vec # eg. result$Lambda has rows (Lambda11,Lambda21,Lambda12,Lambda22) # # define functions needed # # ----------------------------------------------------------------------------------- myin = function(i,ind) {i %in% ind} ispd = function(mat,d=nrow(mat)) { if(!is.matrix(mat)) { res = FALSE } else if(!((nrow(mat)==d) & (ncol(mat)==d))) { res = FALSE } else { diff = (t(mat)+mat)/2 - mat perdiff = sum(diff^2)/sum(mat^2) res = ((det(mat)>0) & (perdiff < 1e-10)) } res } #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # print out components of inputs ---------------------------------------------- cat('\nIn function rscaleUsage\n\n') if(!missing(Data)) { cat(' Data has components: ') cat(paste(names(Data),collapse=' ')[1],'\n') } if(!missing(Prior)) { cat(' Prior has components: ') cat(paste(names(Prior),collapse=' ')[1],'\n') } if(!missing(Mcmc)) { cat(' Mcmc has components: ') cat(paste(names(Mcmc),collapse=' ')[1],'\n') } cat('\n') # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # process Data argument -------------------------- if(missing(Data)) {pandterm("Requires Data argument - list of k=question scale and x = data")} if(is.null(Data$k)) { pandterm("k not specified") } else { k = as.integer(Data$k) if(!((k>0) & (k<50))) {pandterm("Data$k must be integer between 1 and 50")} } if(is.null(Data$x)) { pandterm('x (the data), not specified') } else { if(!is.matrix(Data$x)) {pandterm('Data$x must be a matrix')} x = matrix(as.integer(Data$x),nrow=nrow(Data$x)) checkx = sum(sapply(as.vector(x),myin,1:k)) if(!(checkx == nrow(x)*ncol(x))) {pandterm('each element of Data$x must be in 1,2...k')} p = ncol(x) n = nrow(x) if((p<2) | (n<1)) {pandterm(paste('invalid dimensions for x: nrow,ncol: ',n,p))} } # ++++++++++++++++++++++++++++++++++++++++++++++++ # process Mcmc argument --------------------- #run mcmc R = 1000 keep = BayesmConstant.keep ndghk= 100 nprint = BayesmConstant.nprint if(!missing(Mcmc)) { if(!is.null(Mcmc$R)) { R = as.integer(Mcmc$R) } if(!is.null(Mcmc$keep)) { keep = as.integer(Mcmc$keep) } if(!is.null(Mcmc$ndghk)) { ndghk = as.integer(Mcmc$ndghk) } if(!is.null(Mcmc$nprint)) { nprint = as.integer(Mcmc$nprint) } } if(R<1) { pandterm('R must be positive')} if(keep<1) { pandterm('keep must be positive') } if(ndghk<1) { pandterm('ndghk must be positive') } if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} #state y = matrix(as.double(x),nrow=nrow(x)) mu = apply(y,2,mean) Sigma = var(y) tau = rep(0,n) sigma = rep(1,n) #Lambda = matrix(c(3.7,-.22,-.22,.32),ncol=2) #Lambda = matrix(c((k/4)^2,(k/4)*.5*(-.2),0,.25),nrow=2); Lambda[1,2]=Lambda[2,1] Lambda = matrix(c(4,0,0,.5),ncol=2) e=0 if(!missing(Mcmc)) { if(!is.null(Mcmc$y)) { y = Mcmc$y } if(!is.null(Mcmc$mu)) { mu = Mcmc$mu } if(!is.null(Mcmc$Sigma)) { Sigma = Mcmc$Sigma } if(!is.null(Mcmc$tau)) { tau = Mcmc$tau } if(!is.null(Mcmc$sigma)) { sigma = Mcmc$sigma } if(!is.null(Mcmc$Lambda)) { Lambda = Mcmc$Lambda } if(!is.null(Mcmc$e)) { e = Mcmc$e } } if(!ispd(Sigma,p)) { pandterm(paste('Sigma must be positive definite with dimension ',p)) } if(!ispd(Lambda,2)) { pandterm(paste('Lambda must be positive definite with dimension ',2)) } if(!is.vector(mu)) { pandterm('mu must be a vector') } if(length(mu) != p) { pandterm(paste('mu must have length ',p)) } if(length(tau) != n) { pandterm(paste('tau must have length ',n)) } if(!is.vector(sigma)) { pandterm('sigma must be a vector') } if(length(sigma) != n) { pandterm(paste('sigma must have length ',n)) } if(!is.matrix(y)) { pandterm('y must be a matrix') } if(nrow(y) != n) { pandterm(paste('y must have',n,'rows')) } if(ncol(y) != p) { pandterm(paste('y must have',p,'columns')) } #do draws domu=TRUE doSigma=TRUE dosigma=TRUE dotau=TRUE doLambda=TRUE doe=TRUE if(!missing(Mcmc)) { if(!is.null(Mcmc$domu)) { domu = Mcmc$domu } if(!is.null(Mcmc$doSigma)) { doSigma = Mcmc$doSigma } if(!is.null(Mcmc$dotau)) { dotau = Mcmc$dotau } if(!is.null(Mcmc$dosigma)) { dosigma = Mcmc$dosigma } if(!is.null(Mcmc$doLambda)) { doLambda = Mcmc$doLambda } if(!is.null(Mcmc$doe)) { doe = Mcmc$doe } } #++++++++++++++++++++++++++++++++++++++ #process Prior argument ---------------------------------- nu = p+BayesmConstant.nuInc V= nu*diag(p) mubar = matrix(rep(k/2,p),ncol=1) Am = BayesmConstant.A*diag(p) gs = 200 gsigma = 6*(1:gs)/gs gl11 = .1 + 5.9*(1:gs)/gs gl22 = .1 + 2.0*(1:gs)/gs #gl12 = -.8 + 1.6*(1:gs)/gs gl12 = -2.0 + 4*(1:gs)/gs nuL=20 VL = (nuL-3)*Lambda ge = -.1+.2*(0:gs)/gs if(!missing(Prior)) { if(!is.null(Prior$nu)) { nu = Prior$nu; V = nu*diag(p) } if(!is.null(Prior$V)) { V = Prior$V } if(!is.null(Prior$mubar)) { mubar = matrix(Prior$mubar,ncol=1) } if(!is.null(Prior$Am)) { Am = Prior$Am } if(!is.null(Prior$gsigma)) { gsigma = Prior$gsigma } if(!is.null(Prior$gl11)) { gl11 = Prior$gl11 } if(!is.null(Prior$gl22)) { gl22 = Prior$gl22 } if(!is.null(Prior$gl12)) { gl12 = Prior$gl12 } if(!is.null(Prior$Lambdanu)) { nuL = Prior$Lambdanu; VL = (nuL-3)*Lambda } if(!is.null(Prior$LambdaV)) { VL = Prior$LambdaV } if(!is.null(Prior$ge)) { ge = Prior$ge } } if(!ispd(V,p)) { pandterm(paste('V must be positive definite with dimension ',p)) } if(!ispd(Am,p)) { pandterm(paste('Am must be positive definite with dimension ',p)) } if(!ispd(VL,2)) { pandterm(paste('VL must be positive definite with dimension ',2)) } if(nrow(mubar) != p) { pandterm(paste('mubar must have length',p)) } #++++++++++++++++++++++++++++++++++++++++ #print out run info ------------------------- # # note in the documentation and in BSM, m is used instead of p # for print-out purposes I'm using m P. Rossi 12/06 cat(' n,m,k: ', n,p,k,'\n') cat(' R,keep,ndghk,nprint: ', R,keep,ndghk,nprint,'\n') cat('\n') cat(' Data:\n') cat(' x[1,1],x[n,1],x[1,m],x[n,m]: ',x[1,1],x[n,1],x[1,p],x[n,p],'\n\n') cat(' Prior:\n') cat(' ','nu: ',nu,'\n') cat(' ','V[1,1]/nu,V[m,m]/nu: ',V[1,1]/nu,V[p,p]/nu,'\n') cat(' ','mubar[1],mubar[m]: ',mubar[1],mubar[p],'\n') cat(' ','Am[1,1],Am[m,m]: ',Am[1,1],Am[p,p],'\n') cat(' ','Lambdanu: ',nuL,'\n') cat(' ','LambdaV11,22/(Lambdanu-3): ',VL[1,1]/(nuL-3),VL[2,2]/(nuL-3),'\n') cat(' ','sigma grid, 1,',length(gsigma),': ',gsigma[1],', ',gsigma[length(gsigma)],'\n') cat(' ','Lambda11 grid, 1,',length(gl11),': ',gl11[1],', ',gl11[length(gl11)],'\n') cat(' ','Lambda12 grid, 1,',length(gl12),': ',gl12[1],', ',gl12[length(gl12)],'\n') cat(' ','Lambda22 grid, 1,',length(gl22),': ',gl22[1],', ',gl22[length(gl22)],'\n') cat(' ','e grid, 1,',length(ge),': ',ge[1],', ',ge[length(ge)],'\n') cat(' ','draw e: ',doe,'\n') cat(' ','draw Lambda: ',doLambda,'\n') #++++++++++++++++++++++++++++++++++++++++++++ ################################################################### # Wayne Taylor # 3/14/2015 ################################################################### out = rscaleUsage_rcpp_loop(k,x,p,n, R,keep,ndghk,nprint, y,mu,Sigma,tau,sigma,Lambda,e, domu,doSigma,dosigma,dotau,doLambda,doe, nu,V,mubar,Am, gsigma,gl11,gl22,gl12, nuL,VL,ge) R = out$ndpost ################################################################### attributes(out$drmu)$class=c("bayesm.mat","mcmc") attributes(out$drmu)$mcpar=c(1,R,keep) attributes(out$drtau)$class=c("bayesm.mat","mcmc") attributes(out$drtau)$mcpar=c(1,R,keep) attributes(out$drsigma)$class=c("bayesm.mat","mcmc") attributes(out$drsigma)$mcpar=c(1,R,keep) attributes(out$drLambda)$class=c("bayesm.mat","mcmc") attributes(out$drLambda)$mcpar=c(1,R,keep) attributes(out$dre)$class=c("bayesm.mat","mcmc") attributes(out$dre)$mcpar=c(1,R,keep) attributes(out$drSigma)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(out$drSigma)$mcpar=c(1,R,keep) return(list(Sigmadraw=out$drSigma,mudraw=out$drmu,taudraw = out$drtau, sigmadraw=out$drsigma,Lambdadraw=out$drLambda,edraw=out$dre)) } bayesm/R/summary.bayesm.nmix.R0000755000176000001440000000261710647461730016055 0ustar ripleyuserssummary.bayesm.nmix=function(object,names,burnin=trunc(.1*nrow(probdraw)),...){ nmixlist=object if(mode(nmixlist) != "list") stop(" Argument must be a list \n") probdraw=nmixlist[[1]]; compdraw=nmixlist[[3]] if(!is.matrix(probdraw)) stop(" First Element of List (probdraw) must be a matrix \n") if(mode(compdraw) != "list") stop(" Third Element of List (compdraw) must be a list \n") ncomp=length(compdraw[[1]]) if(ncol(probdraw) != ncomp) stop(" Dim of First Element of List not compatible with Dim of Second \n") # # function to summarize draws of normal mixture components # R=nrow(probdraw) if(R < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} datad=length(compdraw[[1]][[1]]$mu) mumat=matrix(0,nrow=R,ncol=datad) sigmat=matrix(0,nrow=R,ncol=(datad*datad)) if(missing(names)) names=as.character(1:datad) for(i in (burnin+1):R){ if(i%%500 ==0) cat("processing draw ",i,"\n",sep="");fsh() out=momMix(probdraw[i,,drop=FALSE],compdraw[i]) mumat[i,]=out$mu sigmat[i,]=out$sigma } cat("\nNormal Mixture Moments\n Mean\n") attributes(mumat)$class="bayesm.mat" attributes(sigmat)$class="bayesm.var" summary(mumat,names,burnin=burnin,QUANTILES=FALSE,TRAILER=FALSE) cat(" \n") summary(sigmat,burnin=burnin) cat("note: 1st and 2nd Moments for a Normal Mixture \n") cat(" may not be interpretable, consider plots\n") invisible() } bayesm/R/rhierMnlDP_rcpp.r0000644000176000001440000002766712524557300015223 0ustar ripleyusersrhierMnlDP=function(Data,Prior,Mcmc){ # # created 3/08 by Rossi from rhierMnlRwMixture adding DP draw for to replace finite mixture of normals # # revision history: # changed 12/17/04 by rossi to fix bug in drawdelta when there is zero/one unit # in a mixture component # added loglike output, changed to reflect new argument order in llmnl, mnlHess 9/05 # changed weighting scheme to (1-w)logl_i + w*Lbar (normalized) 12/05 # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: run hierarchical mnl logit model with mixture of normals # using RW and cov(RW inc) = (hess_i + Vbeta^-1)^-1 # uses normal approximation to pooled likelihood # # Arguments: # Data contains a list of (p,lgtdata, and possibly Z) # p is number of choice alternatives # lgtdata is a list of lists (one list per unit) # lgtdata[[i]]=list(y,X) # y is a vector indicating alternative chosen # integers 1:p indicate alternative # X is a length(y)*p x nvar matrix of values of # X vars including intercepts # Z is an length(lgtdata) x nz matrix of values of variables # note: Z should NOT contain an intercept # Prior contains a list of (deltabar,Ad,lambda_hyper,Prioralpha) # alpha: starting value # lambda_hyper: hyperparms of prior on lambda # Prioralpha: hyperparms of alpha prior; a list of (Istarmin,Istarmax,power) # if elements of the prior don't exist, defaults are assumed # Mcmc contains a list of (s,c,R,keep,nprint) # # Output: as list containing # Deltadraw R/keep x nz*nvar matrix of draws of Delta, first row is initial value # betadraw is nlgt x nvar x R/keep array of draws of betas # probdraw is R/keep x 1 matrix of draws of probs of mixture components # compdraw is a list of list of lists (length R/keep) # compdraw[[rep]] is the repth draw of components for mixtures # loglike log-likelikelhood at each kept draw # # Priors: # beta_i = D %*% z[i,] + u_i # vec(D)~N(deltabar) # u_i ~ N(theta_i) # theta_i~G # G|lambda,alpha ~ DP(G|G0(lambda),alpha) # # lambda: # G0 ~ N(mubar,Sigma (x) Amu^-1) # mubar=vec(mubar) # Sigma ~ IW(nu,nu*v*I) note: mode(Sigma)=nu/(nu+2)*v*I # mubar=0 # amu is uniform on grid specified by alim # nu is log uniform, nu=d-1+exp(Z) z is uniform on seq defined bvy nulim # v is uniform on sequence specificd by vlim # # Prioralpha: # alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power # alphamin=exp(digamma(Istarmin)-log(gamma+log(N))) # alphamax=exp(digamma(Istarmax)-log(gamma+log(N))) # gamma= .5772156649015328606 # # MCMC parameters # s is the scaling parameter for the RW inc covariance matrix; s^2 Var is inc cov # matrix # w is parameter for weighting function in fractional likelihood # w is the weight on the normalized pooled likelihood # R is number of draws # keep is thinning parameter, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw #-------------------------------------------------------------------------------------------------- llmnlFract= function(beta,y,X,betapooled,rootH,w,wgt){ z=as.vector(rootH%*%(beta-betapooled)) return((1-w)*llmnl(beta,y,X)+w*wgt*(-.5*(z%*%z))) } # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p,lgtdata, and (possibly) Z")} if(is.null(Data$p)) {pandterm("Requires Data element p (# chce alternatives)") } p=Data$p if(is.null(Data$lgtdata)) {pandterm("Requires Data element lgtdata (list of data for each unit)")} lgtdata=Data$lgtdata nlgt=length(lgtdata) drawdelta=TRUE if(is.null(Data$Z)) { cat("Z not specified",fill=TRUE); fsh() ; drawdelta=FALSE} else {if (nrow(Data$Z) != nlgt) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number logits ",nlgt))} else {Z=Data$Z}} if(drawdelta) { nz=ncol(Z) colmeans=apply(Z,2,mean) if(sum(colmeans) > .00001) {pandterm(paste("Z does not appear to be de-meaned: colmeans= ",colmeans))} } # # check lgtdata for validity # ypooled=NULL Xpooled=NULL if(!is.null(lgtdata[[1]]$X)) {oldncol=ncol(lgtdata[[1]]$X)} for (i in 1:nlgt) { if(is.null(lgtdata[[i]]$y)) {pandterm(paste("Requires element y of lgtdata[[",i,"]]"))} if(is.null(lgtdata[[i]]$X)) {pandterm(paste("Requires element X of lgtdata[[",i,"]]"))} ypooled=c(ypooled,lgtdata[[i]]$y) nrowX=nrow(lgtdata[[i]]$X) if((nrowX/p) !=length(lgtdata[[i]]$y)) {pandterm(paste("nrow(X) ne p*length(yi); exception at unit",i))} newncol=ncol(lgtdata[[i]]$X) if(newncol != oldncol) {pandterm(paste("All X elements must have same # of cols; exception at unit",i))} Xpooled=rbind(Xpooled,lgtdata[[i]]$X) oldncol=newncol } nvar=ncol(Xpooled) levely=as.numeric(levels(as.factor(ypooled))) if(length(levely) != p) {pandterm(paste("y takes on ",length(levely)," values -- must be = p"))} bady=FALSE for (i in 1:p ) { if(levely[i] != i) bady=TRUE } cat("Table of Y values pooled over all units",fill=TRUE) print(table(ypooled)) if (bady) {pandterm("Invalid Y")} # # check on prior # alimdef=BayesmConstant.DPalimdef nulimdef=BayesmConstant.DPnulimdef vlimdef=BayesmConstant.DPvlimdef if(missing(Prior)) {Prior=NULL} if(is.null(Prior$lambda_hyper)) {lambda_hyper=list(alim=alimdef,nulim=nulimdef,vlim=vlimdef)} else {lambda_hyper=Prior$lambda_hyper; if(is.null(lambda_hyper$alim)) {lambda_hyper$alim=alimdef} if(is.null(lambda_hyper$nulim)) {lambda_hyper$nulim=nulimdef} if(is.null(lambda_hyper$vlim)) {lambda_hyper$vlim=vlimdef} } if(is.null(Prior$Prioralpha)) {Prioralpha=list(Istarmin=BayesmConstant.DPIstarmin,Istarmax=min(50,0.1*nlgt),power=BayesmConstant.DPpower)} else {Prioralpha=Prior$Prioralpha; if(is.null(Prioralpha$Istarmin)) {Prioralpha$Istarmin=BayesmConstant.DPIstarmin} else {Prioralpha$Istarmin=Prioralpha$Istarmin} if(is.null(Prioralpha$Istarmax)) {Prioralpha$Istarmax=min(50,0.1*nlgt)} else {Prioralpha$Istarmax=Prioralpha$Istarmax} if(is.null(Prioralpha$power)) {Prioralpha$power=BayesmConstant.DPpower} } gamma= BayesmConstant.gamma Prioralpha$alphamin=exp(digamma(Prioralpha$Istarmin)-log(gamma+log(nlgt))) Prioralpha$alphamax=exp(digamma(Prioralpha$Istarmax)-log(gamma+log(nlgt))) Prioralpha$n=nlgt # # check Prior arguments for valdity # if(lambda_hyper$alim[1]<0) {pandterm("alim[1] must be >0")} if(lambda_hyper$nulim[1]<0) {pandterm("nulim[1] must be >0")} if(lambda_hyper$vlim[1]<0) {pandterm("vlim[1] must be >0")} if(Prioralpha$Istarmin <1){pandterm("Prioralpha$Istarmin must be >= 1")} if(Prioralpha$Istarmax <= Prioralpha$Istarmin){pandterm("Prioralpha$Istarmin must be < Prioralpha$Istarmax")} if(is.null(Prior$Ad) & drawdelta) {Ad=BayesmConstant.A*diag(nvar*nz)} else {Ad=Prior$Ad} if(drawdelta) {if(ncol(Ad) != nvar*nz | nrow(Ad) != nvar*nz) {pandterm("Ad must be nvar*nz x nvar*nz")}} if(is.null(Prior$deltabar)& drawdelta) {deltabar=rep(0,nz*nvar)} else {deltabar=Prior$deltabar} if(drawdelta) {if(length(deltabar) != nz*nvar) {pandterm("deltabar must be of length nvar*nz")}} # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$s)) {s=BayesmConstant.RRScaling/sqrt(nvar)} else {s=Mcmc$s} if(is.null(Mcmc$w)) {w=BayesmConstant.w} else {w=Mcmc$w} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$maxuniq)) {maxuniq=BayesmConstant.DPmaxuniq} else {keep=Mcmc$maxuniq} if(is.null(Mcmc$gridsize)) {gridsize=BayesmConstant.DPgridsize} else {gridsize=Mcmc$gridsize} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} } # # print out problem # cat(" ",fill=TRUE) cat("Starting MCMC Inference for Hierarchical Logit:",fill=TRUE) cat(" Dirichlet Process Prior",fill=TRUE) cat(paste(" ",p," alternatives; ",nvar," variables in X"),fill=TRUE) cat(paste(" for ",nlgt," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat(" Prior Parms: ",fill=TRUE) cat(" G0 ~ N(mubar,Sigma (x) Amu^-1)",fill=TRUE) cat(" mubar = ",0,fill=TRUE) cat(" Sigma ~ IW(nu,nu*v*I)",fill=TRUE) cat(" Amu ~ uniform[",lambda_hyper$alim[1],",",lambda_hyper$alim[2],"]",fill=TRUE) cat(" nu ~ uniform on log grid [",nvar-1+exp(lambda_hyper$nulim[1]), ",",nvar-1+exp(lambda_hyper$nulim[2]),"]",fill=TRUE) cat(" v ~ uniform[",lambda_hyper$vlim[1],",",lambda_hyper$vlim[2],"]",fill=TRUE) cat(" ",fill=TRUE) cat(" alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power",fill=TRUE) cat(" Istarmin = ",Prioralpha$Istarmin,fill=TRUE) cat(" Istarmax = ",Prioralpha$Istarmax,fill=TRUE) cat(" alphamin = ",Prioralpha$alphamin,fill=TRUE) cat(" alphamax = ",Prioralpha$alphamax,fill=TRUE) cat(" power = ",Prioralpha$power,fill=TRUE) cat(" ",fill=TRUE) if(drawdelta) { cat("deltabar",fill=TRUE) print(deltabar) cat("Ad",fill=TRUE) print(Ad) } cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat(paste("s=",round(s,3)," w= ",w," R= ",R," keep= ",keep," nprint= ",nprint," maxuniq= ",maxuniq, " gridsize for lambda hyperparms= ",gridsize),fill=TRUE) cat("",fill=TRUE) # # allocate space for draws # oldbetas=matrix(double(nlgt*nvar),ncol=nvar) # # intialize compute quantities for Metropolis # cat("initializing Metropolis candidate densities for ",nlgt," units ...",fill=TRUE) fsh() # # now go thru and computed fraction likelihood estimates and hessians # # Lbar=log(pooled likelihood^(n_i/N)) # # fraction loglike = (1-w)*loglike_i + w*Lbar # betainit=c(rep(0,nvar)) # # compute pooled optimum # out=optim(betainit,llmnl,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-6), X=Xpooled,y=ypooled) betapooled=out$par H=mnlHess(betapooled,ypooled,Xpooled) rootH=chol(H) # # initialize betas for all units # for (i in 1:nlgt) { wgt=length(lgtdata[[i]]$y)/length(ypooled) out=optim(betapooled,llmnlFract,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-4), X=lgtdata[[i]]$X,y=lgtdata[[i]]$y,betapooled=betapooled,rootH=rootH,w=w,wgt=wgt) if(out$convergence == 0) { hess=mnlHess(out$par,lgtdata[[i]]$y,lgtdata[[i]]$X) lgtdata[[i]]=c(lgtdata[[i]],list(converge=1,betafmle=out$par,hess=hess)) } else { lgtdata[[i]]=c(lgtdata[[i]],list(converge=0,betafmle=c(rep(0,nvar)), hess=diag(nvar))) } oldbetas[i,]=lgtdata[[i]]$betafmle if(i%%50 ==0) cat(" completed unit #",i,fill=TRUE) fsh() } #Initialize placeholders when drawdelta == FALSE if (drawdelta==FALSE){ Z = matrix(0) deltabar = 0 Ad = matrix(0) } ################################################################### # Wayne Taylor # 2/21/2015 ################################################################### out = rhierMnlDP_rcpp_loop(R,keep,nprint, lgtdata,Z,deltabar,Ad,Prioralpha,lambda_hyper, drawdelta,nvar,oldbetas,s,maxuniq,gridsize, BayesmConstant.A,BayesmConstant.nuInc,BayesmConstant.DPalpha) ################################################################### if(drawdelta){ attributes(out$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(out$Deltadraw)$mcpar=c(1,R,keep)} attributes(out$betadraw)$class=c("bayesm.hcoef") attributes(out$nmix)$class="bayesm.nmix" attributes(out$adraw)$class=c("bayesm.mat","mcmc") attributes(out$nudraw)$class=c("bayesm.mat","mcmc") attributes(out$vdraw)$class=c("bayesm.mat","mcmc") attributes(out$Istardraw)$class=c("bayesm.mat","mcmc") attributes(out$alphadraw)$class=c("bayesm.mat","mcmc") return(out) }bayesm/R/rhierMnlRwMixture_rcpp.r0000644000176000001440000002434512524560256016660 0ustar ripleyusersrhierMnlRwMixture=function(Data,Prior,Mcmc){ # # revision history: # changed 12/17/04 by rossi to fix bug in drawdelta when there is zero/one unit # in a mixture component # added loglike output, changed to reflect new argument order in llmnl, mnlHess 9/05 # changed weighting scheme to (1-w)logl_i + w*Lbar (normalized) 12/05 # 3/07 added classes # 9/08 changed Dirichlet a check # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: run hierarchical mnl logit model with mixture of normals # using RW and cov(RW inc) = (hess_i + Vbeta^-1)^-1 # uses normal approximation to pooled likelihood # # Arguments: # Data contains a list of (p,lgtdata, and possibly Z) # p is number of choice alternatives # lgtdata is a list of lists (one list per unit) # lgtdata[[i]]=list(y,X) # y is a vector indicating alternative chosen # integers 1:p indicate alternative # X is a length(y)*p x nvar matrix of values of # X vars including intercepts # Z is an length(lgtdata) x nz matrix of values of variables # note: Z should NOT contain an intercept # Prior contains a list of (deltabar,Ad,mubar,Amu,nu,V,ncomp) # ncomp is the number of components in normal mixture # if elements of Prior (other than ncomp) do not exist, defaults are used # Mcmc contains a list of (s,c,R,keep,nprint) # # Output: as list containing # Deltadraw R/keep x nz*nvar matrix of draws of Delta, first row is initial value # betadraw is nlgt x nvar x R/keep array of draws of betas # probdraw is R/keep x ncomp matrix of draws of probs of mixture components # compdraw is a list of list of lists (length R/keep) # compdraw[[rep]] is the repth draw of components for mixtures # loglike log-likelikelhood at each kept draw # # Priors: # beta_i = D %*% z[i,] + u_i # u_i ~ N(mu_ind[i],Sigma_ind[i]) # ind[i] ~multinomial(p) # p ~ dirichlet (a) # D is a k x nz array # delta= vec(D) ~ N(deltabar,A_d^-1) # mu_j ~ N(mubar,A_mu^-1(x)Sigma_j) # Sigma_j ~ IW(nu,V^-1) # ncomp is number of components # # MCMC parameters # s is the scaling parameter for the RW inc covariance matrix; s^2 Var is inc cov # matrix # w is parameter for weighting function in fractional likelihood # w is the weight on the normalized pooled likelihood # R is number of draws # keep is thinning parameter, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p,lgtdata, and (possibly) Z")} if(is.null(Data$p)) {pandterm("Requires Data element p (# chce alternatives)") } p=Data$p if(is.null(Data$lgtdata)) {pandterm("Requires Data element lgtdata (list of data for each unit)")} lgtdata=Data$lgtdata nlgt=length(lgtdata) drawdelta=TRUE if(is.null(Data$Z)) { cat("Z not specified",fill=TRUE); fsh() ; drawdelta=FALSE} else {if (nrow(Data$Z) != nlgt) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number logits ",nlgt))} else {Z=Data$Z}} if(drawdelta) { nz=ncol(Z) colmeans=apply(Z,2,mean) if(sum(colmeans) > .00001) {pandterm(paste("Z does not appear to be de-meaned: colmeans= ",colmeans))} } # # check lgtdata for validity # ypooled=NULL Xpooled=NULL if(!is.null(lgtdata[[1]]$X)) {oldncol=ncol(lgtdata[[1]]$X)} for (i in 1:nlgt) { if(is.null(lgtdata[[i]]$y)) {pandterm(paste("Requires element y of lgtdata[[",i,"]]"))} if(is.null(lgtdata[[i]]$X)) {pandterm(paste("Requires element X of lgtdata[[",i,"]]"))} ypooled=c(ypooled,lgtdata[[i]]$y) nrowX=nrow(lgtdata[[i]]$X) if((nrowX/p) !=length(lgtdata[[i]]$y)) {pandterm(paste("nrow(X) ne p*length(yi); exception at unit",i))} newncol=ncol(lgtdata[[i]]$X) if(newncol != oldncol) {pandterm(paste("All X elements must have same # of cols; exception at unit",i))} Xpooled=rbind(Xpooled,lgtdata[[i]]$X) oldncol=newncol } nvar=ncol(Xpooled) levely=as.numeric(levels(as.factor(ypooled))) if(length(levely) != p) {pandterm(paste("y takes on ",length(levely)," values -- must be = p"))} bady=FALSE for (i in 1:p ) { if(levely[i] != i) bady=TRUE } cat("Table of Y values pooled over all units",fill=TRUE) print(table(ypooled)) if (bady) {pandterm("Invalid Y")} # # check on prior # if(missing(Prior)) {pandterm("Requires Prior list argument (at least ncomp)")} if(is.null(Prior$ncomp)) {pandterm("Requires Prior element ncomp (num of mixture components)")} else {ncomp=Prior$ncomp} if(is.null(Prior$mubar)) {mubar=matrix(rep(0,nvar),nrow=1)} else { mubar=matrix(Prior$mubar,nrow=1)} if(ncol(mubar) != nvar) {pandterm(paste("mubar must have ncomp cols, ncol(mubar)= ",ncol(mubar)))} if(is.null(Prior$Amu)) {Amu=matrix(BayesmConstant.A,ncol=1)} else {Amu=matrix(Prior$Amu,ncol=1)} if(ncol(Amu) != 1 | nrow(Amu) != 1) {pandterm("Am must be a 1 x 1 array")} if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu} if(nu < 1) {pandterm("invalid nu value")} if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V} if(sum(dim(V)==c(nvar,nvar)) !=2) pandterm("Invalid V in prior") if(is.null(Prior$Ad) & drawdelta) {Ad=BayesmConstant.A*diag(nvar*nz)} else {Ad=Prior$Ad} if(drawdelta) {if(ncol(Ad) != nvar*nz | nrow(Ad) != nvar*nz) {pandterm("Ad must be nvar*nz x nvar*nz")}} if(is.null(Prior$deltabar)& drawdelta) {deltabar=rep(0,nz*nvar)} else {deltabar=Prior$deltabar} if(drawdelta) {if(length(deltabar) != nz*nvar) {pandterm("deltabar must be of length nvar*nz")}} if(is.null(Prior$a)) { a=rep(BayesmConstant.a,ncomp)} else {a=Prior$a} if(length(a) != ncomp) {pandterm("Requires dim(a)= ncomp (no of components)")} bada=FALSE for(i in 1:ncomp) { if(a[i] < 0) bada=TRUE} if(bada) pandterm("invalid values in a vector") # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$s)) {s=BayesmConstant.RRScaling/sqrt(nvar)} else {s=Mcmc$s} if(is.null(Mcmc$w)) {w=BayesmConstant.w} else {w=Mcmc$w} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ",fill=TRUE) cat("Starting MCMC Inference for Hierarchical Logit:",fill=TRUE) cat(" Normal Mixture with",ncomp,"components for first stage prior",fill=TRUE) cat(paste(" ",p," alternatives; ",nvar," variables in X"),fill=TRUE) cat(paste(" for ",nlgt," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("nu =",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat("mubar ",fill=TRUE) print(mubar) cat("Amu ", fill=TRUE) print(Amu) cat("a ",fill=TRUE) print(a) if(drawdelta) { cat("deltabar",fill=TRUE) print(deltabar) cat("Ad",fill=TRUE) print(Ad) } cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat(paste("s=",round(s,3)," w= ",w," R= ",R," keep= ",keep," nprint= ",nprint),fill=TRUE) cat("",fill=TRUE) oldbetas = matrix(double(nlgt * nvar), ncol = nvar) #-------------------------------------------------------------------------------------------------- # # create functions needed # llmnlFract= function(beta,y,X,betapooled,rootH,w,wgt){ z=as.vector(rootH%*%(beta-betapooled)) return((1-w)*llmnl(beta,y,X)+w*wgt*(-.5*(z%*%z))) } #------------------------------------------------------------------------------------------------------- # # intialize compute quantities for Metropolis # cat("initializing Metropolis candidate densities for ",nlgt," units ...",fill=TRUE) fsh() # # now go thru and computed fraction likelihood estimates and hessians # # Lbar=log(pooled likelihood^(n_i/N)) # # fraction loglike = (1-w)*loglike_i + w*Lbar # betainit=c(rep(0,nvar)) # # compute pooled optimum # out=optim(betainit,llmnl,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-6), X=Xpooled,y=ypooled) betapooled=out$par H=mnlHess(betapooled,ypooled,Xpooled) rootH=chol(H) for (i in 1:nlgt) { wgt=length(lgtdata[[i]]$y)/length(ypooled) out=optim(betapooled,llmnlFract,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-4), X=lgtdata[[i]]$X,y=lgtdata[[i]]$y,betapooled=betapooled,rootH=rootH,w=w,wgt=wgt) if(out$convergence == 0) { hess=mnlHess(out$par,lgtdata[[i]]$y,lgtdata[[i]]$X) lgtdata[[i]]=c(lgtdata[[i]],list(converge=1,betafmle=out$par,hess=hess)) } else { lgtdata[[i]]=c(lgtdata[[i]],list(converge=0,betafmle=c(rep(0,nvar)), hess=diag(nvar))) } oldbetas[i,]=lgtdata[[i]]$betafmle if(i%%50 ==0) cat(" completed unit #",i,fill=TRUE) fsh() } # # initialize values # # set initial values for the indicators # ind is of length(nlgt) and indicates which mixture component this obs # belongs to. # ind=NULL ninc=floor(nlgt/ncomp) for (i in 1:(ncomp-1)) {ind=c(ind,rep(i,ninc))} if(ncomp != 1) {ind = c(ind,rep(ncomp,nlgt-length(ind)))} else {ind=rep(1,nlgt)} # # initialize probs # oldprob=rep(1/ncomp,ncomp) # #initialize delta # if (drawdelta){ olddelta = rep(0,nz*nvar) } else { #send placeholders to the _loop function if there is no Z matrix olddelta = 0 Z = matrix(0) deltabar = 0 Ad = matrix(0) } ################################################################### # Wayne Taylor # 09/22/2014 ################################################################### draws = rhierMnlRwMixture_rcpp_loop(lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, as.matrix(olddelta), a, oldprob, oldbetas, ind) #################################################################### if(drawdelta){ attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep)} attributes(draws$betadraw)$class=c("bayesm.hcoef") attributes(draws$nmix)$class="bayesm.nmix" return(draws) }bayesm/R/clusterMix_rcpp.R0000644000176000001440000000437612524505401015301 0ustar ripleyusersclusterMix=function(zdraw,cutoff=.9,SILENT=FALSE,nprint=BayesmConstant.nprint){ # # # revision history: # written by p. rossi 9/05 # # purpose: cluster observations based on draws of indicators of # normal mixture components # # arguments: # zdraw is a R x nobs matrix of draws of indicators (typically output from rnmixGibbs) # the rth row of zdraw contains rth draw of indicators for each observations # each element of zdraw takes on up to p values for up to p groups. The maximum # number of groups is nobs. Typically, however, the number of groups will be small # and equal to the number of components used in the normal mixture fit. # # cutoff is a cutoff used in determining one clustering scheme it must be # a number between .5 and 1. # # nprint - print every nprint'th draw # # output: # two clustering schemes each with a vector of length nobs which gives the assignment # of each observation to a cluster # # clustera (finds zdraw with similarity matrix closest to posterior mean of similarity) # clusterb (finds clustering scheme by assigning ones if posterior mean of similarity matrix # > cutoff and computing associated z ) # # define needed functions # # ------------------------------------------------------------------------------------------ # # check arguments # if(missing(zdraw)) {pandterm("Requires zdraw argument -- R x n matrix of indicator draws")} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} # # check validity of zdraw rows -- must be integers in the range 1:nobs # nobs=ncol(zdraw) R=nrow(zdraw) if(sum(zdraw %in% (1:nobs)) < ncol(zdraw)*nrow(zdraw)) {pandterm("Bad zdraw argument -- all elements must be integers in 1:nobs")} cat("Table of zdraw values pooled over all rows",fill=TRUE) print(table(zdraw)) # # check validity of cuttoff if(cutoff > 1 || cutoff < .5) {pandterm(paste("cutoff invalid, = ",cutoff))} ################################################################### # Keunwoo Kim # 10/06/2014 ################################################################### out=clusterMix_rcpp_loop(zdraw, cutoff, SILENT, nprint) ################################################################### return(list(clustera=as.vector(out$clustera),clusterb=as.vector(out$clusterb))) }bayesm/R/mnlHess.R0000755000176000001440000000136010316323040013512 0ustar ripleyusersmnlHess = function(beta,y,X) { # p.rossi 2004 # changed argument order 9/05 # # Purpose: compute mnl -Expected[Hessian] # # Arguments: # beta is k vector of coefs # y is n vector with element = 1,...,j indicating which alt chosen # X is nj x k matrix of xvalues for each of j alt on each of n occasions # # Output: -Hess evaluated at beta # n=length(y) j=nrow(X)/n k=ncol(X) Xbeta=X%*%beta Xbeta=matrix(Xbeta,byrow=T,ncol=j) Xbeta=exp(Xbeta) iota=c(rep(1,j)) denom=Xbeta%*%iota Prob=Xbeta/as.vector(denom) Hess=matrix(double(k*k),ncol=k) for (i in 1:n) { p=as.vector(Prob[i,]) A=diag(p)-outer(p,p) Xt=X[(j*(i-1)+1):(j*i),] Hess=Hess+crossprod(Xt,A)%*%Xt } return(Hess) } bayesm/R/rivDP_rcpp.R0000644000176000001440000002222612524506070014163 0ustar ripleyusersrivDP = function(Data,Prior,Mcmc) { # # revision history: # P. Rossi 1/06 # added draw of alpha 2/06 # added automatic scaling 2/06 # removed reqfun 7/07 -- now functions are in rthetaDP # fixed initialization of theta 3/09 # fixed error in assigning user defined prior parms # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: # draw from posterior for linear I.V. model with DP process for errors # # Arguments: # Data -- list of z,w,x,y # y is vector of obs on lhs var in structural equation # x is "endogenous" var in structural eqn # w is matrix of obs on "exogenous" vars in the structural eqn # z is matrix of obs on instruments # Prior -- list of md,Ad,mbg,Abg,mubar,Amu,nuV # md is prior mean of delta # Ad is prior prec # mbg is prior mean vector for beta,gamma # Abg is prior prec of same # lamda is a list of prior parms for DP draw # mubar is prior mean of means for "errors" # Amu is scale precision parm for means # nu,V parms for IW on Sigma (idential priors for each normal comp # alpha prior parm for DP process (weight on base measure) # or starting value if there is a prior on alpha (requires element Prioralpha) # Prioralpha list of hyperparms for draw of alpha (alphamin,alphamax,power,n) # # Mcmc -- list of R,keep,starting values for delta,beta,gamma,theta # maxuniq is maximum number of unique theta values # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # SCALE if scale data, def: TRUE # gridsize is the gridsize parm for alpha draws # # Output: # list of draws of delta,beta,gamma and thetaNp1 which is used for # predictive distribution of errors (density estimation) # # Model: # # x=z'delta + e1 # y=beta*x + w'gamma + e2 # e1,e2 ~ N(theta_i) # # Priors # delta ~ N(md,Ad^-1) # vec(beta,gamma) ~ N(mbg,Abg^-1) # theta ~ DPP(alpha|lambda) # # # extract data and check dimensios # if(missing(Data)) {pandterm("Requires Data argument -- list of z,w,x,y")} if(is.null(Data$w)) isgamma=FALSE else isgamma=TRUE if(isgamma) w = Data$w #matrix if(is.null(Data$z)) {pandterm("Requires Data element z")} z=Data$z if(is.null(Data$x)) {pandterm("Requires Data element x")} x=as.vector(Data$x) if(is.null(Data$y)) {pandterm("Requires Data element y")} y=as.vector(Data$y) # # check data for validity # n=length(y) if(isgamma) {if(!is.matrix(w)) {pandterm("w is not a matrix")} dimg=ncol(w) if(n != nrow(w) ) {pandterm("length(y) ne nrow(w)")}} if(!is.matrix(z)) {pandterm("z is not a matrix")} dimd=ncol(z) if(n != length(x) ) {pandterm("length(y) ne length(x)")} if(n != nrow(z) ) {pandterm("length(y) ne nrow(z)")} # # extract elements corresponding to the prior # alimdef=BayesmConstant.DPalimdef nulimdef=BayesmConstant.DPnulimdef vlimdef=BayesmConstant.DPvlimdef if(missing(Prior)) { md=c(rep(0,dimd)) Ad=diag(BayesmConstant.A,dimd) if(isgamma) dimbg=1+dimg else dimbg=1 mbg=c(rep(0,dimbg)) Abg=diag(BayesmConstant.A,dimbg) gamma= BayesmConstant.gamma Istarmin=BayesmConstant.DPIstarmin alphamin=exp(digamma(Istarmin)-log(gamma+log(n))) Istarmax=floor(.1*n) alphamax=exp(digamma(Istarmax)-log(gamma+log(n))) power=BayesmConstant.DPpower Prioralpha=list(n=n,alphamin=alphamin,alphamax=alphamax,power=power) lambda_hyper=list(alim=alimdef,nulim=nulimdef,vlim=vlimdef) } else { if(is.null(Prior$md)) md=c(rep(0,dimd)) else md=Prior$md if(is.null(Prior$Ad)) Ad=diag(BayesmConstant.A,dimd) else Ad=Prior$Ad if(isgamma) dimbg=1+dimg else dimbg=1 if(is.null(Prior$mbg)) mbg=c(rep(0,dimbg)) else mbg=Prior$mbg if(is.null(Prior$Abg)) Abg=diag(BayesmConstant.A,dimbg) else Abg=Prior$Abg if(!is.null(Prior$Prioralpha)) {Prioralpha=Prior$Prioralpha} else {gamma= BayesmConstant.gamma Istarmin=BayesmConstant.DPIstarmin alphamin=exp(digamma(Istarmin)-log(gamma+log(n))) Istarmax=floor(.1*n) alphamax=exp(digamma(Istarmax)-log(gamma+log(n))) power=BayesmConstant.DPpower Prioralpha=list(n=n,alphamin=alphamin,alphamax=alphamax,power=power)} if(is.null(Prior$lambda_hyper)) {lambda_hyper=Prior$lambda_hyper} else {lambda_hyper=Prior$lambda_hyper; if(is.null(lambda_hyper$alim)) {lambda_hyper$alim=alimdef} if(is.null(lambda_hyper$nulim)) {lambda_hyper$nulim=nulimdef} if(is.null(lambda_hyper$vlim)) {lambda_hyper$vlim=vlimdef} } } # # check Prior arguments for valdity # if(lambda_hyper$alim[1]<0) {pandterm("alim[1] must be >0")} if(lambda_hyper$nulim[1]<0) {pandterm("nulim[1] must be >0")} if(lambda_hyper$vlim[1]<0) {pandterm("vlim[1] must be >0")} # # obtain starting values for MCMC # # we draw need inital values of delta, theta and indic # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} theta=NULL if(!is.null(Mcmc$delta)) {delta = Mcmc$delta} else {lmxz = lm(x~z,data.frame(x=x,z=z)) delta = lmxz$coef[2:(ncol(z)+1)]} if(!is.null(Mcmc$theta)) {theta=Mcmc$theta } else {onecomp=list(mu=c(0,0),rooti=diag(2)) theta=vector("list",length(y)) for(i in 1:n) {theta[[i]]=onecomp} } dimd = length(delta) if(is.null(Mcmc$maxuniq)) {maxuniq=BayesmConstant.DPmaxuniq} else {maxuniq=Mcmc$maxuniq} if(is.null(Mcmc$R)) {pandterm("requres Mcmc argument, R")} R = Mcmc$R if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$gridsize)) {gridsize=BayesmConstant.DPgridsize} else {gridsize=Mcmc$gridsize} if(is.null(Mcmc$SCALE)) {SCALE=BayesmConstant.DPSCALE} else {SCALE=Mcmc$SCALE} # # scale and center # if(SCALE){ scaley=sqrt(var(y)) scalex=sqrt(var(x)) meany=mean(y) meanx=mean(x) meanz=apply(z,2,mean) y=(y-meany)/scaley; x=(x-meanx)/scalex z=scale(z,center=TRUE,scale=FALSE) if(isgamma) {meanw=apply(w,2,mean); w=scale(w,center=TRUE,scale=FALSE)} } # # print out model # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for Linear IV Model With DP Process Errors",fill=TRUE) cat(" ",fill=TRUE) cat(" nobs= ",n,"; ",ncol(z)," instruments",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("mean of delta ",fill=TRUE) print(md) cat(" ",fill=TRUE) cat("Adelta",fill=TRUE) print(Ad) cat(" ",fill=TRUE) cat("mean of beta/gamma",fill=TRUE) print(mbg) cat(" ",fill=TRUE) cat("Abeta/gamma",fill=TRUE) print(Abg) cat(" ",fill=TRUE) cat("G0 ~ N(mubar,Sigma (x) Amu^-1)",fill=TRUE) cat(" mubar = ",0,fill=TRUE) cat(" Sigma ~ IW(nu,nu*v*I)",fill=TRUE) cat(" Amu ~ uniform[",lambda_hyper$alim[1],",",lambda_hyper$alim[2],"]",fill=TRUE) cat(" nu ~ uniform on log grid [",2-1+exp(lambda_hyper$nulim[1]), ",",2-1+exp(lambda_hyper$nulim[2]),"]",fill=TRUE) cat(" v ~ uniform[",lambda_hyper$vlim[1],",",lambda_hyper$vlim[2],"]",fill=TRUE) cat(" ",fill=TRUE) cat("Parameters of Prior on Dirichlet Process parm (alpha)",fill=TRUE) cat("alphamin= ",Prioralpha$alphamin," alphamax= ",Prioralpha$alphamax," power=", Prioralpha$power,fill=TRUE) cat("alpha values correspond to Istarmin = ",Istarmin," Istarmax = ",Istarmax,fill=TRUE) cat(" ",fill=TRUE) cat("MCMC parms: R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" maximum number of unique thetas= ",maxuniq,fill=TRUE) cat(" gridsize for alpha draws= ",gridsize,fill=TRUE) cat(" SCALE data= ",SCALE,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 3/14/2015 ################################################################### if(isgamma == FALSE) w=matrix() out = rivDP_rcpp_loop(R,keep,nprint,dimd,mbg,Abg,md,Ad,y,isgamma,z,x,w, delta,PrioralphaList=Prioralpha,gridsize,SCALE,maxuniq,scalex,scaley,lambda_hyper, BayesmConstant.A,BayesmConstant.nu) ################################################################### nmix=list(probdraw=matrix(c(rep(1,length(out$thetaNp1draw))),ncol=1),zdraw=NULL,compdraw=out$thetaNp1draw) # # densitymix is in the format to be used with the generic mixture of normals plotting # methods (plot.bayesm.nmix) # attributes(nmix)$class=c("bayesm.nmix") attributes(out$deltadraw)$class=c("bayesm.mat","mcmc") attributes(out$deltadraw)$mcpar=c(1,R,keep) attributes(out$betadraw)$class=c("bayesm.mat","mcmc") attributes(out$betadraw)$mcpar=c(1,R,keep) attributes(out$alphadraw)$class=c("bayesm.mat","mcmc") attributes(out$alphadraw)$mcpar=c(1,R,keep) attributes(out$Istardraw)$class=c("bayesm.mat","mcmc") attributes(out$Istardraw)$mcpar=c(1,R,keep) if(isgamma){ attributes(out$gammadraw)$class=c("bayesm.mat","mcmc") attributes(out$gammadraw)$mcpar=c(1,R,keep)} if(isgamma) { return(list(deltadraw=out$deltadraw,betadraw=out$betadraw,alphadraw=out$alphadraw,Istardraw=out$Istardraw, gammadraw=out$gammadraw,nmix=nmix))} else { return(list(deltadraw=out$deltadraw,betadraw=out$betadraw,alphadraw=out$alphadraw,Istardraw=out$Istardraw, nmix=nmix))} }bayesm/MD50000644000176000001440000002130012541204511012061 0ustar ripleyusers5e6b8ff59c55ec888fc23256f6a5edc5 *DESCRIPTION ea2da2fb3b1d8a3583ca145c403bfcbf *NAMESPACE ea81ddf03297bdab18de68ef0fce3434 *R/BayesmConstants.R 750237445e7fb47a0c72db9038bad9b3 *R/BayesmFunctions.R 5f045fe686e9700b1e69e17eb2cd1a4b *R/clusterMix_rcpp.R 0207b70adcd95129ba38b6177b235d51 *R/condMom.R 7e3f01b37b79b1cb694dcc89bef6801b *R/createX.R f0ab1f89e4c7b93a3abf2969c94b4b19 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http://www.perossi.org/home/bsm-1 Description: Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014). NeedsCompilation: yes Packaged: 2015-06-20 06:26:44 UTC; ripley Repository: CRAN Date/Publication: 2015-06-20 08:33:45 bayesm/man/0000755000176000001440000000000012537615432012345 5ustar ripleyusersbayesm/man/simnhlogit.Rd0000755000176000001440000000257012524672205015015 0ustar ripleyusers\name{simnhlogit} \alias{simnhlogit} \concept{logit} \concept{non-homothetic} \title{ Simulate from Non-homothetic Logit Model } \description{ \code{simnhlogit} simulates from the non-homothetic logit model } \usage{ simnhlogit(theta, lnprices, Xexpend) } \arguments{ \item{theta}{ coefficient vector } \item{lnprices}{ n x p array of prices } \item{Xexpend}{ n x k array of values of expenditure variables} } \details{ For details on parameterization, see \code{llnhlogit}. } \value{ a list containing: \item{y}{n x 1 vector of multinomial outcomes (1, \ldots, p)} \item{Xexpend}{expenditure variables} \item{lnprices}{ price array } \item{theta}{coefficients} \item{prob}{n x p array of choice probabilities} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 4. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{llnhlogit}} } \examples{ ## N=1000 p=3 k=1 theta = c(rep(1,p),seq(from=-1,to=1,length=p),rep(2,k),.5) lnprices = matrix(runif(N*p),ncol=p) Xexpend = matrix(runif(N*k),ncol=k) simdata = simnhlogit(theta,lnprices,Xexpend) } \keyword{ models } bayesm/man/rmnlIndepMetrop.Rd0000644000176000001440000000531012536435674015761 0ustar ripleyusers\name{rmnlIndepMetrop} \alias{rmnlIndepMetrop} \concept{MCMC} \concept{multinomial logit} \concept{Metropolis algorithm} \concept{bayes} \title{ MCMC Algorithm for Multinomial Logit Model } \description{ \code{rmnIndepMetrop} implements Independence Metropolis for the MNL. } \usage{ rmnlIndepMetrop(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(p,y,X)} \item{Prior}{ list(A,betabar) optional} \item{Mcmc}{ list(R,keep,nprint,nu) } } \details{ Model: y \eqn{\sim}{~} MNL(X,\eqn{\beta}). \eqn{\Pr(y=j) = exp(x_j'\beta)/\sum_k{e^{x_k'\beta}}}. \cr Prior: \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})} \cr list arguments contain: \itemize{ \item{\code{p}}{ number of alternatives} \item{\code{y}}{ nobs vector of multinomial outcomes (1,\ldots, p)} \item{\code{X}}{ nobs*p x nvar matrix} \item{\code{A}}{ nvar x nvar pds prior prec matrix (def: .01I)} \item{\code{betabar}}{ nvar x 1 prior mean (def: 0)} \item{\code{R}}{ number of MCMC draws} \item{\code{keep}}{ MCMC thinning parm: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} \item{\code{nu}}{ degrees of freedom parameter for independence t density (def: 6) } } } \value{ a list containing: \item{betadraw}{R/keep x nvar array of beta draws} \item{loglike}{R/keep vector of loglike values for each draw} \item{acceptr}{acceptance rate of Metropolis draws} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 5. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rhierMnlRwMixture}} } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) n=200; p=3; beta=c(1,-1,1.5,.5) simmnl= function(p,n,beta) { # note: create X array with 2 alt.spec vars k=length(beta) X1=matrix(runif(n*p,min=-1,max=1),ncol=p) X2=matrix(runif(n*p,min=-1,max=1),ncol=p) X=createX(p,na=2,nd=NULL,Xd=NULL,Xa=cbind(X1,X2),base=1) Xbeta=X\%*\%beta # now do probs p=nrow(Xbeta)/n Xbeta=matrix(Xbeta,byrow=TRUE,ncol=p) Prob=exp(Xbeta) iota=c(rep(1,p)) denom=Prob\%*\%iota Prob=Prob/as.vector(denom) # draw y y=vector("double",n) ind=1:p for (i in 1:n) { yvec=rmultinom(1,1,Prob[i,]); y[i]=ind\%*\%yvec } return(list(y=y,X=X,beta=beta,prob=Prob)) } simout=simmnl(p,n,beta) Data1=list(y=simout$y,X=simout$X,p=p); Mcmc1=list(R=R,keep=1) out=rmnlIndepMetrop(Data=Data1,Mcmc=Mcmc1) cat("Summary of beta draws",fill=TRUE) summary(out$betadraw,tvalues=beta) if(0){ ## plotting examples plot(out$betadraw) } } \keyword{ models } bayesm/man/summary.bayesm.mat.Rd0000644000176000001440000000370712523217715016374 0ustar ripleyusers\name{summary.bayesm.mat} \alias{summary.bayesm.mat} \title{Summarize Mcmc Parameter Draws } \description{ \code{summary.bayesm.mat} is an S3 method to summarize marginal distributions given an array of draws } \usage{ \method{summary}{bayesm.mat}(object, names, burnin = trunc(0.1 * nrow(X)), tvalues, QUANTILES = TRUE, TRAILER = TRUE,...) } \arguments{ \item{object}{ \code{object} (hereafter \code{X}) is an array of draws, usually an object of class "bayesm.mat" } \item{names}{ optional character vector of names for the columns of \code{X}} \item{burnin}{ number of draws to burn-in (def: .1*nrow(X))} \item{tvalues}{ optional vector of "true" values for use in simulation examples } \item{QUANTILES}{ logical for should quantiles be displayed (def: TRUE)} \item{TRAILER}{ logical for should a trailer be displayed (def: TRUE)} \item{...}{ optional arguments for generic function } } \details{ Typically, \code{summary.bayesm.nmix} will be invoked by a call to the generic summary function as in \code{summary(object)} where object is of class bayesm.mat. Mean, Std Dev, Numerical Standard error (of estimate of posterior mean), relative numerical efficiency (see \code{numEff}) and effective sample size are displayed. If QUANTILES=TRUE, quantiles of marginal distirbutions in the columns of X are displayed. \cr \cr \code{summary.bayesm.mat} is also exported for direct use as a standard function, as in \code{summary.bayesm.mat(matrix)}. \cr \code{summary.bayesm.mat(matrix)} returns (invisibly) the array of the various summary statistics for further use. To assess this array use\code{stats=summary(Drawmat)}. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{summary.bayesm.var}}, \code{\link{summary.bayesm.nmix}}} \examples{ ## ## not run # out=rmnpGibbs(Data,Prior,Mcmc) # summary(out$betadraw) # } \keyword{ univar } bayesm/man/ghkvec.Rd0000755000176000001440000000600312536133223014075 0ustar ripleyusers\name{ghkvec} \alias{ghkvec} \concept{multivariate normal distribution} \concept{GHK method} \concept{integral} \title{ Compute GHK approximation to Multivariate Normal Integrals } \description{ \code{ghkvec} computes the GHK approximation to the integral of a multivariate normal density over a half plane defined by a set of truncation points. } \usage{ ghkvec(L, trunpt, above, r, HALTON=TRUE, pn) } \arguments{ \item{L}{ lower triangular Cholesky root of covariance matrix } \item{trunpt}{ vector of truncation points} \item{above}{ vector of indicators for truncation above(1) or below(0) } \item{r}{ number of draws to use in GHK } \item{HALTON}{ if TRUE, use Halton sequence. If FALSE, use R::runif random number generator (optional / def: TRUE)} \item{pn}{ prime number used for Halton sequence (optional / def: the smallest prime numbers, i.e. 2, 3, 5, ...)} } \value{ approximation to integral } \note{ \code{ghkvec} can accept a vector of truncations and compute more than one integral. That is, length(trunpt)/length(above) number of different integrals, each with the same Sigma and mean 0 but different truncation points. See example below for an example with two integrals at different truncation points. \cr User can choose what random number to use for the numerical integration: psuedo-random numbers by \code{R::runif} or quasi-random numbers by Halton sequence. Generally, the quasi-random sequence (e.g., Halton) is more uniformly distributed within domain, so it shows lower error and improved convergence than the psuedo-random sequence (Morokoff and Caflisch, 1995). \cr For the prime numbers generating Halton sequence, we suggest to use the first smallest prime numbers. Halton (1960) and Kocis and Whiten (1997) prove that their discrepancy measures (how uniformly the sample points are distributed) have the upper bounds, which decrease as the generating prime number decreases. \cr Note: For a high dimensional integration (10 or more dimension), we suggest to use the psuedo-random number generator (\code{R::runif}). According to Kocis and Whiten (1997), Halton sequences may be highly correlated when the dimension is 10 or more. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} \cr For Halton sequence, see Halton (1960, Numerische Mathematik), Morokoff and Caflisch (1995, Journal of Computational Physics), and Kocis and Whiten (1997, ACM Transactions on Mathematical Software). } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.\cr Keunwoo Kim, Anderson School, UCLA, \email{keunwoo.kim@gmail.com} } \examples{ Sigma=matrix(c(1,.5,.5,1),ncol=2) L=t(chol(Sigma)) trunpt=c(0,0,1,1) above=c(1,1) # drawn by Halton sequence ghkvec(L,trunpt,above,r=100) # use prime number 11 and 13 ghkvec(L,trunpt,above,r=100,HALTON=TRUE,pn=c(11,13)) # drawn by R::runif ghkvec(L,trunpt,above,r=100,HALTON=FALSE) } \keyword{ distribution } bayesm/man/clusterMix.Rd0000644000176000001440000000505512524765041014776 0ustar ripleyusers\name{clusterMix} \alias{clusterMix} \concept{normal mixture} \concept{clustering} \title{ Cluster Observations Based on Indicator MCMC Draws } \description{ \code{clusterMix} uses MCMC draws of indicator variables from a normal component mixture model to cluster observations based on a similarity matrix. } \usage{ clusterMix(zdraw, cutoff = 0.9, SILENT = FALSE, nprint = BayesmConstant.nprint) } \arguments{ \item{zdraw}{ R x nobs array of draws of indicators } \item{cutoff}{ cutoff probability for similarity (def: .9)} \item{SILENT}{ logical flag for silent operation (def: FALSE) } \item{nprint}{ print every nprint'th draw (def: 100) } } \details{ Define a similarity matrix, Sim, Sim[i,j]=1 if observations i and j are in same component. Compute the posterior mean of Sim over indicator draws. Clustering is achieved by two means: Method A: Find the indicator draw whose similarity matrix minimizes, loss(E[Sim]-Sim(z)), where loss is absolute deviation. Method B: Define a Similarity matrix by setting any element of E[Sim] = 1 if E[Sim] > cutoff. Compute the clustering scheme associated with this "windsorized" Similarity matrix. } \value{ \item{clustera}{indicator function for clustering based on method A above} \item{clusterb}{indicator function for clustering based on method B above} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, . } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rnmixGibbs}} } \keyword{ models } \keyword{ multivariate } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) { ## simulate data from mixture of normals n=500 pvec=c(.5,.5) mu1=c(2,2) mu2=c(-2,-2) Sigma1=matrix(c(1,.5,.5,1),ncol=2) Sigma2=matrix(c(1,.5,.5,1),ncol=2) comps=NULL comps[[1]]=list(mu1,backsolve(chol(Sigma1),diag(2))) comps[[2]]=list(mu2,backsolve(chol(Sigma2),diag(2))) dm=rmixture(n,pvec,comps) ## run MCMC on normal mixture R=2000 Data=list(y=dm$x) ncomp=2 Prior=list(ncomp=ncomp,a=c(rep(100,ncomp))) Mcmc=list(R=R,keep=1) out=rnmixGibbs(Data=Data,Prior=Prior,Mcmc=Mcmc) begin=500 end=R ## find clusters outclusterMix=clusterMix(out$nmix$zdraw[begin:end,]) ## ## check on clustering versus "truth" ## note: there could be switched labels ## table(outclusterMix$clustera,dm$z) table(outclusterMix$clusterb,dm$z) } ## } bayesm/man/mixDenBi.Rd0000644000176000001440000000307412523207436014333 0ustar ripleyusers\name{mixDenBi} \alias{mixDenBi} \concept{normal mixture} \concept{marginal distribution} \concept{density} \title{ Compute Bivariate Marginal Density for a Normal Mixture } \description{ \code{mixDenBi} computes the implied bivariate marginal density from a mixture of normals with specified mixture probabilities and component parameters. } \usage{ mixDenBi(i, j, xi, xj, pvec, comps) } \arguments{ \item{i}{ index of first variable } \item{j}{ index of second variable } \item{xi}{ grid of values of first variable } \item{xj}{ grid of values of second variable } \item{pvec}{ normal mixture probabilities } \item{comps}{ list of lists of components } } \details{ length(comps) is the number of mixture components. comps[[j]] is a list of parameters of the jth component. comps[[j]]$mu is mean vector; comps[[j]]$rooti is the UL decomp of \eqn{\Sigma^{-1}}. } \value{ an array (length(xi)=length(xj) x 2) with density value } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rnmixGibbs}}, \code{\link{mixDen}} } \examples{ \dontrun{ ## ## see examples in rnmixGibbs documentation ## } } \keyword{ models } \keyword{ multivariate } bayesm/man/rbayesBLP.Rd0000644000176000001440000002232012537614443014457 0ustar ripleyusers\name{rbayesBLP} \alias{rbayesBLP} \concept{bayes} \concept{random coefficient logit} \concept{BLP} \concept{Metropolis Hasting} \title{ Bayesian Analysis of Random Coefficient Logit Models Using Aggregate Data } \description{ \code{rbayesBLP} implements a hybrid MCMC algorithm for aggregate level sales data in a market with differentiated products. Version 3.0-1 contains an error for use of instruments with this function. This will be fixed in version 3.0-2. } \usage{ rbayesBLP(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(X,share,J,Z) (X, share, and J: required). } \item{Prior}{ list(sigmasqR,theta_hat,A,deltabar,Ad,nu0,s0_sq,VOmega) (optional).} \item{Mcmc}{ list(R,H,initial_theta_bar,initial_r,initial_tau_sq,initial_Omega,initial_delta,s,cand_cov,tol,keep,nprint) (R and H: required).} } \details{ Model: \cr \eqn{u_ijt = X_jt \theta_i + \eta_jt + e_ijt}\cr \eqn{e_ijt} \eqn{\sim}{~} type I Extreme Value (logit)\cr \eqn{\theta_i} \eqn{\sim}{~} \eqn{N(\theta_bar, \Sigma)}\cr \eqn{\eta_jt} \eqn{\sim}{~} \eqn{N(0, \tau_sq)}\cr This structure implies a logit model for each consumer (\eqn{\theta}). Aggregate shares \code{share} are produced by integrating this consumer level logit model over the assumed normal distribution of \eqn{\theta}. Priors:\cr \eqn{r} \eqn{\sim}{~} \eqn{N(0,diag(sigmasqR))}.\cr \eqn{\theta_bar} \eqn{\sim}{~} \eqn{N(\theta_hat,A^-1)}.\cr \eqn{\tau_sq} \eqn{\sim}{~} \eqn{nu0*s0_sq / \chi^2 (nu0)}\cr Note: we observe the aggregate level market share, not individual level choice.\cr Note: \eqn{r} is the vector of nonzero elements of cholesky root of \eqn{\Sigma}. Instead of \eqn{\Sigma} we draw \eqn{r}, which is one-to-one correspondence with the positive-definite \eqn{\Sigma}. Model (with IV): \cr \eqn{u_ijt = X_jt \theta_i + \eta_jt + e_ijt}\cr \eqn{e_ijt} \eqn{\sim}{~} type I Extreme Value (logit)\cr \eqn{\theta_i} \eqn{\sim}{~} \eqn{N(\theta_bar, \Sigma)}\cr \eqn{X_jt = [X_exo_jt, X_endo_jt]}\cr \eqn{X_endo_jt = Z_jt \delta_jt + \zeta_jt}\cr \eqn{vec(\zeta_jt, \eta_jt)} \eqn{\sim}{~} \eqn{N(0, \Omega)}\cr Priors (with IV):\cr \eqn{r} \eqn{\sim}{~} \eqn{N(0,diag(sigmasqR))}.\cr \eqn{\theta_bar} \eqn{\sim}{~} \eqn{N(\theta_hat,A^-1)}.\cr \eqn{\delta} \eqn{\sim}{~} \eqn{N(deltabar,Ad^-1)}.\cr \eqn{\Omega} \eqn{\sim}{~} \eqn{IW(nu0, VOmega)}\cr Step 1 (\eqn{\Sigma}):\cr Given \eqn{\theta_bar} and \eqn{\tau_sq}, draw \eqn{r} via Metropolis-Hasting.\cr Covert the drawn \eqn{r} to \eqn{\Sigma}.\cr Note: if user does not specify the Metropolis-Hasting increment parameters (\code{s} and \code{cand_cov}), \code{rbayesBLP} automatically tunes the parameters. Step 2 (\eqn{\theta_bar}, \eqn{\tau_sq}):\cr Given \eqn{\Sigma}, draw \eqn{\theta_bar} and \eqn{\tau_sq} via Gibbs sampler.\cr Step 2 (with IV: \eqn{\theta_bar}, \eqn{\delta}, \eqn{\Omega}):\cr Given \eqn{\Sigma}, draw \eqn{\theta_bar}, \eqn{\delta}, and \eqn{\Omega} via IV Gibbs sampler.\cr List arguments contain:\cr Data \itemize{ \item{\code{J}}{ number of alternatives without outside option} \item{\code{X}}{ J*T by K matrix (no outside option, which is normalized to 0). If IV is used, the last column is endogeneous variable.} \item{\code{share}}{ J*T vector (no outside option)} \item{\code{Z}}{ J*T by I matrix of instrumental variables (optional)} } Note: both the \code{share} vector and the \code{X} matrix are organized by the jt index. j varies faster than t, i.e. (j=1,t=1),(j=2,t=1), ..., (j=J,T=1), ..., (j=J,t=T)\cr Prior \itemize{ \item{\code{sigmasqR}}{ K*(K+1)/2 vector for \eqn{r} prior variance (def: diffuse prior for \eqn{\Sigma})} \item{\code{theta_hat}}{ K vector for \eqn{\theta_bar} prior mean (def: 0 vector)} \item{\code{A}}{ K by K matrix for \eqn{\theta_bar} prior precision (def: 0.01*diag(K))} \item{\code{deltabar}}{ I vector for \eqn{\delta} prior mean (def: 0 vector)} \item{\code{Ad}}{ I by I matrix for \eqn{\delta} prior precision (def: 0.01*diag(I))} \item{\code{nu0}}{ d.f. parameter for \eqn{\tau_sq} and \eqn{\Omega} prior (def: K+1)} \item{\code{s0_sq}}{ scale parameter for \eqn{\tau_sq} prior (def: 1)} \item{\code{VOmega}}{ 2 by 2 matrix parameter for \eqn{\Omega} prior (def: matrix(c(1,0.5,0.5,1),2,2))} } Mcmc \itemize{ \item{\code{R}}{ number of MCMC draws} \item{\code{H}}{ number of random draws used for Monte-Carlo integration} \item{\code{initial_theta_bar}}{ initial value of \eqn{\theta_bar} (def: 0 vector)} \item{\code{initial_r}}{ initial value of \eqn{r} (def: 0 vector)} \item{\code{initial_tau_sq}}{ initial value of \eqn{\tau_sq} (def: 0.1)} \item{\code{initial_Omega}}{ initial value of \eqn{\Omega} (def: diag(2))} \item{\code{initial_delta}}{ initial value of \eqn{\delta} (def: 0 vector)} \item{\code{s}}{ scale parameter of Metropolis-Hasting increment (def: automatically tuned)} \item{\code{cand_cov}}{ var-cov matrix of Metropolis-Hasting increment (def: automatically tuned)} \item{\code{tol}}{ convergence tolerance for the contraction mapping (def: 1e-6)} \item{\code{keep}}{ MCMC thinning parameter: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } Tuning Metropolis-Hastings algorithm:\cr r_cand = r_old + s*N(0,cand_cov)\cr Fix the candidate covariance matrix as cand_cov0 = diag(rep(0.1, K), rep(1, K*(K-1)/2)).\cr Start from s0 = 2.38/sqrt(dim(r))\cr Repeat\{\cr Run 500 MCMC chain.\cr If acceptance rate < 30\% => update s1 = s0/5.\cr If acceptance rate > 50\% => update s1 = s0*3.\cr (Store r draws if acceptance rate is 20~80\%.)\cr s0 = s1\cr \} until acceptance rate is 30~50\% Scale matrix C = s1*sqrt(cand_cov0)\cr Correlation matrix R = Corr(r draws)\cr Use C*R*C as s^2*cand_cov.\cr } \value{ a list containing \item{thetabardraw}{K by R/keep matrix of random coefficient mean draws} \item{Sigmadraw}{K*K by R/keep matrix of random coefficient variance draws} \item{rdraw}{K*K by R/keep matrix of \eqn{r} draws (same information as in \code{Sigmadraw})} \item{tausqdraw}{R/keep vector of aggregate demand shock variance draws} \item{Omegadraw}{2*2 by R/keep matrix of correlated endogenous shock variance draws} \item{deltadraw}{I by R/keep matrix of endogenous structural equation coefficient draws} \item{acceptrate}{scalor of acceptance rate of Metropolis-Hasting} \item{s}{scale parameter used for Metropolis-Hasting} \item{cand_cov}{var-cov matrix used for Metropolis-Hasting} } \references{ For further discussion, see \emph{Bayesian Analysis of Random Coefficient Logit Models Using Aggregate Data} by Jiang, Manchanda and Rossi, Journal of Econometrics, 2009. \cr \url{http://www.sciencedirect.com/science/article/pii/S0304407608002297} } \author{ Keunwoo Kim, Anderson School, UCLA, \email{keunwoo.kim@gmail.com}. } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) { ### ### Simulate aggregate level data ### simulData <- function(para, others, Hbatch){ # # Keunwoo Kim, UCLA Anderson # ### parameters theta_bar <- para$theta_bar Sigma <- para$Sigma tau_sq <- para$tau_sq T <- others$T J <- others$J p <- others$p H <- others$H K <- J + p # Hbatch does the integration for computing market shares in batches of # size Hbatch ### build X X <- matrix(runif(T*J*p), T*J, p) inter <- NULL for (t in 1:T){ inter <- rbind(inter, diag(J)) } X <- cbind(inter, X) ### draw eta ~ N(0, tau_sq) eta <- rnorm(T*J)*sqrt(tau_sq) X <- cbind(X, eta) share <- rep(0, J*T) for (HH in 1:(H/Hbatch)){ ### draw theta ~ N(theta_bar, Sigma) cho <- chol(Sigma) theta <- matrix(rnorm(K*Hbatch), nrow=K, ncol=Hbatch) theta <- t(cho)\%*\%theta + theta_bar ### utility V <- X\%*\%rbind(theta, 1) expV <- exp(V) expSum <- matrix(colSums(matrix(expV, J, T*Hbatch)), T, Hbatch) expSum <- expSum \%x\% matrix(1, J, 1) choiceProb <- expV / (1 + expSum) share <- share + rowSums(choiceProb) / H } ### the last K+1'th column is eta, which is unobservable. X <- X[,c(1:K)] return (list(X=X, share=share)) } ### true parameter theta_bar_true <- c(-2, -3, -4, -5) Sigma_true <- rbind(c(3,2,1.5,1),c(2,4,-1,1.5),c(1.5,-1,4,-0.5),c(1,1.5,-0.5,3)) cho <- chol(Sigma_true) r_true <- c(log(diag(cho)),cho[1,2:4],cho[2,3:4],cho[3,4]) tau_sq_true <- 1 ### simulate data set.seed(66) T <- 300;J <- 3;p <- 1;K <- 4;H <- 1000000;Hbatch <- 5000 dat <- simulData(para=list(theta_bar=theta_bar_true, Sigma=Sigma_true, tau_sq=tau_sq_true), others=list(T=T, J=J, p=p, H=H), Hbatch) X <- dat$X share <- dat$share ### Mcmc run R <- 2000;H <- 50 Data1 <- list(X=X, share=share, J=J) Mcmc1 <- list(R=R, H=H, nprint=0) set.seed(66) out <- rbayesBLP(Data=Data1, Mcmc=Mcmc1) ### acceptance rate out$acceptrate ### summary of draws summary(out$thetabardraw) summary(out$Sigmadraw) summary(out$tausqdraw) ### plotting draws plot(out$thetabardraw) plot(out$Sigmadraw) plot(out$tausqdraw) } } bayesm/man/rnegbinRw.Rd0000644000176000001440000000707712523216163014575 0ustar ripleyusers\name{rnegbinRw} \alias{rnegbinRw} \concept{MCMC} \concept{NBD regression} \concept{Negative Binomial regression} \concept{Poisson regression} \concept{Metropolis algorithm} \concept{bayes} \title{ MCMC Algorithm for Negative Binomial Regression } \description{ \code{rnegbinRw} implements a Random Walk Metropolis Algorithm for the Negative Binomial (NBD) regression model. beta | alpha and alpha | beta are drawn with two different random walks. } \usage{ rnegbinRw(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(y,X) } \item{Prior}{ list(betabar,A,a,b) } \item{Mcmc}{ list(R,keep,s_beta,s_alpha,beta0 } } \details{ Model: \eqn{y} \eqn{\sim}{~} \eqn{NBD(mean=\lambda, over-dispersion=alpha)}. \cr \eqn{\lambda=exp(x'\beta)} Prior: \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})} \cr \eqn{alpha} \eqn{\sim}{~} \eqn{Gamma(a,b)}. \cr note: prior mean of \eqn{alpha = a/b}, \eqn{variance = a/(b^2)} list arguments contain: \itemize{ \item{\code{y}}{ nobs vector of counts (0,1,2,\ldots)} \item{\code{X}}{nobs x nvar matrix} \item{\code{betabar}}{ nvar x 1 prior mean (def: 0)} \item{\code{A}}{ nvar x nvar pds prior prec matrix (def: .01I)} \item{\code{a}}{ Gamma prior parm (def: .5)} \item{\code{b}}{ Gamma prior parm (def: .1)} \item{\code{R}}{ number of MCMC draws} \item{\code{keep}}{ MCMC thinning parm: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} \item{\code{s_beta}}{ scaling for beta| alpha RW inc cov matrix (def: 2.93/sqrt(nvar))} \item{\code{s_alpha}}{ scaling for alpha | beta RW inc cov matrix (def: 2.93)} } } \value{ a list containing: \item{betadraw}{R/keep x nvar array of beta draws} \item{alphadraw}{R/keep vector of alpha draws} \item{llike}{R/keep vector of log-likelihood values evaluated at each draw} \item{acceptrbeta}{acceptance rate of the beta draws} \item{acceptralpha}{acceptance rate of the alpha draws} } \note{ The NBD regression encompasses Poisson regression in the sense that as alpha goes to infinity the NBD distribution tends toward the Poisson.\cr For "small" values of alpha, the dependent variable can be extremely variable so that a large number of observations may be required to obtain precise inferences. } \seealso{ \code{\link{rhierNegbinRw}} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Sridhar Narayanam & Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10} set.seed(66) simnegbin = function(X, beta, alpha) { # Simulate from the Negative Binomial Regression lambda = exp(X \%*\% beta) y=NULL for (j in 1:length(lambda)) y = c(y,rnbinom(1,mu = lambda[j],size = alpha)) return(y) } nobs = 500 nvar=2 # Number of X variables alpha = 5 Vbeta = diag(nvar)*0.01 # Construct the regdata (containing X) simnegbindata = NULL beta = c(0.6,0.2) X = cbind(rep(1,nobs),rnorm(nobs,mean=2,sd=0.5)) simnegbindata = list(y=simnegbin(X,beta,alpha), X=X, beta=beta) Data1 = simnegbindata Mcmc1 = list(R=R) out = rnegbinRw(Data=Data1,Mcmc=Mcmc1) cat("Summary of alpha/beta draw",fill=TRUE) summary(out$alphadraw,tvalues=alpha) summary(out$betadraw,tvalues=beta) if(0){ ## plotting examples plot(out$betadraw) } } \keyword{ models } bayesm/man/rhierNegbinRw.Rd0000644000176000001440000001200712536144134015374 0ustar ripleyusers\name{rhierNegbinRw} \alias{rhierNegbinRw} \concept{MCMC} \concept{hierarchical NBD regression} \concept{Negative Binomial regression} \concept{Poisson regression} \concept{Metropolis algorithm} \concept{bayes} \title{ MCMC Algorithm for Negative Binomial Regression } \description{ \code{rhierNegbinRw} implements an MCMC strategy for the hierarchical Negative Binomial (NBD) regression model. Metropolis steps for each unit level set of regression parameters are automatically tuned by optimization. Over-dispersion parameter (alpha) is common across units. } \usage{ rhierNegbinRw(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(regdata,Z) } \item{Prior}{ list(Deltabar,Adelta,nu,V,a,b) } \item{Mcmc}{ list(R,keep,nprint,s_beta,s_alpha,c,Vbeta0,Delta0) } } \details{ Model: \eqn{y_i} \eqn{\sim}{~} NBD(mean=\eqn{\lambda}, over-dispersion=alpha). \cr \eqn{\lambda=exp(X_i\beta_i)} Prior: \eqn{\beta_i} \eqn{\sim}{~} \eqn{N(\Delta'z_i,Vbeta)}. \eqn{vec(\Delta|Vbeta)} \eqn{\sim}{~} \eqn{N(vec(Deltabar),Vbeta (x) Adelta)}. \cr \eqn{Vbeta} \eqn{\sim}{~} \eqn{IW(nu,V)}. \cr \eqn{alpha} \eqn{\sim}{~} \eqn{Gamma(a,b)}. \cr note: prior mean of \eqn{alpha = a/b}, \eqn{variance = a/(b^2)} list arguments contain: \itemize{ \item{\code{regdata}}{ list of lists with data on each of nreg units} \item{\code{regdata[[i]]$X}}{ nobs\_i x nvar matrix of X variables} \item{\code{regdata[[i]]$y}}{ nobs\_i x 1 vector of count responses} \item{\code{Z}}{nreg x nz mat of unit chars (def: vector of ones)} \item{\code{Deltabar}}{ nz x nvar prior mean matrix (def: 0)} \item{\code{Adelta}}{ nz x nz pds prior prec matrix (def: .01I)} \item{\code{nu}}{ d.f. parm for IWishart (def: nvar+3)} \item{\code{V}}{location matrix of IWishart prior (def: nuI)} \item{\code{a}}{ Gamma prior parm (def: .5)} \item{\code{b}}{ Gamma prior parm (def: .1)} \item{\code{R}}{ number of MCMC draws} \item{\code{keep}}{ MCMC thinning parm: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} \item{\code{s_beta}}{ scaling for beta| alpha RW inc cov (def: 2.93/sqrt(nvar))} \item{\code{s_alpha}}{ scaling for alpha | beta RW inc cov (def: 2.93)} \item{\code{c}}{ fractional likelihood weighting parm (def:2)} \item{\code{Vbeta0}}{ starting value for Vbeta (def: I)} \item{\code{Delta0}}{ starting value for Delta (def: 0)} } } \value{ a list containing: \item{llike}{R/keep vector of values of log-likelihood} \item{betadraw}{nreg x nvar x R/keep array of beta draws} \item{alphadraw}{R/keep vector of alpha draws} \item{acceptrbeta}{acceptance rate of the beta draws} \item{acceptralpha}{acceptance rate of the alpha draws} } \note{ The NBD regression encompasses Poisson regression in the sense that as alpha goes to infinity the NBD distribution tends to the Poisson.\cr For "small" values of alpha, the dependent variable can be extremely variable so that a large number of observations may be required to obtain precise inferences. For ease of interpretation, we recommend demeaning Z variables. } \seealso{ \code{\link{rnegbinRw}} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 5. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Sridhar Narayanam & Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} ## set.seed(66) simnegbin = function(X, beta, alpha) { # Simulate from the Negative Binomial Regression lambda = exp(X \%*\% beta) y=NULL for (j in 1:length(lambda)) y = c(y,rnbinom(1,mu = lambda[j],size = alpha)) return(y) } nreg = 100 # Number of cross sectional units T = 50 # Number of observations per unit nobs = nreg*T nvar=2 # Number of X variables nz=2 # Number of Z variables # Construct the Z matrix Z = cbind(rep(1,nreg),rnorm(nreg,mean=1,sd=0.125)) Delta = cbind(c(4,2), c(0.1,-1)) alpha = 5 Vbeta = rbind(c(2,1),c(1,2)) # Construct the regdata (containing X) simnegbindata = NULL for (i in 1:nreg) { betai = as.vector(Z[i,]\%*\%Delta) + chol(Vbeta)\%*\%rnorm(nvar) X = cbind(rep(1,T),rnorm(T,mean=2,sd=0.25)) simnegbindata[[i]] = list(y=simnegbin(X,betai,alpha), X=X,beta=betai) } Beta = NULL for (i in 1:nreg) {Beta=rbind(Beta,matrix(simnegbindata[[i]]$beta,nrow=1))} Data1 = list(regdata=simnegbindata, Z=Z) Mcmc1 = list(R=R) out = rhierNegbinRw(Data=Data1, Mcmc=Mcmc1) cat("Summary of Delta draws",fill=TRUE) summary(out$Deltadraw,tvalues=as.vector(Delta)) cat("Summary of Vbeta draws",fill=TRUE) summary(out$Vbetadraw,tvalues=as.vector(Vbeta[upper.tri(Vbeta,diag=TRUE)])) cat("Summary of alpha draws",fill=TRUE) summary(out$alpha,tvalues=alpha) if(0){ ## plotting examples plot(out$betadraw) plot(out$alpha,tvalues=alpha) plot(out$Deltadraw,tvalues=as.vector(Delta)) } } \keyword{models} bayesm/man/rhierLinearModel.Rd0000644000176000001440000000777212536120467016074 0ustar ripleyusers\name{rhierLinearModel} \alias{rhierLinearModel} \concept{bayes} \concept{MCMC} \concept{Gibbs Sampling} \concept{hierarchical models} \concept{linear model} \title{ Gibbs Sampler for Hierarchical Linear Model } \description{ \code{rhierLinearModel} implements a Gibbs Sampler for hierarchical linear models with a normal prior. } \usage{ rhierLinearModel(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(regdata,Z) (Z optional). } \item{Prior}{ list(Deltabar,A,nu.e,ssq,nu,V) (optional).} \item{Mcmc}{ list(R,keep,nprint) (R required).} } \details{ Model: length(regdata) regression equations. \cr \eqn{y_i = X_i\beta_i + e_i}. \eqn{e_i} \eqn{\sim}{~} \eqn{N(0,\tau_i)}. nvar X vars in each equation. Priors:\cr \eqn{\tau_i} \eqn{\sim}{~} nu.e*\eqn{ssq_i/\chi^2_{nu.e}}. \eqn{\tau_i} is the variance of \eqn{e_i}.\cr \eqn{\beta_i} \eqn{\sim}{~} N(Z\eqn{\Delta}[i,],\eqn{V_{\beta}}). \cr Note: Z\eqn{\Delta} is the matrix Z * \eqn{\Delta}; [i,] refers to ith row of this product.\cr \eqn{vec(\Delta)} given \eqn{V_{\beta}} \eqn{\sim}{~} \eqn{N(vec(Deltabar),V_{\beta} (x) A^{-1})}.\cr \eqn{V_{\beta}} \eqn{\sim}{~} \eqn{IW(nu,V)}. \cr \eqn{Delta, Deltabar} are nz x nvar. \eqn{A} is nz x nz. \eqn{V_{\beta}} is nvar x nvar. Note: if you don't have any Z vars, omit Z in the \code{Data} argument and a vector of ones will be inserted for you. In this case (of no Z vars), the matrix \eqn{\Delta} will be 1 x nvar and should be interpreted as the mean of all unit \eqn{\beta} s. List arguments contain: \itemize{ \item{\code{regdata}}{ list of lists with X,y matrices for each of length(regdata) regressions} \item{\code{regdata[[i]]$X}}{ X matrix for equation i } \item{\code{regdata[[i]]$y}}{ y vector for equation i } \item{\code{Deltabar}}{ nz x nvar matrix of prior means (def: 0)} \item{\code{A}}{ nz x nz matrix for prior precision (def: .01I)} \item{\code{nu.e}}{ d.f. parm for regression error variance prior (def: 3)} \item{\code{ssq}}{ scale parm for regression error var prior (def: var(\eqn{y_i}))} \item{\code{nu}}{ d.f. parm for Vbeta prior (def: nvar+3)} \item{\code{V}}{ Scale location matrix for Vbeta prior (def: nu*I)} \item{\code{R}}{ number of MCMC draws} \item{\code{keep}}{ MCMC thinning parm: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ a list containing \item{betadraw}{nreg x nvar x R/keep array of individual regression coef draws} \item{taudraw}{R/keep x nreg array of error variance draws} \item{Deltadraw}{R/keep x nz x nvar array of Deltadraws} \item{Vbetadraw}{R/keep x nvar*nvar array of Vbeta draws} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.comu}. } \seealso{ \code{\link{rhierLinearMixture}} } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} nreg=100; nobs=100; nvar=3 Vbeta=matrix(c(1,.5,0,.5,2,.7,0,.7,1),ncol=3) Z=cbind(c(rep(1,nreg)),3*runif(nreg)); Z[,2]=Z[,2]-mean(Z[,2]) nz=ncol(Z) Delta=matrix(c(1,-1,2,0,1,0),ncol=2) Delta=t(Delta) # first row of Delta is means of betas Beta=matrix(rnorm(nreg*nvar),nrow=nreg)\%*\%chol(Vbeta)+Z\%*\%Delta tau=.1 iota=c(rep(1,nobs)) regdata=NULL for (reg in 1:nreg) { X=cbind(iota,matrix(runif(nobs*(nvar-1)),ncol=(nvar-1))) y=X\%*\%Beta[reg,]+sqrt(tau)*rnorm(nobs); regdata[[reg]]=list(y=y,X=X) } Data1=list(regdata=regdata,Z=Z) Mcmc1=list(R=R,keep=1) out=rhierLinearModel(Data=Data1,Mcmc=Mcmc1) cat("Summary of Delta draws",fill=TRUE) summary(out$Deltadraw,tvalues=as.vector(Delta)) cat("Summary of Vbeta draws",fill=TRUE) summary(out$Vbetadraw,tvalues=as.vector(Vbeta[upper.tri(Vbeta,diag=TRUE)])) if(0){ ## plotting examples plot(out$betadraw) plot(out$Deltadraw) } } \keyword{ regression } bayesm/man/llnhlogit.Rd0000644000176000001440000000410212524673457014634 0ustar ripleyusers\name{llnhlogit} \alias{llnhlogit} \concept{multinomial logit} \concept{non-homothetic utility} \title{ Evaluate Log Likelihood for non-homothetic Logit Model } \description{ \code{llnhlogit} evaluates log-likelihood for the Non-homothetic Logit model. } \usage{ llnhlogit(theta, choice, lnprices, Xexpend) } \arguments{ \item{theta}{ parameter vector (see details section) } \item{choice}{ n x 1 vector of choice (1, \ldots, p) } \item{lnprices}{ n x p array of log-prices} \item{Xexpend}{ n x d array of vars predicting expenditure } } \details{ Non-homothetic logit model, \eqn{Pr(i) = exp(tau v_i)/sum_j(exp(tau v_j))} \cr \eqn{v_i = alpha_i - e^{kappaStar_i}u^i - lnp_i} tau is the scale parameter of extreme value error distribution.\cr \eqn{u^i} solves \eqn{u^i = psi_i(u^i)E/p_i}.\cr \eqn{ln(psi_i(U)) = alpha_i - e^{kappaStar_i}U}. \cr \eqn{lnE = gamma'Xexpend}.\cr Structure of theta vector \cr alpha: (p x 1) vector of utility intercepts.\cr kappaStar: (p x 1) vector of utility rotation parms expressed on natural log scale. \cr gamma: (k x 1) -- expenditure variable coefs.\cr tau: (1 x 1) -- logit scale parameter.\cr } \value{ value of log-likelihood (sum of log prob of observed multinomial outcomes). } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch,Chapter 4. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{simnhlogit}} } \examples{ ## N=1000 p=3 k=1 theta = c(rep(1,p),seq(from=-1,to=1,length=p),rep(2,k),.5) lnprices = matrix(runif(N*p),ncol=p) Xexpend = matrix(runif(N*k),ncol=k) simdata = simnhlogit(theta,lnprices,Xexpend) # # let's evaluate likelihood at true theta # llstar = llnhlogit(theta,simdata$y,simdata$lnprices,simdata$Xexpend) } \keyword{ models }bayesm/man/rbprobitGibbs.Rd0000644000176000001440000000413012511022351015404 0ustar ripleyusers\name{rbprobitGibbs} \alias{rbprobitGibbs} \concept{bayes} \concept{MCMC} \concept{probit} \concept{Gibbs Sampling} \title{ Gibbs Sampler (Albert and Chib) for Binary Probit } \description{ \code{rbprobitGibbs} implements the Albert and Chib Gibbs Sampler for the binary probit model. } \usage{ rbprobitGibbs(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(X,y)} \item{Prior}{ list(betabar,A)} \item{Mcmc}{ list(R,keep,nprint) } } \details{ Model: \eqn{z = X\beta + e}. \eqn{e} \eqn{\sim}{~} \eqn{N(0,I)}. y=1, if z> 0. Prior: \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})}. List arguments contain \describe{ \item{\code{X}}{Design Matrix} \item{\code{y}}{n x 1 vector of observations, (0 or 1)} \item{\code{betabar}}{k x 1 prior mean (def: 0)} \item{\code{A}}{k x k prior precision matrix (def: .01I)} \item{\code{R}}{ number of MCMC draws } \item{\code{keep}}{ thinning parameter - keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ \item{betadraw }{R/keep x k array of betadraws} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmnpGibbs}} } \examples{ ## ## rbprobitGibbs example ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) simbprobit= function(X,beta) { ## function to simulate from binary probit including x variable y=ifelse((X\%*\%beta+rnorm(nrow(X)))<0,0,1) list(X=X,y=y,beta=beta) } nobs=200 X=cbind(rep(1,nobs),runif(nobs),runif(nobs)) beta=c(0,1,-1) nvar=ncol(X) simout=simbprobit(X,beta) Data1=list(X=simout$X,y=simout$y) Mcmc1=list(R=R,keep=1) out=rbprobitGibbs(Data=Data1,Mcmc=Mcmc1) summary(out$betadraw,tvalues=beta) if(0){ ## plotting example plot(out$betadraw,tvalues=beta) } } \keyword{ models } bayesm/man/rmvst.Rd0000755000176000001440000000174511430347045014012 0ustar ripleyusers\name{rmvst} \alias{rmvst} \concept{multivariate t distribution} \concept{student-t} \concept{simulation} \title{ Draw from Multivariate Student-t } \description{ \code{rmvst} draws from a Multivariate student-t distribution. } \usage{ rmvst(nu, mu, root) } \arguments{ \item{nu}{ d.f. parameter } \item{mu}{ mean vector } \item{root}{ Upper Tri Cholesky Root of Sigma } } \value{ length(mu) draw vector } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{lndMvst}}} \examples{ ## set.seed(66) rmvst(nu=5,mu=c(rep(0,2)),root=chol(matrix(c(2,1,1,2),ncol=2))) } \keyword{ distribution } bayesm/man/rmultireg.Rd0000644000176000001440000000476512523215707014656 0ustar ripleyusers\name{rmultireg} \alias{rmultireg} \concept{bayes} \concept{multivariate regression} \concept{simulation} \title{ Draw from the Posterior of a Multivariate Regression } \description{ \code{ rmultireg} draws from the posterior of a Multivariate Regression model with a natural conjugate prior. } \usage{ rmultireg(Y, X, Bbar, A, nu, V) } \arguments{ \item{Y}{ n x m matrix of observations on m dep vars } \item{X}{ n x k matrix of observations on indep vars (supply intercept) } \item{Bbar}{ k x m matrix of prior mean of regression coefficients } \item{A}{ k x k Prior precision matrix } \item{nu}{ d.f. parameter for Sigma } \item{V}{ m x m pdf location parameter for prior on Sigma } } \details{ Model: \eqn{Y=XB+U}. \eqn{cov(u_i) = \Sigma}. \eqn{B} is k x m matrix of coefficients. \eqn{\Sigma} is m x m covariance. Priors: \eqn{\beta} given \eqn{\Sigma} \eqn{\sim}{~} \eqn{N(betabar,\Sigma (x) A^{-1})}. \eqn{betabar=vec(Bbar)}; \eqn{\beta = vec(B)} \cr \eqn{\Sigma} \eqn{\sim}{~} IW(nu,V). } \value{ A list of the components of a draw from the posterior \item{B }{ draw of regression coefficient matrix } \item{Sigma }{ draw of Sigma } } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) n=200 m=2 X=cbind(rep(1,n),runif(n)) k=ncol(X) B=matrix(c(1,2,-1,3),ncol=m) Sigma=matrix(c(1,.5,.5,1),ncol=m); RSigma=chol(Sigma) Y=X\%*\%B+matrix(rnorm(m*n),ncol=m)\%*\%RSigma betabar=rep(0,k*m);Bbar=matrix(betabar,ncol=m) A=diag(rep(.01,k)) nu=3; V=nu*diag(m) betadraw=matrix(double(R*k*m),ncol=k*m) Sigmadraw=matrix(double(R*m*m),ncol=m*m) for (rep in 1:R) {out=rmultireg(Y,X,Bbar,A,nu,V);betadraw[rep,]=out$B Sigmadraw[rep,]=out$Sigma} cat(" Betadraws ",fill=TRUE) mat=apply(betadraw,2,quantile,probs=c(.01,.05,.5,.95,.99)) mat=rbind(as.vector(B),mat); rownames(mat)[1]="beta" print(mat) cat(" Sigma draws",fill=TRUE) mat=apply(Sigmadraw,2,quantile,probs=c(.01,.05,.5,.95,.99)) mat=rbind(as.vector(Sigma),mat); rownames(mat)[1]="Sigma" print(mat) } \keyword{ regression } bayesm/man/runireg.Rd0000644000176000001440000000415612523217333014306 0ustar ripleyusers\name{runireg} \alias{runireg} \concept{bayes} \concept{regression} \title{ IID Sampler for Univariate Regression } \description{ \code{runireg} implements an iid sampler to draw from the posterior of a univariate regression with a conjugate prior. } \usage{ runireg(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(y,X)} \item{Prior}{ list(betabar,A, nu, ssq) } \item{Mcmc}{ list(R,keep,nprint)} } \details{ Model: \eqn{y = X\beta + e}. \eqn{e} \eqn{\sim}{~} \eqn{N(0,\sigma^2)}. \cr Priors: \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,\sigma^2*A^{-1})}. \eqn{\sigma^2} \eqn{\sim}{~} \eqn{(nu*ssq)/\chi^2_{nu}}. List arguments contain \itemize{ \item{\code{X}}{n x k Design Matrix} \item{\code{y}}{n x 1 vector of observations} \item{\code{betabar}}{k x 1 prior mean (def: 0)} \item{\code{A}}{k x k prior precision matrix (def: .01I)} \item{\code{nu}}{ d.f. parm for Inverted Chi-square prior (def: 3)} \item{\code{ssq}}{ scale parm for Inverted Chi-square prior (def: var(y))} \item{\code{R}}{ number of draws } \item{\code{keep}}{ thinning parameter - keep every keepth draw} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ list of iid draws \item{betadraw }{ R x k array of betadraws } \item{sigmasqdraw }{ R vector of sigma-sq draws} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{runiregGibbs}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) n=200 X=cbind(rep(1,n),runif(n)); beta=c(1,2); sigsq=.25 y=X\%*\%beta+rnorm(n,sd=sqrt(sigsq)) out=runireg(Data=list(y=y,X=X),Mcmc=list(R=R)) cat("Summary of beta/sigma-sq draws",fill=TRUE) summary(out$betadraw,tvalues=beta) summary(out$sigmasqdraw,tvalues=sigsq) if(0){ ## plotting examples plot(out$betadraw) } } \keyword{ regression } bayesm/man/cgetC.Rd0000644000176000001440000000202312523224164013647 0ustar ripleyusers\name{cgetC} \alias{cgetC} \title{ Obtain A List of Cut-offs for Scale Usage Problems } \description{ \code{cgetC} obtains a list of censoring points, or cut-offs, used in the ordinal multivariate probit model of Rossi et al (2001). This approach uses a quadratic parameterization of the cut-offs. The model is useful for modeling correlated ordinal data on a scale from 1, ..., k with different scale usage patterns. } \usage{ cgetC(e, k) } \arguments{ \item{e}{ quadratic parameter (>0 and less than 1) } \item{k}{ items are on a scale from 1, \ldots, k } } \section{Warning}{ This is a utility function which implements \strong{no} error-checking. } \value{ A vector of k+1 cut-offs. } \references{ Rossi et al (2001), \dQuote{Overcoming Scale Usage Heterogeneity,} \emph{JASA}96, 20-31. } \author{ Rob McCulloch and Peter Rossi, Anderson School, UCLA. \email{perossichi@gmail.com}. } \seealso{ \code{\link{rscaleUsage}} } \examples{ ## cgetC(.1,10) } \keyword{ utilities } bayesm/man/lndMvn.Rd0000644000176000001440000000213412523034211014053 0ustar ripleyusers\name{lndMvn} \alias{lndMvn} \concept{multivariate normal distribution} \concept{density} \title{ Compute Log of Multivariate Normal Density } \description{ \code{lndMvn} computes the log of a Multivariate Normal Density. } \usage{ lndMvn(x, mu, rooti) } \arguments{ \item{x}{ density ordinate } \item{mu}{ mu vector } \item{rooti}{ inv of Upper Triangular Cholesky root of \eqn{\Sigma} } } \details{ \eqn{z} \eqn{\sim}{~} \eqn{N(mu,\Sigma)} } \value{ log density value } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{lndMvst}} } \examples{ ## Sigma=matrix(c(1,.5,.5,1),ncol=2) lndMvn(x=c(rep(0,2)),mu=c(rep(0,2)),rooti=backsolve(chol(Sigma),diag(2))) } \keyword{ distribution } bayesm/man/mixDen.Rd0000644000176000001440000000266212523034314014053 0ustar ripleyusers\name{mixDen} \alias{mixDen} \concept{normal mixture} \concept{marginal distribution} \concept{density} \title{ Compute Marginal Density for Multivariate Normal Mixture } \description{ \code{mixDen} computes the marginal density for each component of a normal mixture at each of the points on a user-specifed grid. } \usage{ mixDen(x, pvec, comps) } \arguments{ \item{x}{ array - ith column gives grid points for ith variable } \item{pvec}{ vector of mixture component probabilites } \item{comps}{ list of lists of components for normal mixture } } \details{ length(comps) is the number of mixture components. comps[[j]] is a list of parameters of the jth component. comps[[j]]$mu is mean vector; comps[[j]]$rooti is the UL decomp of \eqn{\Sigma^{-1}}. } \value{ an array of the same dimension as grid with density values. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rnmixGibbs}} } \examples{ \dontrun{ ## ## see examples in rnmixGibbs documentation ## } } \keyword{ models } \keyword{ multivariate } bayesm/man/lndIWishart.Rd0000755000176000001440000000227511430344044015062 0ustar ripleyusers\name{lndIWishart} \alias{lndIWishart} \concept{Inverted Wishart distribution} \concept{density} \title{ Compute Log of Inverted Wishart Density } \description{ \code{lndIWishart} computes the log of an Inverted Wishart density. } \usage{ lndIWishart(nu, V, IW) } \arguments{ \item{nu}{ d.f. parameter } \item{V}{ "location" parameter } \item{IW}{ ordinate for density evaluation } } \details{ \eqn{Z} \eqn{\sim}{~} Inverted Wishart(nu,V). \cr in this parameterization, \eqn{E[Z]=1/(nu-k-1) V}, V is a k x k matrix \code{lndIWishart} computes the complete log-density, including normalizing constants. } \value{ log density value } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rwishart}} } \examples{ ## lndIWishart(5,diag(3),(diag(3)+.5)) } \keyword{ distribution } bayesm/man/lndMvst.Rd0000644000176000001440000000234112523034207014251 0ustar ripleyusers\name{lndMvst} \alias{lndMvst} \concept{multivariate t distribution} \concept{student-t distribution} \concept{density} \title{ Compute Log of Multivariate Student-t Density } \description{ \code{lndMvst} computes the log of a Multivariate Student-t Density. } \usage{ lndMvst(x, nu, mu, rooti,NORMC) } \arguments{ \item{x}{ density ordinate } \item{nu}{ d.f. parameter } \item{mu}{ mu vector } \item{rooti}{ inv of Cholesky root of \eqn{\Sigma} } \item{NORMC}{ include normalizing constant (def: FALSE) } } \details{ \eqn{z} \eqn{\sim}{~} \eqn{MVst(mu,nu,\Sigma)} } \value{ log density value } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{lndMvn}} } \examples{ ## Sigma=matrix(c(1,.5,.5,1),ncol=2) lndMvst(x=c(rep(0,2)),nu=4,mu=c(rep(0,2)),rooti=backsolve(chol(Sigma),diag(2))) } \keyword{ distribution } bayesm/man/lndIChisq.Rd0000644000176000001440000000221712523222075014504 0ustar ripleyusers\name{lndIChisq} \alias{lndIChisq} \concept{Inverted Chi-squared Distribution} \concept{density} \title{ Compute Log of Inverted Chi-Squared Density } \description{ \code{lndIChisq} computes the log of an Inverted Chi-Squared Density. } \usage{ lndIChisq(nu, ssq, X) } \arguments{ \item{nu}{ d.f. parameter } \item{ssq}{ scale parameter } \item{X}{ ordinate for density evaluation (this must be a matrix)} } \details{ \eqn{Z= nu*ssq/\chi^2_{nu}}, \eqn{Z} \eqn{\sim}{~} Inverted Chi-Squared. \cr \code{lndIChisq} computes the complete log-density, including normalizing constants. } \value{ log density value } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{dchisq}} } \examples{ ## lndIChisq(3,1,matrix(2)) } \keyword{ distribution } bayesm/man/rmnpGibbs.Rd0000644000176000001440000000710312536435720014557 0ustar ripleyusers\name{rmnpGibbs} \alias{rmnpGibbs} \concept{bayes} \concept{multinomial probit} \concept{MCMC} \concept{Gibbs Sampling} \title{ Gibbs Sampler for Multinomial Probit } \description{ \code{rmnpGibbs} implements the McCulloch/Rossi Gibbs Sampler for the multinomial probit model. } \usage{ rmnpGibbs(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(p, y, X)} \item{Prior}{ list(betabar,A,nu,V) (optional)} \item{Mcmc}{ list(beta0,sigma0,R,keep,nprint) (R required) } } \details{ model: \cr \eqn{w_i = X_i\beta + e}. \eqn{e} \eqn{\sim}{~} \eqn{N(0,\Sigma)}. note: \eqn{w_i, e} are (p-1) x 1.\cr \eqn{y_i = j}, if \eqn{w_{ij} > max(0,w_{i,-j})} j=1,\ldots,p-1. \eqn{w_{i,-j}} means elements of \eqn{w_i} other than the jth. \cr \eqn{y_i = p}, if all \eqn{w_i < 0}.\cr priors:\cr \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})} \cr \eqn{\Sigma} \eqn{\sim}{~} IW(nu,V)\cr to make up X matrix use \code{\link{createX}} with \code{DIFF=TRUE}. List arguments contain \itemize{ \item{\code{p}}{number of choices or possible multinomial outcomes} \item{\code{y}}{n x 1 vector of multinomial outcomes} \item{\code{X}}{n*(p-1) x k Design Matrix} \item{\code{betabar}}{k x 1 prior mean (def: 0)} \item{\code{A}}{k x k prior precision matrix (def: .01I)} \item{\code{nu}}{ d.f. parm for IWishart prior (def: (p-1) + 3)} \item{\code{V}}{ pds location parm for IWishart prior (def: nu*I)} \item{\code{beta0}}{ initial value for beta} \item{\code{sigma0}}{ initial value for sigma } \item{\code{R}}{ number of MCMC draws } \item{\code{keep}}{ thinning parameter - keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ a list containing: \item{betadraw }{R/keep x k array of betadraws} \item{sigmadraw}{R/keep x (p-1)*(p-1) array of sigma draws -- each row is in vector form} } \note{ \eqn{\beta} is not identified. \eqn{\beta}/sqrt(\eqn{\sigma_{11}}) and \eqn{\Sigma}/\eqn{\sigma_{11}} are. See Allenby et al or example below for details. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 4. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmvpGibbs}} } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) p=3 n=500 beta=c(-1,1,1,2) Sigma=matrix(c(1,.5,.5,1),ncol=2) k=length(beta) X1=matrix(runif(n*p,min=0,max=2),ncol=p); X2=matrix(runif(n*p,min=0,max=2),ncol=p) X=createX(p,na=2,nd=NULL,Xa=cbind(X1,X2),Xd=NULL,DIFF=TRUE,base=p) simmnp= function(X,p,n,beta,sigma) { indmax=function(x) {which(max(x)==x)} Xbeta=X\%*\%beta w=as.vector(crossprod(chol(sigma),matrix(rnorm((p-1)*n),ncol=n)))+ Xbeta w=matrix(w,ncol=(p-1),byrow=TRUE) maxw=apply(w,1,max) y=apply(w,1,indmax) y=ifelse(maxw < 0,p,y) return(list(y=y,X=X,beta=beta,sigma=sigma)) } simout=simmnp(X,p,500,beta,Sigma) Data1=list(p=p,y=simout$y,X=simout$X) Mcmc1=list(R=R,keep=1) out=rmnpGibbs(Data=Data1,Mcmc=Mcmc1) cat(" Summary of Betadraws ",fill=TRUE) betatilde=out$betadraw/sqrt(out$sigmadraw[,1]) attributes(betatilde)$class="bayesm.mat" summary(betatilde,tvalues=beta) cat(" Summary of Sigmadraws ",fill=TRUE) sigmadraw=out$sigmadraw/out$sigmadraw[,1] attributes(sigmadraw)$class="bayesm.var" summary(sigmadraw,tvalues=as.vector(Sigma[upper.tri(Sigma,diag=TRUE)])) if(0){ ## plotting examples plot(betatilde,tvalues=beta) } } \keyword{ models } bayesm/man/nmat.Rd0000755000176000001440000000167411430344656013604 0ustar ripleyusers\name{nmat} \alias{nmat} \title{ Convert Covariance Matrix to a Correlation Matrix } \description{ \code{nmat} converts a covariance matrix (stored as a vector, col by col) to a correlation matrix (also stored as a vector). } \usage{ nmat(vec) } \arguments{ \item{vec}{ k x k Cov matrix stored as a k*k x 1 vector (col by col) } } \details{ This routine is often used with apply to convert an R x (k*k) array of covariance MCMC draws to correlations. As in \code{corrdraws=apply(vardraws,1,nmat)} } \value{ k*k x 1 vector with correlation matrix } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \examples{ ## set.seed(66) X=matrix(rnorm(200,4),ncol=2) Varmat=var(X) nmat(as.vector(Varmat)) } \keyword{ utilities } \keyword{ array } bayesm/man/orangeJuice.Rd0000644000176000001440000001507712523027600015066 0ustar ripleyusers\name{orangeJuice} \alias{orangeJuice} \docType{data} \title{Store-level Panel Data on Orange Juice Sales} \description{ yx, weekly sales of refrigerated orange juice at 83 stores. \cr storedemo, contains demographic information on those stores. \cr } \usage{data(orangeJuice)} \format{ This R object is a list of two data frames, list(yx,storedemo).\cr List of 2 \cr $ yx :'data.frame': 106139 obs. of 19 variables:\cr \ldots $ store : int [1:106139] 2 2 2 2 2 2 2 2 2 2 \cr \ldots $ brand : int [1:106139] 1 1 1 1 1 1 1 1 1 1 \cr \ldots $ week : int [1:106139] 40 46 47 48 50 51 52 53 54 57 \cr \ldots $ logmove : num [1:106139] 9.02 8.72 8.25 8.99 9.09 \cr \ldots $ constant: int [1:106139] 1 1 1 1 1 1 1 1 1 1 \cr \ldots $ price1 : num [1:106139] 0.0605 0.0605 0.0605 0.0605 0.0605 \cr \ldots $ price2 : num [1:106139] 0.0605 0.0603 0.0603 0.0603 0.0603 \cr \ldots $ price3 : num [1:106139] 0.0420 0.0452 0.0452 0.0498 0.0436 \cr \ldots $ price4 : num [1:106139] 0.0295 0.0467 0.0467 0.0373 0.0311 \cr \ldots $ price5 : num [1:106139] 0.0495 0.0495 0.0373 0.0495 0.0495 \cr \ldots $ price6 : num [1:106139] 0.0530 0.0478 0.0530 0.0530 0.0530 \cr \ldots $ price7 : num [1:106139] 0.0389 0.0458 0.0458 0.0458 0.0466 \cr \ldots $ price8 : num [1:106139] 0.0414 0.0280 0.0414 0.0414 0.0414 \cr \ldots $ price9 : num [1:106139] 0.0289 0.0430 0.0481 0.0423 0.0423 \cr \ldots $ price10 : num [1:106139] 0.0248 0.0420 0.0327 0.0327 0.0327 \cr \ldots $ price11 : num [1:106139] 0.0390 0.0390 0.0390 0.0390 0.0382 \cr \ldots $ deal : int [1:106139] 1 0 0 0 0 0 1 1 1 1 \cr \ldots $ feat : num [1:106139] 0 0 0 0 0 0 0 0 0 0 \cr \ldots $ profit : num [1:106139] 38.0 30.1 30.0 29.9 29.9 \cr 1 Tropicana Premium 64 oz; 2 Tropicana Premium 96 oz; 3 Florida's Natural 64 oz; \cr 4 Tropicana 64 oz; 5 Minute Maid 64 oz; 6 Minute Maid 96 oz; \cr 7 Citrus Hill 64 oz; 8 Tree Fresh 64 oz; 9 Florida Gold 64 oz; \cr 10 Dominicks 64 oz; 11 Dominicks 128 oz. \cr $ storedemo:'data.frame': 83 obs. of 12 variables:\cr \ldots $ STORE : int [1:83] 2 5 8 9 12 14 18 21 28 32 \cr \ldots $ AGE60 : num [1:83] 0.233 0.117 0.252 0.269 0.178 \cr \ldots $ EDUC : num [1:83] 0.2489 0.3212 0.0952 0.2222 0.2534 \cr \ldots $ ETHNIC : num [1:83] 0.1143 0.0539 0.0352 0.0326 0.3807 \cr \ldots $ INCOME : num [1:83] 10.6 10.9 10.6 10.8 10.0 \cr \ldots $ HHLARGE : num [1:83] 0.1040 0.1031 0.1317 0.0968 0.0572 \cr \ldots $ WORKWOM : num [1:83] 0.304 0.411 0.283 0.359 0.391 \cr \ldots $ HVAL150 : num [1:83] 0.4639 0.5359 0.0542 0.5057 0.3866 \cr \ldots $ SSTRDIST: num [1:83] 2.11 3.80 2.64 1.10 9.20 \cr \ldots $ SSTRVOL : num [1:83] 1.143 0.682 1.500 0.667 1.111 \cr \ldots $ CPDIST5 : num [1:83] 1.93 1.60 2.91 1.82 0.84 \cr \ldots $ CPWVOL5 : num [1:83] 0.377 0.736 0.641 0.441 0.106 \cr } \details{ \describe{ \item{\code{store}}{store number} \item{\code{brand}}{brand indicator} \item{\code{week}}{week number} \item{\code{logmove}}{log of the number of units sold} \item{\code{constant}}{a vector of 1} \item{\code{price1}}{price of brand 1} \item{\code{deal}}{in-store coupon activity} \item{\code{feature}}{feature advertisement} \item{\code{STORE}}{store number} \item{\code{AGE60}}{percentage of the population that is aged 60 or older} \item{\code{EDUC}}{percentage of the population that has a college degree} \item{\code{ETHNIC}}{percent of the population that is black or Hispanic} \item{\code{INCOME}}{median income} \item{\code{HHLARGE}}{percentage of households with 5 or more persons} \item{\code{WORKWOM}}{percentage of women with full-time jobs} \item{\code{HVAL150}}{percentage of households worth more than $150,000} \item{\code{SSTRDIST}}{distance to the nearest warehouse store} \item{\code{SSTRVOL}}{ratio of sales of this store to the nearest warehouse store} \item{\code{CPDIST5}}{average distance in miles to the nearest 5 supermarkets} \item{\code{CPWVOL5}}{ratio of sales of this store to the average of the nearest five stores} } } \source{ Alan L. Montgomery (1997), "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," \emph{Marketing Science} 16(4) 315-337. } \references{ Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi et al.\cr \url{http://www.perossi.org/home/bsm-1} } \examples{ ## Example ## load data data(orangeJuice) ## print some quantiles of yx data cat("Quantiles of the Variables in yx data",fill=TRUE) mat=apply(as.matrix(orangeJuice$yx),2,quantile) print(mat) ## print some quantiles of storedemo data cat("Quantiles of the Variables in storedemo data",fill=TRUE) mat=apply(as.matrix(orangeJuice$storedemo),2,quantile) print(mat) ## Example 2 processing for use with rhierLinearModel ## ## if(0) { ## select brand 1 for analysis brand1=orangeJuice$yx[(orangeJuice$yx$brand==1),] store = sort(unique(brand1$store)) nreg = length(store) nvar=14 regdata=NULL for (reg in 1:nreg) { y=brand1$logmove[brand1$store==store[reg]] iota=c(rep(1,length(y))) X=cbind(iota,log(brand1$price1[brand1$store==store[reg]]), log(brand1$price2[brand1$store==store[reg]]), log(brand1$price3[brand1$store==store[reg]]), log(brand1$price4[brand1$store==store[reg]]), log(brand1$price5[brand1$store==store[reg]]), log(brand1$price6[brand1$store==store[reg]]), log(brand1$price7[brand1$store==store[reg]]), log(brand1$price8[brand1$store==store[reg]]), log(brand1$price9[brand1$store==store[reg]]), log(brand1$price10[brand1$store==store[reg]]), log(brand1$price11[brand1$store==store[reg]]), brand1$deal[brand1$store==store[reg]], brand1$feat[brand1$store==store[reg]]) regdata[[reg]]=list(y=y,X=X) } ## storedemo is standardized to zero mean. Z=as.matrix(orangeJuice$storedemo[,2:12]) dmean=apply(Z,2,mean) for (s in 1:nreg){ Z[s,]=Z[s,]-dmean } iotaz=c(rep(1,nrow(Z))) Z=cbind(iotaz,Z) nz=ncol(Z) Data=list(regdata=regdata,Z=Z) Mcmc=list(R=R,keep=1) out=rhierLinearModel(Data=Data,Mcmc=Mcmc) summary(out$Deltadraw) summary(out$Vbetadraw) if(0){ ## plotting examples plot(out$betadraw) } } } \keyword{datasets} bayesm/man/rdirichlet.Rd0000755000176000001440000000152411430345261014761 0ustar ripleyusers\name{rdirichlet} \alias{rdirichlet} \concept{dirichlet distribution} \concept{simulation} \title{ Draw From Dirichlet Distribution } \description{ \code{rdirichlet} draws from Dirichlet } \usage{ rdirichlet(alpha) } \arguments{ \item{alpha}{ vector of Dirichlet parms (must be > 0)} } \value{ Vector of draws from Dirichlet } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \examples{ ## set.seed(66) rdirichlet(c(rep(3,5))) } \keyword{ distribution } bayesm/man/momMix.Rd0000644000176000001440000000322412523034412014067 0ustar ripleyusers\name{momMix} \alias{momMix} \concept{mcmc} \concept{normal mixture} \concept{posterior moments} \title{ Compute Posterior Expectation of Normal Mixture Model Moments } \description{ \code{momMix} averages the moments of a normal mixture model over MCMC draws. } \usage{ momMix(probdraw, compdraw) } \arguments{ \item{probdraw}{ R x ncomp list of draws of mixture probs } \item{compdraw}{ list of length R of draws of mixture component moments } } \details{ R is the number of MCMC draws in argument list above. \cr ncomp is the number of mixture components fitted.\cr compdraw is a list of lists of lists with mixture components. \cr compdraw[[i]] is ith draw. \cr compdraw[[i]][[j]][[1]] is the mean parameter vector for the jth component, ith MCMC draw. \cr compdraw[[i]][[j]][[2]] is the UL decomposition of \eqn{\Sigma^{-1}} for the jth component, ith MCMC draw. } \value{ a list of the following items \dots \item{mu }{Posterior Expectation of Mean} \item{sigma }{Posterior Expecation of Covariance Matrix} \item{sd }{Posterior Expectation of Vector of Standard Deviations} \item{corr }{Posterior Expectation of Correlation Matrix} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 5. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rmixGibbs}}} \keyword{ multivariate } bayesm/man/mnpProb.Rd0000755000176000001440000000334011430344565014251 0ustar ripleyusers\name{mnpProb} \alias{mnpProb} \concept{MNP} \concept{Multinomial Probit Model} \concept{GHK} \concept{market share simulator} \title{ Compute MNP Probabilities } \description{ \code{mnpProb} computes MNP probabilities for a given X matrix corresponding to one observation. This function can be used with output from \code{rmnpGibbs} to simulate the posterior distribution of market shares or fitted probabilties. } \usage{ mnpProb(beta, Sigma, X, r) } \arguments{ \item{beta}{ MNP coefficients } \item{Sigma}{ Covariance matrix of latents } \item{X}{ X array for one observation -- use \code{createX} to make } \item{r}{ number of draws used in GHK (def: 100)} } \details{ see \code{\link{rmnpGibbs}} for definition of the model and the interpretation of the beta, Sigma parameters. Uses the GHK method to compute choice probabilities. To simulate a distribution of probabilities, loop over the beta, Sigma draws from \code{rmnpGibbs} output. } \value{ p x 1 vector of choice probabilites } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi,Allenby and McCulloch, Chapters 2 and 4. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmnpGibbs}}, \code{\link{createX}} } \examples{ ## ## example of computing MNP probabilites ## here I'm thinking of Xa as having the prices of each of the 3 alternatives Xa=matrix(c(1,.5,1.5),nrow=1) X=createX(p=3,na=1,nd=NULL,Xa=Xa,Xd=NULL,DIFF=TRUE) beta=c(1,-1,-2) ## beta contains two intercepts and the price coefficient Sigma=matrix(c(1,.5,.5,1),ncol=2) mnpProb(beta,Sigma,X) } \keyword{ models } bayesm/man/tuna.Rd0000644000176000001440000001023612536617561013611 0ustar ripleyusers\name{tuna} \alias{tuna} \docType{data} \title{Data on Canned Tuna Sales} \description{ Volume of canned tuna sales as well as a measure of display activity, log price and log wholesale price. Weekly data aggregated to the chain level. This data is extracted from the Dominick's Finer Foods database maintained by the Kilts center for marketing at the University of Chicago's Booth School of Business. Brands are seven of the top 10 UPCs in the canned tuna product category. } \usage{data(tuna)} \format{ A data frame with 338 observations on the following 30 variables. \describe{ \item{\code{WEEK}}{a numeric vector} \item{\code{MOVE1}}{unit sales of Star Kist 6 oz.} \item{\code{MOVE2}}{unit sales of Chicken of the Sea 6 oz.} \item{\code{MOVE3}}{unit sales of Bumble Bee Solid 6.12 oz.} \item{\code{MOVE4}}{unit sales of Bumble Bee Chunk 6.12 oz.} \item{\code{MOVE5}}{unit sales of Geisha 6 oz.} \item{\code{MOVE6}}{unit sales of Bumble Bee Large Cans.} \item{\code{MOVE7}}{unit sales of HH Chunk Lite 6.5 oz.} \item{\code{NSALE1}}{a measure of display activity of Star Kist 6 oz.} \item{\code{NSALE2}}{a measure of display activity of Chicken of the Sea 6 oz.} \item{\code{NSALE3}}{a measure of display activity of Bumble Bee Solid 6.12 oz.} \item{\code{NSALE4}}{a measure of display activity of Bumble Bee Chunk 6.12 oz.} \item{\code{NSALE5}}{a measure of display activity of Geisha 6 oz.} \item{\code{NSALE6}}{a measure of display activity of Bumble Bee Large Cans.} \item{\code{NSALE7}}{a measure of display activity of HH Chunk Lite 6.5 oz.} \item{\code{LPRICE1}}{log of price of Star Kist 6 oz.} \item{\code{LPRICE2}}{log of price of Chicken of the Sea 6 oz.} \item{\code{LPRICE3}}{log of price of Bumble Bee Solid 6.12 oz.} \item{\code{LPRICE4}}{log of price of Bumble Bee Chunk 6.12 oz.} \item{\code{LPRICE5}}{log of price of Geisha 6 oz.} \item{\code{LPRICE6}}{log of price of Bumble Bee Large Cans.} \item{\code{LPRICE7}}{log of price of HH Chunk Lite 6.5 oz.} \item{\code{LWHPRIC1}}{log of wholesale price of Star Kist 6 oz.} \item{\code{LWHPRIC2}}{log of wholesale price of Chicken of the Sea 6 oz.} \item{\code{LWHPRIC3}}{log of wholesale price of Bumble Bee Solid 6.12 oz.} \item{\code{LWHPRIC4}}{log of wholesale price of Bumble Bee Chunk 6.12 oz.} \item{\code{LWHPRIC5}}{log of wholesale price of Geisha 6 oz.} \item{\code{LWHPRIC6}}{log of wholesale price of Bumble Bee Large Cans.} \item{\code{LWHPRIC7}}{log of wholesale price of HH Chunk Lite 6.5 oz.} \item{\code{FULLCUST}}{total customers visits} } } \source{ Chevalier, A. Judith, Anil K. Kashyap and Peter E. Rossi (2003), "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data," \emph{The American Economic Review} , 93(1), 15-37. } \references{ Chapter 7, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \examples{ data(tuna) cat(" Quantiles of sales",fill=TRUE) mat=apply(as.matrix(tuna[,2:5]),2,quantile) print(mat) ## ## example of processing for use with rivGibbs ## if(0) { data(tuna) t = dim(tuna)[1] customers = tuna[,30] sales = tuna[,2:8] lnprice = tuna[,16:22] lnwhPrice= tuna[,23:29] share=sales/mean(customers) shareout=as.vector(1-rowSums(share)) lnprob=log(share/shareout) # create w matrix I1=as.matrix(rep(1, t)) I0=as.matrix(rep(0, t)) intercept=rep(I1, 4) brand1=rbind(I1, I0, I0, I0) brand2=rbind(I0, I1, I0, I0) brand3=rbind(I0, I0, I1, I0) w=cbind(intercept, brand1, brand2, brand3) ## choose brand 1 to 4 y=as.vector(as.matrix(lnprob[,1:4])) X=as.vector(as.matrix(lnprice[,1:4])) lnwhPrice=as.vector(as.matrix (lnwhPrice[1:4])) z=cbind(w, lnwhPrice) Data=list(z=z, w=w, x=X, y=y) Mcmc=list(R=R, keep=1) set.seed(66) out=rivGibbs(Data=Data,Mcmc=Mcmc) cat(" betadraws ",fill=TRUE) summary(out$betadraw) if(0){ ## plotting examples plot(out$betadraw) } } } \keyword{datasets} bayesm/man/mnlHess.Rd0000644000176000001440000000210412536435650014243 0ustar ripleyusers\name{mnlHess} \alias{mnlHess} \concept{multinomial logit} \concept{hessian} \title{ Computes -Expected Hessian for Multinomial Logit} \description{ \code{mnlHess} computes -Expected[Hessian] for Multinomial Logit Model } \usage{ mnlHess(beta,y,X) } \arguments{ \item{beta}{ k x 1 vector of coefficients } \item{y}{ n x 1 vector of choices, (1, \ldots,p) } \item{X}{ n*p x k Design matrix } } \details{ See \code{\link{llmnl}} for information on structure of X array. Use \code{\link{createX}} to make X. } \value{ k x k matrix } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{llmnl}}, \code{\link{createX}}, \code{\link{rmnlIndepMetrop}} } \examples{ ## \dontrun{mnlHess(beta,y,X)} } \keyword{ models } bayesm/man/Scotch.Rd0000755000176000001440000000502011430347622014051 0ustar ripleyusers\name{Scotch} \alias{Scotch} \docType{data} \title{ Survey Data on Brands of Scotch Consumed} \description{ from Simmons Survey. Brands used in last year for those respondents who report consuming scotch. } \usage{data(Scotch)} \format{ A data frame with 2218 observations on the following 21 variables. All variables are coded 1 if consumed in last year, 0 if not. \describe{ \item{\code{Chivas.Regal}}{a numeric vector} \item{\code{Dewar.s.White.Label}}{a numeric vector} \item{\code{Johnnie.Walker.Black.Label}}{a numeric vector} \item{\code{J...B}}{a numeric vector} \item{\code{Johnnie.Walker.Red.Label}}{a numeric vector} \item{\code{Other.Brands}}{a numeric vector} \item{\code{Glenlivet}}{a numeric vector} \item{\code{Cutty.Sark}}{a numeric vector} \item{\code{Glenfiddich}}{a numeric vector} \item{\code{Pinch..Haig.}}{a numeric vector} \item{\code{Clan.MacGregor}}{a numeric vector} \item{\code{Ballantine}}{a numeric vector} \item{\code{Macallan}}{a numeric vector} \item{\code{Passport}}{a numeric vector} \item{\code{Black...White}}{a numeric vector} \item{\code{Scoresby.Rare}}{a numeric vector} \item{\code{Grants}}{a numeric vector} \item{\code{Ushers}}{a numeric vector} \item{\code{White.Horse}}{a numeric vector} \item{\code{Knockando}}{a numeric vector} \item{\code{the.Singleton}}{a numeric vector} } } \source{ Edwards, Y. and G. Allenby (2003), "Multivariate Analysis of Multiple Response Data," \emph{JMR} 40, 321-334. } \references{ Chapter 4, \emph{Bayesian Statistics and Marketing} by Rossi et al.\cr \url{http://www.perossi.org/home/bsm-1} } \examples{ data(Scotch) cat(" Frequencies of Brands", fill=TRUE) mat=apply(as.matrix(Scotch),2,mean) print(mat) ## ## use Scotch data to run Multivariate Probit Model ## if(0){ ## y=as.matrix(Scotch) p=ncol(y); n=nrow(y) dimnames(y)=NULL y=as.vector(t(y)) y=as.integer(y) I_p=diag(p) X=rep(I_p,n) X=matrix(X,nrow=p) X=t(X) R=2000 Data=list(p=p,X=X,y=y) Mcmc=list(R=R) set.seed(66) out=rmvpGibbs(Data=Data,Mcmc=Mcmc) ind=(0:(p-1))*p + (1:p) cat(" Betadraws ",fill=TRUE) mat=apply(out$betadraw/sqrt(out$sigmadraw[,ind]),2,quantile,probs=c(.01,.05,.5,.95,.99)) attributes(mat)$class="bayesm.mat" summary(mat) rdraw=matrix(double((R)*p*p),ncol=p*p) rdraw=t(apply(out$sigmadraw,1,nmat)) attributes(rdraw)$class="bayesm.var" cat(" Draws of Correlation Matrix ",fill=TRUE) summary(rdraw) } } \keyword{datasets} bayesm/man/rnmixGibbs.Rd0000644000176000001440000001037112523216535014736 0ustar ripleyusers\name{rnmixGibbs} \alias{rnmixGibbs} \concept{bayes} \concept{MCMC} \concept{normal mixtures} \concept{Gibbs Sampling} \title{ Gibbs Sampler for Normal Mixtures} \description{ \code{rnmixGibbs} implements a Gibbs Sampler for normal mixtures. } \usage{ rnmixGibbs(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(y) } \item{Prior}{ list(Mubar,A,nu,V,a,ncomp) (only ncomp required)} \item{Mcmc}{ list(R,keep,nprint,Loglike) (R required) } } \details{ Model: \cr \eqn{y_i} \eqn{\sim}{~} \eqn{N(\mu_{ind_i},\Sigma_{ind_i})}. \cr ind \eqn{\sim}{~} iid multinomial(p). p is a ncomp x 1 vector of probs. Priors:\cr \eqn{\mu_j} \eqn{\sim}{~} \eqn{N(mubar,\Sigma_j (x) A^{-1})}. \eqn{mubar=vec(Mubar)}. \cr \eqn{\Sigma_j} \eqn{\sim}{~} IW(nu,V).\cr note: this is the natural conjugate prior -- a special case of multivariate regression.\cr \eqn{p} \eqn{\sim}{~} Dirchlet(a). Output of the components is in the form of a list of lists. \cr compsdraw[[i]] is ith draw -- list of ncomp lists. \cr compsdraw[[i]][[j]] is list of parms for jth normal component. \cr jcomp=compsdraw[[i]][j]]. Then jth comp \eqn{\sim}{~} \eqn{N(jcomp[[1]],\Sigma)}, \eqn{\Sigma} = t(R)\%*\%R, \eqn{R^{-1}} = jcomp[[2]]. List arguments contain: \itemize{ \item{y}{ n x k array of data (rows are obs) } \item{Mubar}{ 1 x k array with prior mean of normal comp means (def: 0)} \item{A}{ 1 x 1 precision parameter for prior on mean of normal comp (def: .01)} \item{nu}{ d.f. parameter for prior on Sigma (normal comp cov matrix) (def: k+3)} \item{V}{ k x k location matrix of IW prior on Sigma (def: nuI)} \item{a}{ ncomp x 1 vector of Dirichlet prior parms (def: rep(5,ncomp))} \item{ncomp}{ number of normal components to be included } \item{R}{ number of MCMC draws } \item{keep}{ MCMC thinning parm: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} \item{LogLike}{ logical flag for compute log-likelihood (def: FALSE)} } } \value{ \item{nmix}{a list containing: probdraw,zdraw,compdraw} \item{ll}{vector of log-likelihood values} } \note{ more details on contents of nmix: \cr \describe{ \item{probdraw}{R/keep x ncomp array of mixture prob draws} \item{zdraw}{R/keep x nobs array of indicators of mixture comp identity for each obs} \item{compdraw}{R/keep lists of lists of comp parm draws} } In this model, the component normal parameters are not-identified due to label-switching. However, the fitted mixture of normals density is identified as it is invariant to label-switching. See Allenby et al, chapter 5 for details. Use \code{eMixMargDen} or \code{momMix} to compute posterior expectation or distribution of various identified parameters. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmixture}}, \code{\link{rmixGibbs}} ,\code{\link{eMixMargDen}}, \code{\link{momMix}}, \code{\link{mixDen}}, \code{\link{mixDenBi}}} \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) dim=5; k=3 # dimension of simulated data and number of "true" components sigma = matrix(rep(0.5,dim^2),nrow=dim);diag(sigma)=1 sigfac = c(1,1,1);mufac=c(1,2,3); compsmv=list() for(i in 1:k) compsmv[[i]] = list(mu=mufac[i]*1:dim,sigma=sigfac[i]*sigma) comps = list() # change to "rooti" scale for(i in 1:k) comps[[i]] = list(mu=compsmv[[i]][[1]],rooti=solve(chol(compsmv[[i]][[2]]))) pvec=(1:k)/sum(1:k) nobs=500 dm = rmixture(nobs,pvec,comps) Data1=list(y=dm$x) ncomp=9 Prior1=list(ncomp=ncomp) Mcmc1=list(R=R,keep=1) out=rnmixGibbs(Data=Data1,Prior=Prior1,Mcmc=Mcmc1) cat("Summary of Normal Mixture Distribution",fill=TRUE) summary(out) tmom=momMix(matrix(pvec,nrow=1),list(comps)) mat=rbind(tmom$mu,tmom$sd) cat(" True Mean/Std Dev",fill=TRUE) print(mat) if(0){ ## ## plotting examples ## plot(out$nmix,Data=dm$x) } } \keyword{ multivariate } bayesm/man/rtrun.Rd0000755000176000001440000000220011430347414013774 0ustar ripleyusers\name{rtrun} \alias{rtrun} \concept{truncated normal} \concept{simulation} \title{ Draw from Truncated Univariate Normal } \description{ \code{rtrun} draws from a truncated univariate normal distribution } \usage{ rtrun(mu, sigma, a, b) } \arguments{ \item{mu}{ mean } \item{sigma}{ sd } \item{a}{ lower bound } \item{b}{ upper bound } } \details{ Note that due to the vectorization of the rnorm,qnorm commands in R, all arguments can be vectors of equal length. This makes the inverse CDF method the most efficient to use in R. } \value{ draw (possibly a vector) } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \examples{ ## set.seed(66) rtrun(mu=c(rep(0,10)),sigma=c(rep(1,10)),a=c(rep(0,10)),b=c(rep(2,10))) } \keyword{ distribution } bayesm/man/rmixGibbs.Rd0000644000176000001440000000271312516002236014552 0ustar ripleyusers\name{rmixGibbs} \alias{rmixGibbs} \title{ Gibbs Sampler for Normal Mixtures w/o Error Checking} \description{ \code{rmixGibbs} makes one draw using the Gibbs Sampler for a mixture of multivariate normals. } \usage{ rmixGibbs(y, Bbar, A, nu, V, a, p, z) } \arguments{ \item{y}{ data array - rows are obs } \item{Bbar}{ prior mean for mean vector of each norm comp } \item{A}{ prior precision parameter} \item{nu}{ prior d.f. parm } \item{V}{ prior location matrix for covariance priro } \item{a}{ Dirichlet prior parms } \item{p}{ prior prob of each mixture component } \item{z}{ component identities for each observation -- "indicators"} } \details{ \code{rmixGibbs} is not designed to be called directly. Instead, use \code{rnmixGibbs} wrapper function. } \value{ a list containing: \item{p}{draw mixture probabilities } \item{z}{draw of indicators of each component} \item{comps}{new draw of normal component parameters } } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Allenby, McCulloch, and Rossi, Chapter 5. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Rob McCulloch and Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rnmixGibbs}} } \keyword{ multivariate } bayesm/man/rbiNormGibbs.Rd0000644000176000001440000000242712523210210015174 0ustar ripleyusers\name{rbiNormGibbs} \alias{rbiNormGibbs} \concept{bayes} \concept{Gibbs Sampling} \concept{MCMC} \concept{normal distribution} \title{ Illustrate Bivariate Normal Gibbs Sampler } \description{ \code{rbiNormGibbs} implements a Gibbs Sampler for the bivariate normal distribution. Intermediate moves are shown and the output is contrasted with the iid sampler. i This function is designed for illustrative/teaching purposes. } \usage{ rbiNormGibbs(initx = 2, inity = -2, rho, burnin = 100, R = 500) } \arguments{ \item{initx}{ initial value of parameter on x axis (def: 2) } \item{inity}{initial value of parameter on y axis (def: -2) } \item{rho}{ correlation for bivariate normals } \item{burnin}{burn-in number of draws (def:100) } \item{R}{ number of MCMC draws (def:500) } } \details{ \eqn{(\theta_1,\theta_2) ~ N((0,0)}, \eqn{\Sigma}=matrix(c(1,rho,rho,1),ncol=2)) } \value{ R x 2 array of draws } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapters 2 and 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \examples{ ## \dontrun{ out=rbiNormGibbs(rho=.95) } } \keyword{ distribution} bayesm/man/rhierMnlDP.Rd0000644000176000001440000002257612536140666014655 0ustar ripleyusers\name{rhierMnlDP} \alias{rhierMnlDP} \concept{bayes} \concept{MCMC} \concept{Multinomial Logit} \concept{normal mixture} \concept{Dirichlet Process Prior} \concept{heterogeneity} \concept{hierarchical models} \title{ MCMC Algorithm for Hierarchical Multinomial Logit with Dirichlet Process Prior Heterogeneity} \description{ \code{rhierMnlDP} is a MCMC algorithm for a hierarchical multinomial logit with a Dirichlet Process Prior for the distribution of heteorogeneity. A base normal model is used so that the DP can be interpreted as allowing for a mixture of normals with as many components as there are panel units. This is a hybrid Gibbs Sampler with a RW Metropolis step for the MNL coefficients for each panel unit. This procedure can be interpreted as a Bayesian semi-parameteric method in the sense that the DP prior can accomodate heterogeniety of an unknown form. } \usage{ rhierMnlDP(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(p,lgtdata,Z) ( Z is optional) } \item{Prior}{ list(deltabar,Ad,Prioralpha,lambda_hyper) (all are optional)} \item{Mcmc}{ list(s,w,R,keep,nprint) (R required)} } \details{ Model: \cr \eqn{y_i} \eqn{\sim}{~} \eqn{MNL(X_i,\beta_i)}. i=1,\ldots, length(lgtdata). \eqn{\theta_i} is nvar x 1. \eqn{\beta_i}= Z\eqn{\Delta}[i,] + \eqn{u_i}. \cr Note: Z\eqn{\Delta} is the matrix Z * \eqn{\Delta}; [i,] refers to ith row of this product.\cr Delta is an nz x nvar array. \eqn{\beta_i} \eqn{\sim}{~} \eqn{N(\mu_i,\Sigma_i)}. \cr Priors: \cr \eqn{\theta_i=(\mu_i,\Sigma_i)} \eqn{\sim}{~} \eqn{DP(G_0(\lambda),alpha)}\cr \eqn{G_0(\lambda):}\cr \eqn{\mu_i | \Sigma_i} \eqn{\sim}{~} \eqn{N(0,\Sigma_i (x) a^{-1})}\cr \eqn{\Sigma_i} \eqn{\sim}{~} \eqn{IW(nu,nu*v*I)}\cr \eqn{delta= vec(\Delta)} \eqn{\sim}{~} \eqn{N(deltabar,A_d^{-1})}\cr \eqn{\lambda(a,nu,v):}\cr \eqn{a} \eqn{\sim}{~} uniform[alim[1],alimb[2]]\cr \eqn{nu} \eqn{\sim}{~} dim(data)-1 + exp(z) \cr \eqn{z} \eqn{\sim}{~} uniform[dim(data)-1+nulim[1],nulim[2]]\cr \eqn{v} \eqn{\sim}{~} uniform[vlim[1],vlim[2]] \eqn{alpha} \eqn{\sim}{~} \eqn{(1-(alpha-alphamin)/(alphamax-alphamin))^{power}} \cr alpha = alphamin then expected number of components = Istarmin \cr alpha = alphamax then expected number of components = Istarmax \cr Lists contain: \cr Data:\cr \itemize{ \item{\code{p}}{ p is number of choice alternatives} \item{\code{lgtdata}}{list of lists with each cross-section unit MNL data} \item{\code{lgtdata[[i]]$y}}{ \eqn{n_i} vector of multinomial outcomes (1,\ldots,m)} \item{\code{lgtdata[[i]]$X}}{ \eqn{n_i} by nvar design matrix for ith unit} } Prior: \cr \itemize{ \item{\code{deltabar}}{nz*nvar vector of prior means (def: 0)} \item{\code{Ad}}{ prior prec matrix for vec(D) (def: .01I)} } Prioralpha:\cr \itemize{ \item{\code{Istarmin}}{expected number of components at lower bound of support of alpha def(1)} \item{\code{Istarmax}}{expected number of components at upper bound of support of alpha (def: min(50,.1*nlgt))} \item{\code{power}}{power parameter for alpha prior (def: .8)} } lambda_hyper:\cr \itemize{ \item{\code{alim}}{defines support of a distribution (def: (.01,2))} \item{\code{nulim}}{defines support of nu distribution (def: (.01,3))} \item{\code{vlim}}{defines support of v distribution (def: (.1,4))} } Mcmc:\cr \itemize{ \item{\code{R}}{ number of mcmc draws} \item{\code{keep}}{ thinning parm, keep every keepth draw} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} \item{\code{maxuniq}}{ storage constraint on the number of unique components} \item{\code{gridsize}}{ number of discrete points for hyperparameter priors,def: 20} } } \value{ a list containing: \item{Deltadraw}{R/keep x nz*nvar matrix of draws of Delta, first row is initial value} \item{betadraw}{ nlgt x nvar x R/keep array of draws of betas} \item{nmix}{ list of 3 components, probdraw, NULL, compdraw } \item{adraw}{R/keep draws of hyperparm a} \item{vdraw}{R/keep draws of hyperparm v} \item{nudraw}{R/keep draws of hyperparm nu} \item{Istardraw}{R/keep draws of number of unique components} \item{alphadraw}{R/keep draws of number of DP tightness parameter} \item{loglike}{R/keep draws of log-likelihood} } \note{ As is well known, Bayesian density estimation involves computing the predictive distribution of a "new" unit parameter, \eqn{\theta_{n+1}} (here "n"=nlgt). This is done by averaging the normal base distribution over draws from the distribution of \eqn{\theta_{n+1}} given \eqn{\theta_1}, ..., \eqn{\theta_n},alpha,lambda,Data. To facilitate this, we store those draws from the predictive distribution of \eqn{\theta_{n+1}} in a list structure compatible with other \code{bayesm} routines that implement a finite mixture of normals. More on nmix list:\cr contains the draws from the predictive distribution of a "new" observations parameters. These are simply the parameters of one normal distribution. We enforce compatibility with a mixture of k components in order to utilize generic summary plotting functions. Therefore,\code{probdraw} is a vector of ones. \code{zdraw} (indicator draws) is omitted as it is not necessary for density estimation. \code{compdraw} contains the draws of the \eqn{\theta_{n+1}} as a list of list of lists. More on \code{compdraw} component of return value list: \itemize{ \item{compdraw[[i]]}{ith draw of components for mixtures} \item{compdraw[[i]][[1]]}{ith draw of the thetanp1} \item{compdraw[[i]][[1]][[1]]}{ith draw of mean vector} \item{compdraw[[i]][[1]][[2]]}{ith draw of parm (rooti)} } We parameterize the prior on \eqn{\Sigma_i} such that \eqn{mode(\Sigma)= nu/(nu+2) vI}. The support of nu enforces a non-degenerate IW density; \eqn{nulim[1] > 0}. The default choices of alim,nulim, and vlim determine the location and approximate size of candidate "atoms" or possible normal components. The defaults are sensible given a reasonable scaling of the X variables. You want to insure that alim is set for a wide enough range of values (remember a is a precision parameter) and the v is big enough to propose Sigma matrices wide enough to cover the data range. A careful analyst should look at the posterior distribution of a, nu, v to make sure that the support is set correctly in alim, nulim, vlim. In other words, if we see the posterior bunched up at one end of these support ranges, we should widen the range and rerun. If you want to force the procedure to use many small atoms, then set nulim to consider only large values and set vlim to consider only small scaling constants. Set alphamax to a large number. This will create a very "lumpy" density estimate somewhat like the classical Kernel density estimates. Of course, this is not advised if you have a prior belief that densities are relatively smooth. Note: Z should \strong{not} include an intercept and is centered for ease of interpretation. The mean of each of the \code{nlgt} \eqn{\beta} s is the mean of the normal mixture. Use \code{summary()} to compute this mean from the \code{compdraw} output.\cr Large R values may be required (>20,000). } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 5. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rhierMnlRwMixture}} } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=20000} else {R=10} set.seed(66) p=3 # num of choice alterns ncoef=3 nlgt=300 # num of cross sectional units nz=2 Z=matrix(runif(nz*nlgt),ncol=nz) Z=t(t(Z)-apply(Z,2,mean)) # demean Z ncomp=3 # no of mixture components Delta=matrix(c(1,0,1,0,1,2),ncol=2) comps=NULL comps[[1]]=list(mu=c(0,-1,-2),rooti=diag(rep(2,3))) comps[[2]]=list(mu=c(0,-1,-2)*2,rooti=diag(rep(2,3))) comps[[3]]=list(mu=c(0,-1,-2)*4,rooti=diag(rep(2,3))) pvec=c(.4,.2,.4) simmnlwX= function(n,X,beta) { ## simulate from MNL model conditional on X matrix k=length(beta) Xbeta=X\%*\%beta j=nrow(Xbeta)/n Xbeta=matrix(Xbeta,byrow=TRUE,ncol=j) Prob=exp(Xbeta) iota=c(rep(1,j)) denom=Prob\%*\%iota Prob=Prob/as.vector(denom) y=vector("double",n) ind=1:j for (i in 1:n) {yvec=rmultinom(1,1,Prob[i,]); y[i]=ind\%*\%yvec} return(list(y=y,X=X,beta=beta,prob=Prob)) } ## simulate data with a mixture of 3 normals simlgtdata=NULL ni=rep(50,300) for (i in 1:nlgt) { betai=Delta\%*\%Z[i,]+as.vector(rmixture(1,pvec,comps)$x) Xa=matrix(runif(ni[i]*p,min=-1.5,max=0),ncol=p) X=createX(p,na=1,nd=NULL,Xa=Xa,Xd=NULL,base=1) outa=simmnlwX(ni[i],X,betai) simlgtdata[[i]]=list(y=outa$y,X=X,beta=betai) } ## plot betas if(1){ ## set if(1) above to produce plots bmat=matrix(0,nlgt,ncoef) for(i in 1:nlgt) {bmat[i,]=simlgtdata[[i]]$beta} par(mfrow=c(ncoef,1)) for(i in 1:ncoef) hist(bmat[,i],breaks=30,col="magenta") } ## set Data and Mcmc lists keep=5 Mcmc1=list(R=R,keep=keep) Data1=list(p=p,lgtdata=simlgtdata,Z=Z) out=rhierMnlDP(Data=Data1,Mcmc=Mcmc1) cat("Summary of Delta draws",fill=TRUE) summary(out$Deltadraw,tvalues=as.vector(Delta)) if(0) { ## plotting examples plot(out$betadraw) plot(out$nmix) } } \keyword{models} bayesm/man/rhierBinLogit.Rd0000644000176000001440000001055712523211056015372 0ustar ripleyusers\name{rhierBinLogit} \alias{rhierBinLogit} \concept{bayes} \concept{MCMC} \concept{hierarchical models} \concept{binary logit} \title{ MCMC Algorithm for Hierarchical Binary Logit } \description{ \code{rhierBinLogit} implements an MCMC algorithm for hierarchical binary logits with a normal heterogeneity distribution. This is a hybrid sampler with a RW Metropolis step for unit-level logit parameters. \code{rhierBinLogit} is designed for use on choice-based conjoint data with partial profiles. The Design matrix is based on differences of characteristics between two alternatives. See Appendix A of \emph{Bayesian Statistics and Marketing} for details. } \usage{ rhierBinLogit(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(lgtdata,Z) (note: Z is optional) } \item{Prior}{ list(Deltabar,ADelta,nu,V) (note: all are optional)} \item{Mcmc}{ list(sbeta,R,keep) (note: all but R are optional)} } \details{ Model: \cr \eqn{y_{hi} = 1} with \eqn{\Pr=exp(x_{hi}'\beta_h)/(1+exp(x_{hi}'\beta_h)}. \eqn{\beta_h} is nvar x 1.\cr h=1,\ldots,length(lgtdata) units or "respondents" for survey data. \eqn{\beta_h}= ZDelta[h,] + \eqn{u_h}. \cr Note: here ZDelta refers to Z\%*\%Delta, ZDelta[h,] is hth row of this product.\cr Delta is an nz x nvar array. \eqn{u_h} \eqn{\sim}{~} \eqn{N(0,V_{beta})}. \cr Priors: \cr \eqn{delta= vec(Delta)} \eqn{\sim}{~} \eqn{N(vec(Deltabar),V_{beta} (x) ADelta^{-1})}\cr \eqn{V_{beta}} \eqn{\sim}{~} \eqn{IW(nu,V)} Lists contain: \itemize{ \item{\code{lgtdata}}{list of lists with each cross-section unit MNL data} \item{\code{lgtdata[[h]]$y}}{ \eqn{n_h} vector of binary outcomes (0,1)} \item{\code{lgtdata[[h]]$X}}{ \eqn{n_h} by nvar design matrix for hth unit} \item{\code{Deltabar}}{nz x nvar matrix of prior means (def: 0)} \item{\code{ADelta}}{ prior prec matrix (def: .01I)} \item{\code{nu}}{ d.f. parm for IW prior on norm comp Sigma (def: nvar+3)} \item{\code{V}}{ pds location parm for IW prior on norm comp Sigma (def: nuI)} \item{\code{sbeta}}{ scaling parm for RW Metropolis (def: .2)} \item{\code{R}}{ number of MCMC draws} \item{\code{keep}}{ MCMC thinning parm: keep every keepth draw (def: 1)} } } \value{ a list containing: \item{Deltadraw}{R/keep x nz*nvar matrix of draws of Delta} \item{betadraw}{ nlgt x nvar x R/keep array of draws of betas} \item{Vbetadraw}{ R/keep x nvar*nvar matrix of draws of Vbeta} \item{llike}{R/keep vector of log-like values} \item{reject}{R/keep vector of reject rates over nlgt units} } \note{ Some experimentation with the Metropolis scaling paramter (sbeta) may be required. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 5. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=10000} else {R=10} set.seed(66) nvar=5 ## number of coefficients nlgt=1000 ## number of cross-sectional units nobs=10 ## number of observations per unit nz=2 ## number of regressors in mixing distribution ## set hyper-parameters ## B=ZDelta + U Z=matrix(c(rep(1,nlgt),runif(nlgt,min=-1,max=1)),nrow=nlgt,ncol=nz) Delta=matrix(c(-2,-1,0,1,2,-1,1,-.5,.5,0),nrow=nz,ncol=nvar) iota=matrix(1,nrow=nvar,ncol=1) Vbeta=diag(nvar)+.5*iota\%*\%t(iota) ## simulate data lgtdata=NULL for (i in 1:nlgt) { beta=t(Delta)\%*\%Z[i,]+as.vector(t(chol(Vbeta))\%*\%rnorm(nvar)) X=matrix(runif(nobs*nvar),nrow=nobs,ncol=nvar) prob=exp(X\%*\%beta)/(1+exp(X\%*\%beta)) unif=runif(nobs,0,1) y=ifelse(unif max(w_{-j})} and \eqn{w_j >0} \cr if \eqn{y=p, w < 0} \cr To use GHK, we must transform so that these are rectangular regions e.g. if \eqn{y=1, w_1 > 0} and \eqn{w_1 - w_{-1} > 0}. Define \eqn{A_j} such that if j=1,\ldots,p-1, \eqn{A_jw = A_jmu + A_je > 0} is equivalent to \eqn{y=j}. Thus, if y=j, we have \eqn{A_je > -A_jmu}. Lower truncation is \eqn{-A_jmu} and \eqn{cov = A_jSigmat(A_j)}. For \eqn{j=p}, \eqn{e < - mu}. } \value{ value of log-likelihood (sum of log prob of observed multinomial outcomes). } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapters 2 and 4. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{createX}}, \code{\link{rmnpGibbs}} } \examples{ ## \dontrun{ll=llmnp(beta,Sigma,X,y,r)} } \keyword{ models } bayesm/man/rmvpGibbs.Rd0000644000176000001440000000677712523215711014577 0ustar ripleyusers\name{rmvpGibbs} \alias{rmvpGibbs} \concept{bayes} \concept{multivariate probit} \concept{MCMC} \concept{Gibbs Sampling} \title{ Gibbs Sampler for Multivariate Probit } \description{ \code{rmvpGibbs} implements the Edwards/Allenby Gibbs Sampler for the multivariate probit model. } \usage{ rmvpGibbs(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(p,y,X)} \item{Prior}{ list(betabar,A,nu,V) (optional)} \item{Mcmc}{ list(beta0,sigma0,R,keep,nprint) (R required) } } \details{ model: \cr \eqn{w_i = X_i\beta + e}. \eqn{e} \eqn{\sim}{~} N(0,\eqn{\Sigma}). note: \eqn{w_i} is p x 1.\cr \eqn{y_{ij} = 1}, if \eqn{w_{ij} > 0}, else \eqn{y_i=0}. j=1,\ldots,p. \cr priors:\cr \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})}\cr \eqn{\Sigma} \eqn{\sim}{~} IW(nu,V)\cr to make up X matrix use \code{createX} List arguments contain \itemize{ \item{\code{p}}{dimension of multivariate probit} \item{\code{X}}{n*p x k Design Matrix} \item{\code{y}}{n*p x 1 vector of 0,1 outcomes} \item{\code{betabar}}{k x 1 prior mean (def: 0)} \item{\code{A}}{k x k prior precision matrix (def: .01I)} \item{\code{nu}}{ d.f. parm for IWishart prior (def: (p-1) + 3)} \item{\code{V}}{ pds location parm for IWishart prior (def: nu*I)} \item{\code{beta0}}{ initial value for beta} \item{\code{sigma0}}{ initial value for sigma } \item{\code{R}}{ number of MCMC draws } \item{\code{keep}}{ thinning parameter - keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ a list containing: \item{betadraw }{R/keep x k array of betadraws} \item{sigmadraw}{R/keep x p*p array of sigma draws -- each row is in vector form} } \note{ beta and Sigma are not identifed. Correlation matrix and the betas divided by the appropriate standard deviation are. See Allenby et al for details or example below. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 4. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmnpGibbs}} } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) p=3 n=500 beta=c(-2,0,2) Sigma=matrix(c(1,.5,.5,.5,1,.5,.5,.5,1),ncol=3) k=length(beta) I2=diag(rep(1,p)); xadd=rbind(I2) for(i in 2:n) { xadd=rbind(xadd,I2)}; X=xadd simmvp= function(X,p,n,beta,sigma) { w=as.vector(crossprod(chol(sigma),matrix(rnorm(p*n),ncol=n)))+ X\%*\%beta y=ifelse(w<0,0,1) return(list(y=y,X=X,beta=beta,sigma=sigma)) } simout=simmvp(X,p,500,beta,Sigma) Data1=list(p=p,y=simout$y,X=simout$X) Mcmc1=list(R=R,keep=1) out=rmvpGibbs(Data=Data1,Mcmc=Mcmc1) ind=seq(from=0,by=p,length=k) inda=1:3 ind=ind+inda cat(" Betadraws ",fill=TRUE) betatilde=out$betadraw/sqrt(out$sigmadraw[,ind]) attributes(betatilde)$class="bayesm.mat" summary(betatilde,tvalues=beta/sqrt(diag(Sigma))) rdraw=matrix(double((R)*p*p),ncol=p*p) rdraw=t(apply(out$sigmadraw,1,nmat)) attributes(rdraw)$class="bayesm.var" tvalue=nmat(as.vector(Sigma)) dim(tvalue)=c(p,p) tvalue=as.vector(tvalue[upper.tri(tvalue,diag=TRUE)]) cat(" Draws of Correlation Matrix ",fill=TRUE) summary(rdraw,tvalues=tvalue) if(0){ plot(betatilde,tvalues=beta/sqrt(diag(Sigma))) } } \keyword{ models } \keyword{ multivariate } bayesm/man/logMargDenNR.Rd0000755000176000001440000000201612536435630015114 0ustar ripleyusers\name{logMargDenNR} \alias{logMargDenNR} \concept{Newton-Raftery approximation} \concept{bayes} \concept{marginal likelihood} \concept{density} \title{ Compute Log Marginal Density Using Newton-Raftery Approx } \description{ \code{logMargDenNR} computes log marginal density using the Newton-Raftery approximation.\cr Note: this approximation can be influenced by outliers in the vector of log-likelihoods. Use with \strong{care} . } \usage{ logMargDenNR(ll) } \arguments{ \item{ll}{ vector of log-likelihoods evaluated at length(ll) MCMC draws } } \value{ approximation to log marginal density value. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 6. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \keyword{ distribution } bayesm/man/rhierMnlRwMixture.Rd0000644000176000001440000001513612536140663016307 0ustar ripleyusers\name{rhierMnlRwMixture} \alias{rhierMnlRwMixture} \concept{bayes} \concept{MCMC} \concept{Multinomial Logit} \concept{mixture of normals} \concept{normal mixture} \concept{heterogeneity} \concept{hierarchical models} \title{ MCMC Algorithm for Hierarchical Multinomial Logit with Mixture of Normals Heterogeneity} \description{ \code{rhierMnlRwMixture} is a MCMC algorithm for a hierarchical multinomial logit with a mixture of normals heterogeneity distribution. This is a hybrid Gibbs Sampler with a RW Metropolis step for the MNL coefficients for each panel unit. } \usage{ rhierMnlRwMixture(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(p,lgtdata,Z) ( Z is optional) } \item{Prior}{ list(a,deltabar,Ad,mubar,Amu,nu,V,a,ncomp) (all but ncomp are optional)} \item{Mcmc}{ list(s,w,R,keep,nprint) (R required)} } \details{ Model: \cr \eqn{y_i} \eqn{\sim}{~} \eqn{MNL(X_i,\beta_i)}. i=1,\ldots, length(lgtdata). \eqn{\beta_i} is nvar x 1. \eqn{\beta_i}= Z\eqn{\Delta}[i,] + \eqn{u_i}. \cr Note: Z\eqn{\Delta} is the matrix Z * \eqn{\Delta}; [i,] refers to ith row of this product.\cr Delta is an nz x nvar array. \eqn{u_i} \eqn{\sim}{~} \eqn{N(\mu_{ind},\Sigma_{ind})}. \eqn{ind} \eqn{\sim}{~} multinomial(pvec). \cr Priors: \cr \eqn{pvec} \eqn{\sim}{~} dirichlet (a)\cr \eqn{delta= vec(\Delta)} \eqn{\sim}{~} \eqn{N(deltabar,A_d^{-1})}\cr \eqn{\mu_j} \eqn{\sim}{~} \eqn{N(mubar,\Sigma_j (x) Amu^{-1})}\cr \eqn{\Sigma_j} \eqn{\sim}{~} IW(nu,V) \cr Lists contain: \itemize{ \item{\code{p}}{ p is number of choice alternatives} \item{\code{lgtdata}}{list of lists with each cross-section unit MNL data} \item{\code{lgtdata[[i]]$y}}{ \eqn{n_i} vector of multinomial outcomes (1,\ldots,m)} \item{\code{lgtdata[[i]]$X}}{ \eqn{n_i}*p by nvar design matrix for ith unit} \item{\code{a}}{vector of length ncomp of Dirichlet prior parms (def: rep(5,ncomp))} \item{\code{deltabar}}{nz*nvar vector of prior means (def: 0)} \item{\code{Ad}}{ prior prec matrix for vec(D) (def: .01I)} \item{\code{mubar}}{ nvar x 1 prior mean vector for normal comp mean (def: 0)} \item{\code{Amu}}{ prior precision for normal comp mean (def: .01I)} \item{\code{nu}}{ d.f. parm for IW prior on norm comp Sigma (def: nvar+3)} \item{\code{V}}{ pds location parm for IW prior on norm comp Sigma (def: nuI)} \item{\code{a}}{ Dirichlet prior parameter (def: 5)} \item{\code{ncomp}}{ number of components used in normal mixture } \item{\code{s}}{ scaling parm for RW Metropolis (def: 2.93/sqrt(nvar))} \item{\code{w}}{ fractional likelihood weighting parm (def: .1)} \item{\code{R}}{ number of MCMC draws} \item{\code{keep}}{ MCMC thinning parm: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ a list containing: \item{Deltadraw}{R/keep x nz*nvar matrix of draws of Delta, first row is initial value} \item{betadraw}{ nlgt x nvar x R/keep array of draws of betas} \item{nmix}{ list of 3 components, probdraw, NULL, compdraw } \item{loglike}{ log-likelihood for each kept draw (length R/keep)} } \note{ More on \code{probdraw} component of nmix list:\cr R/keep x ncomp matrix of draws of probs of mixture components (pvec) \cr More on \code{compdraw} component of return value list: \cr \itemize{ \item{compdraw[[i]]}{ the ith draw of components for mixtures} \item{compdraw[[i]][[j]]}{ ith draw of the jth normal mixture comp} \item{compdraw[[i]][[j]][[1]]}{ ith draw of jth normal mixture comp mean vector} \item{compdraw[[i]][[j]][[2]]}{ ith draw of jth normal mixture cov parm (rooti) } } Note: Z should \strong{not} include an intercept and is centered for ease of interpretation. The mean of each of the \code{nlgt} \eqn{\beta} s is the mean of the normal mixture. Use \code{summary()} to compute this mean from the \code{compdraw} output.\cr Be careful in assessing prior parameter, Amu. .01 is too small for many applications. See Rossi et al, chapter 5 for full discussion.\cr Note: as of version 2.0-2 of \code{bayesm}, the fractional weight parameter has been changed to a weight between 0 and 1. w is the fractional weight on the normalized pooled likelihood. This differs from what is in Rossi et al chapter 5, i.e. \eqn{like_i^{(1-w)} x like_pooled^{((n_i/N)*w)}} Large R values may be required (>20,000). } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 5. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmnlIndepMetrop}} } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=10000} else {R=10} set.seed(66) p=3 # num of choice alterns ncoef=3 nlgt=300 # num of cross sectional units nz=2 Z=matrix(runif(nz*nlgt),ncol=nz) Z=t(t(Z)-apply(Z,2,mean)) # demean Z ncomp=3 # no of mixture components Delta=matrix(c(1,0,1,0,1,2),ncol=2) comps=NULL comps[[1]]=list(mu=c(0,-1,-2),rooti=diag(rep(1,3))) comps[[2]]=list(mu=c(0,-1,-2)*2,rooti=diag(rep(1,3))) comps[[3]]=list(mu=c(0,-1,-2)*4,rooti=diag(rep(1,3))) pvec=c(.4,.2,.4) simmnlwX= function(n,X,beta) { ## simulate from MNL model conditional on X matrix k=length(beta) Xbeta=X\%*\%beta j=nrow(Xbeta)/n Xbeta=matrix(Xbeta,byrow=TRUE,ncol=j) Prob=exp(Xbeta) iota=c(rep(1,j)) denom=Prob\%*\%iota Prob=Prob/as.vector(denom) y=vector("double",n) ind=1:j for (i in 1:n) {yvec=rmultinom(1,1,Prob[i,]); y[i]=ind\%*\%yvec} return(list(y=y,X=X,beta=beta,prob=Prob)) } ## simulate data simlgtdata=NULL ni=rep(50,300) for (i in 1:nlgt) { betai=Delta\%*\%Z[i,]+as.vector(rmixture(1,pvec,comps)$x) Xa=matrix(runif(ni[i]*p,min=-1.5,max=0),ncol=p) X=createX(p,na=1,nd=NULL,Xa=Xa,Xd=NULL,base=1) outa=simmnlwX(ni[i],X,betai) simlgtdata[[i]]=list(y=outa$y,X=X,beta=betai) } ## plot betas if(0){ ## set if(1) above to produce plots bmat=matrix(0,nlgt,ncoef) for(i in 1:nlgt) {bmat[i,]=simlgtdata[[i]]$beta} par(mfrow=c(ncoef,1)) for(i in 1:ncoef) hist(bmat[,i],breaks=30,col="magenta") } ## set parms for priors and Z Prior1=list(ncomp=5) keep=5 Mcmc1=list(R=R,keep=keep) Data1=list(p=p,lgtdata=simlgtdata,Z=Z) out=rhierMnlRwMixture(Data=Data1,Prior=Prior1,Mcmc=Mcmc1) cat("Summary of Delta draws",fill=TRUE) summary(out$Deltadraw,tvalues=as.vector(Delta)) cat("Summary of Normal Mixture Distribution",fill=TRUE) summary(out$nmix) if(0) { ## plotting examples plot(out$betadraw) plot(out$nmix) } } \keyword{models} bayesm/man/customerSat.Rd0000644000176000001440000000230012536435535015143 0ustar ripleyusers\name{customerSat} \alias{customerSat} \docType{data} \title{ Customer Satisfaction Data} \description{ Responses to a satisfaction survey for a Yellow Pages advertising product. All responses are on a 10 point scale from 1 to 10 (10 is "Excellent" and 1 is "Poor") } \usage{data(customerSat)} \format{ A data frame with 1811 observations on the following 10 variables. \describe{ \item{\code{q1}}{Overall Satisfaction} \item{\code{q2}}{Setting Competitive Prices} \item{\code{q3}}{Holding Price Increase to a Minimum} \item{\code{q4}}{Appropriate Pricing given Volume} \item{\code{q5}}{Demonstrating Effectiveness of Purchase} \item{\code{q6}}{Reach a Large # of Customers} \item{\code{q7}}{Reach of Advertising} \item{\code{q8}}{Long-term Exposure} \item{\code{q9}}{Distribution} \item{\code{q10}}{Distribution to Right Geographic Areas} } } \source{ Rossi et al (2001), "Overcoming Scale Usage Heterogeneity," \emph{JASA} 96, 20-31. } \references{ Case Study 3, \emph{Bayesian Statistics and Marketing} by Rossi et al.\cr \url{http://www.perossi.org/home/bsm-1} } \examples{ data(customerSat) apply(as.matrix(customerSat),2,table) } \keyword{datasets} bayesm/man/margarine.Rd0000644000176000001440000001036012523034265014573 0ustar ripleyusers\name{margarine} \alias{margarine} \docType{data} \title{Household Panel Data on Margarine Purchases} \description{ Panel data on purchases of margarine by 516 households. Demographic variables are included. } \usage{data(margarine)} \format{ This is an R object that is a list of two data frames, list(choicePrice,demos) List of 2 \cr $ choicePrice:`data.frame': 4470 obs. of 12 variables:\cr \ldots $ hhid : int [1:4470] 2100016 2100016 2100016 2100016 \cr \ldots $ choice : num [1:4470] 1 1 1 1 1 4 1 1 4 1 \cr \ldots $ PPk_Stk : num [1:4470] 0.66 0.63 0.29 0.62 0.5 0.58 0.29 \cr \ldots $ PBB_Stk : num [1:4470] 0.67 0.67 0.5 0.61 0.58 0.45 0.51 \cr \ldots $ PFl_Stk : num [1:4470] 1.09 0.99 0.99 0.99 0.99 0.99 0.99 \cr \ldots $ PHse_Stk: num [1:4470] 0.57 0.57 0.57 0.57 0.45 0.45 0.29 \cr \ldots $ PGen_Stk: num [1:4470] 0.36 0.36 0.36 0.36 0.33 0.33 0.33 \cr \ldots $ PImp_Stk: num [1:4470] 0.93 1.03 0.69 0.75 0.72 0.72 0.72 \cr \ldots $ PSS_Tub : num [1:4470] 0.85 0.85 0.79 0.85 0.85 0.85 0.85 \cr \ldots $ PPk_Tub : num [1:4470] 1.09 1.09 1.09 1.09 1.07 1.07 1.07 \cr \ldots $ PFl_Tub : num [1:4470] 1.19 1.19 1.19 1.19 1.19 1.19 1.19 \cr \ldots $ PHse_Tub: num [1:4470] 0.33 0.37 0.59 0.59 0.59 0.59 0.59 \cr Pk is Parkay; BB is BlueBonnett, Fl is Fleischmanns, Hse is house, Gen is generic, Imp is Imperial, SS is Shed Spread. _Stk indicates stick, _Tub indicates Tub form. $ demos :`data.frame': 516 obs. of 8 variables:\cr \ldots $ hhid : num [1:516] 2100016 2100024 2100495 2100560 \cr \ldots $ Income : num [1:516] 32.5 17.5 37.5 17.5 87.5 12.5 \cr \ldots $ Fs3_4 : int [1:516] 0 1 0 0 0 0 0 0 0 0 \cr \ldots $ Fs5 : int [1:516] 0 0 0 0 0 0 0 0 1 0 \cr \ldots $ Fam_Size : int [1:516] 2 3 2 1 1 2 2 2 5 2 \cr \ldots $ college : int [1:516] 1 1 0 0 1 0 1 0 1 1 \cr \ldots $ whtcollar: int [1:516] 0 1 0 1 1 0 0 0 1 1 \cr \ldots $ retired : int [1:516] 1 1 1 0 0 1 0 1 0 0 \cr Fs3_4 is dummy (family size 3-4). Fs5 is dummy for family size >= 5. college,whtcollar,retired are dummies reflecting these statuses. } \details{ choice is a multinomial indicator of one of the 10 brands (in order listed under format). All prices are in $. } \source{ Allenby and Rossi (1991), "Quality Perceptions and Asymmetric Switching Between Brands," \emph{Marketing Science} 10, 185-205. } \references{ Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi et al.\cr \url{http://www.perossi.org/home/bsm-1} } \examples{ data(margarine) cat(" Table of Choice Variable ",fill=TRUE) print(table(margarine$choicePrice[,2])) cat(" Means of Prices",fill=TRUE) mat=apply(as.matrix(margarine$choicePrice[,3:12]),2,mean) print(mat) cat(" Quantiles of Demographic Variables",fill=TRUE) mat=apply(as.matrix(margarine$demos[,2:8]),2,quantile) print(mat) ## ## example of processing for use with rhierMnlRwMixture ## if(0) { select= c(1:5,7) ## select brands chPr=as.matrix(margarine$choicePrice) ## make sure to log prices chPr=cbind(chPr[,1],chPr[,2],log(chPr[,2+select])) demos=as.matrix(margarine$demos[,c(1,2,5)]) ## remove obs for other alts chPr=chPr[chPr[,2] <= 7,] chPr=chPr[chPr[,2] != 6,] ## recode choice chPr[chPr[,2] == 7,2]=6 hhidl=levels(as.factor(chPr[,1])) lgtdata=NULL nlgt=length(hhidl) p=length(select) ## number of choice alts ind=1 for (i in 1:nlgt) { nobs=sum(chPr[,1]==hhidl[i]) if(nobs >=5) { data=chPr[chPr[,1]==hhidl[i],] y=data[,2] names(y)=NULL X=createX(p=p,na=1,Xa=data[,3:8],nd=NULL,Xd=NULL,INT=TRUE,base=1) lgtdata[[ind]]=list(y=y,X=X,hhid=hhidl[i]); ind=ind+1 } } nlgt=length(lgtdata) ## ## now extract demos corresponding to hhs in lgtdata ## Z=NULL nlgt=length(lgtdata) for(i in 1:nlgt){ Z=rbind(Z,demos[demos[,1]==lgtdata[[i]]$hhid,2:3]) } ## ## take log of income and family size and demean ## Z=log(Z) Z[,1]=Z[,1]-mean(Z[,1]) Z[,2]=Z[,2]-mean(Z[,2]) keep=5 R=20000 mcmc1=list(keep=keep,R=R) out=rhierMnlRwMixture(Data=list(p=p,lgtdata=lgtdata,Z=Z),Prior=list(ncomp=1),Mcmc=mcmc1) summary(out$Deltadraw) summary(out$nmix) if(0){ ## plotting examples plot(out$nmix) plot(out$Deltadraw)} } } \keyword{datasets} bayesm/man/summary.bayesm.var.Rd0000644000176000001440000000340312524764574016407 0ustar ripleyusers\name{summary.bayesm.var} \alias{summary.bayesm.var} \title{Summarize Draws of Var-Cov Matrices} \description{ \code{summary.bayesm.var} is an S3 method to summarize marginal distributions given an array of draws } \usage{ \method{summary}{bayesm.var}(object, names, burnin = trunc(0.1 * nrow(Vard)), tvalues, QUANTILES = FALSE , ...) } \arguments{ \item{object}{ \code{object} (herafter, \code{Vard}) is an array of draws of a covariance matrix } \item{names}{ optional character vector of names for the columns of \code{Vard}} \item{burnin}{ number of draws to burn-in (def: .1*nrow(Vard))} \item{tvalues}{ optional vector of "true" values for use in simulation examples } \item{QUANTILES}{ logical for should quantiles be displayed (def: TRUE)} \item{...}{ optional arguments for generic function } } \details{ Typically, \code{summary.bayesm.var} will be invoked by a call to the generic summary function as in summary(object) where object is of class bayesm.var. Mean, Std Dev, Numerical Standard error (of estimate of posterior mean), relative numerical efficiency (see \code{numEff}) and effective sample size are displayed. If QUANTILES=TRUE, quantiles of marginal distirbutions in the columns of Vard are displayed. \cr \cr \code{Vard} is an array of draws of a covariance matrix stored as vectors. Each row is a different draw. \cr The posterior mean of the vector of standard deviations and the correlation matrix are also displayed } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{summary.bayesm.mat}}, \code{\link{summary.bayesm.nmix}}} \examples{ ## ## not run # out=rmnpGibbs(Data,Prior,Mcmc) # summary(out$sigmadraw) # } \keyword{ univar } bayesm/man/summary.bayesm.nmix.Rd0000644000176000001440000000270612523217754016567 0ustar ripleyusers\name{summary.bayesm.nmix} \alias{summary.bayesm.nmix} \title{Summarize Draws of Normal Mixture Components } \description{ \code{summary.bayesm.nmix} is an S3 method to display summaries of the distribution implied by draws of Normal Mixture Components. Posterior means and Variance-Covariance matrices are displayed.\cr \cr Note: 1st and 2nd moments may not be very interpretable for mixtures of normals. This summary function can take a minute or so. The current implementation is not efficient. } \usage{ \method{summary}{bayesm.nmix}(object, names,burnin = trunc(0.1 * nrow(probdraw)), ...) } \arguments{ \item{object}{ an object of class "bayesm.nmix" -- a list of lists of draws} \item{names}{ optional character vector of names fo reach dimension of the density} \item{burnin}{ number of draws to burn-in (def: .1*nrow(probdraw))} \item{...}{ parms to send to summary} } \details{ an object of class "bayesm.nmix" is a list of three components: \describe{ \item{probdraw}{ a matrix of R/keep rows by dim of normal mix of mixture prob draws} \item{second comp}{ not used} \item{compdraw}{ list of list of lists with draws of mixture comp parms} } } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{summary.bayesm.mat}}, \code{\link{summary.bayesm.var}}} \examples{ ## ## not run # out=rnmix(Data,Prior,Mcmc) # summary(out) # } \keyword{ plot } bayesm/man/plot.bayesm.mat.Rd0000644000176000001440000000377012523207637015657 0ustar ripleyusers\name{plot.bayesm.mat} \alias{plot.bayesm.mat} \concept{MCMC} \concept{S3 method} \concept{plot} \title{Plot Method for Arrays of MCMC Draws} \description{ \code{plot.bayesm.mat} is an S3 method to plot arrays of MCMC draws. The columns in the array correspond to parameters and the rows to MCMC draws. } \usage{ \method{plot}{bayesm.mat}(x,names,burnin,tvalues,TRACEPLOT,DEN,INT,CHECK_NDRAWS, ...) } \arguments{ \item{x}{ An object of either S3 class, bayesm.mat, or S3 class, mcmc } \item{names}{optional character vector of names for coefficients} \item{burnin}{number of draws to discard for burn-in (def: .1*nrow(X))} \item{tvalues}{vector of true values} \item{TRACEPLOT}{ logical, TRUE provide sequence plots of draws and acfs (def: TRUE)} \item{DEN}{ logical, TRUE use density scale on histograms (def: TRUE)} \item{INT}{ logical, TRUE put various intervals and points on graph (def: TRUE)} \item{CHECK_NDRAWS}{ logical, TRUE check that there are at least 100 draws (def: TRUE)} \item{...}{ standard graphics parameters } } \details{ Typically, \code{plot.bayesm.mat} will be invoked by a call to the generic plot function as in \code{plot(object)} where object is of class bayesm.mat. All of the \code{bayesm} MCMC routines return draws in this class (see example below). One can also simply invoke \code{plot.bayesm.mat} on any valid 2-dim array as in \code{plot.bayesm.mat(betadraws)}. \cr \cr \code{plot.bayesm.mat} paints (by default) on the histogram: \cr \cr green "[]" delimiting 95\% Bayesian Credibility Interval \cr yellow "()" showing +/- 2 numerical standard errors \cr red "|" showing posterior mean \cr \cr \code{plot.bayesm.mat} is also exported for use as a standard function, as in \code{plot.bayesm.mat(matrix)} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \examples{ ## ## not run # out=runiregGibbs(Data,Prior,Mcmc) # plot(out$betadraw) # } \keyword{ hplot } bayesm/man/rsurGibbs.Rd0000644000176000001440000000570312523217074014576 0ustar ripleyusers\name{rsurGibbs} \alias{rsurGibbs} \concept{bayes} \concept{Gibbs Sampler} \concept{regression} \concept{SUR model} \concept{Seemingly Unrelated Regression} \concept{MCMC} \title{ Gibbs Sampler for Seemingly Unrelated Regressions (SUR) } \description{ \code{rsurGibbs} implements a Gibbs Sampler to draw from the posterior of the Seemingly Unrelated Regression (SUR) Model of Zellner } \usage{ rsurGibbs(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(regdata)} \item{Prior}{ list(betabar,A, nu, V) } \item{Mcmc}{ list(R,keep)} } \details{ Model: \eqn{y_i = X_i\beta_i + e_i}. i=1,\ldots,m. m regressions. \cr (e(1,k), \ldots, e(m,k)) \eqn{\sim}{~} \eqn{N(0,\Sigma)}. k=1, \ldots, nobs. We can also write as the stacked model: \cr \eqn{y = X\beta + e} where y is a nobs*m long vector and k=length(beta)=sum(length(betai)). Note: we must have the same number of observations in each equation but we can have different numbers of X variables Priors: \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})}. \eqn{\Sigma} \eqn{\sim}{~} \eqn{IW(nu,V)}. List arguments contain \itemize{ \item{\code{regdata}}{list of lists, regdata[[i]]=list(y=yi,X=Xi)} \item{\code{betabar}}{k x 1 prior mean (def: 0)} \item{\code{A}}{k x k prior precision matrix (def: .01I)} \item{\code{nu}}{ d.f. parm for Inverted Wishart prior (def: m+3)} \item{\code{V}}{ scale parm for Inverted Wishart prior (def: nu*I)} \item{\code{R}}{ number of MCMC draws } \item{\code{keep}}{ thinning parameter - keep every keepth draw } \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ list of MCMC draws \item{betadraw }{ R x k array of betadraws } \item{Sigmadraw }{ R x (m*m) array of Sigma draws} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmultireg}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10} ## ## simulate data from SUR set.seed(66) beta1=c(1,2) beta2=c(1,-1,-2) nobs=100 nreg=2 iota=c(rep(1,nobs)) X1=cbind(iota,runif(nobs)) X2=cbind(iota,runif(nobs),runif(nobs)) Sigma=matrix(c(.5,.2,.2,.5),ncol=2) U=chol(Sigma) E=matrix(rnorm(2*nobs),ncol=2)\%*\%U y1=X1\%*\%beta1+E[,1] y2=X2\%*\%beta2+E[,2] ## ## run Gibbs Sampler regdata=NULL regdata[[1]]=list(y=y1,X=X1) regdata[[2]]=list(y=y2,X=X2) Mcmc1=list(R=R) out=rsurGibbs(Data=list(regdata=regdata),Mcmc=Mcmc1) cat("Summary of beta draws",fill=TRUE) summary(out$betadraw,tvalues=c(beta1,beta2)) cat("Summary of Sigmadraws",fill=TRUE) summary(out$Sigmadraw,tvalues=as.vector(Sigma[upper.tri(Sigma,diag=TRUE)])) if(0){ plot(out$betadraw,tvalues=c(beta1,beta2)) } } \keyword{ regression} bayesm/man/llmnl.Rd0000644000176000001440000000246012523206766013755 0ustar ripleyusers\name{llmnl} \alias{llmnl} \concept{multinomial logit} \concept{likelihood} \title{ Evaluate Log Likelihood for Multinomial Logit Model } \description{ \code{llmnl} evaluates log-likelihood for the multinomial logit model. } \usage{ llmnl(beta,y, X) } \arguments{ \item{beta}{ k x 1 coefficient vector } \item{y}{ n x 1 vector of obs on y (1,\ldots, p) } \item{X}{ n*p x k Design matrix (use \code{createX} to make) } } \details{ Let \eqn{\mu_i=X_i beta}, then \eqn{Pr(y_i=j) = exp(\mu_{i,j})/\sum_kexp(\mu_{i,k})}.\cr \eqn{X_i} is the submatrix of X corresponding to the ith observation. X has n*p rows. Use \code{\link{createX}} to create X. } \value{ value of log-likelihood (sum of log prob of observed multinomial outcomes). } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{createX}}, \code{\link{rmnlIndepMetrop}} } \examples{ ## \dontrun{ll=llmnl(beta,y,X)} } \keyword{ models } bayesm/man/bank.Rd0000644000176000001440000000766512536435573013572 0ustar ripleyusers\name{bank} \alias{bank} \docType{data} \title{ Bank Card Conjoint Data of Allenby and Ginter (1995)} \description{ Data from a conjoint experiment in which two partial profiles of credit cards were presented to 946 respondents. The variable bank$choiceAtt$choice indicates which profile was chosen. The profiles are coded as the difference in attribute levels. Thus, a "-1" means the profile coded as a choice of "0" has the attribute. A value of 0 means that the attribute was not present in the comparison. data on age,income and gender (female=1) are also recorded in bank$demo } \usage{data(bank)} \format{ This R object is a list of two data frames, list(choiceAtt,demo). List of 2 $ choiceAtt:`data.frame': 14799 obs. of 16 variables:\cr \ldots$ id : int [1:14799] 1 1 1 1 1 1 1 1 1 1 \cr \ldots$ choice : int [1:14799] 1 1 1 1 1 1 1 1 0 1 \cr \ldots$ Med_FInt : int [1:14799] 1 1 1 0 0 0 0 0 0 0 \cr \ldots$ Low_FInt : int [1:14799] 0 0 0 0 0 0 0 0 0 0 \cr \ldots$ Med_VInt : int [1:14799] 0 0 0 0 0 0 0 0 0 0 \cr \ldots$ Rewrd_2 : int [1:14799] -1 1 0 0 0 0 0 1 -1 0 \cr \ldots$ Rewrd_3 : int [1:14799] 0 -1 1 0 0 0 0 0 1 -1 \cr \ldots$ Rewrd_4 : int [1:14799] 0 0 -1 0 0 0 0 0 0 1 \cr \ldots$ Med_Fee : int [1:14799] 0 0 0 1 1 -1 -1 0 0 0 \cr \ldots$ Low_Fee : int [1:14799] 0 0 0 0 0 1 1 0 0 0 \cr \ldots$ Bank_B : int [1:14799] 0 0 0 -1 1 -1 1 0 0 0 \cr \ldots$ Out_State : int [1:14799] 0 0 0 0 -1 0 -1 0 0 0 \cr \ldots$ Med_Rebate : int [1:14799] 0 0 0 0 0 0 0 0 0 0 \cr \ldots$ High_Rebate : int [1:14799] 0 0 0 0 0 0 0 0 0 0 \cr \ldots$ High_CredLine: int [1:14799] 0 0 0 0 0 0 0 -1 -1 -1 \cr \ldots$ Long_Grace : int [1:14799] 0 0 0 0 0 0 0 0 0 0 $ demo :`data.frame': 946 obs. of 4 variables:\cr \ldots$ id : int [1:946] 1 2 3 4 6 7 8 9 10 11 \cr \ldots$ age : int [1:946] 60 40 75 40 30 30 50 50 50 40 \cr \ldots$ income: int [1:946] 20 40 30 40 30 60 50 100 50 40 \cr \ldots$ gender: int [1:946] 1 1 0 0 0 0 1 0 0 0 \cr } \details{ Each respondent was presented with between 13 and 17 paired comparisons. Thus, this dataset has a panel structure. } \source{ Allenby and Ginter (1995), "Using Extremes to Design Products and Segment Markets," \emph{JMR}, 392-403. } \references{ Appendix A, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \examples{ data(bank) cat(" table of Binary Dep Var", fill=TRUE) print(table(bank$choiceAtt[,2])) cat(" table of Attribute Variables",fill=TRUE) mat=apply(as.matrix(bank$choiceAtt[,3:16]),2,table) print(mat) cat(" means of Demographic Variables",fill=TRUE) mat=apply(as.matrix(bank$demo[,2:3]),2,mean) print(mat) ## example of processing for use with rhierBinLogit ## if(0) { choiceAtt=bank$choiceAtt Z=bank$demo ## center demo data so that mean of random-effects ## distribution can be interpreted as the average respondent Z[,1]=rep(1,nrow(Z)) Z[,2]=Z[,2]-mean(Z[,2]) Z[,3]=Z[,3]-mean(Z[,3]) Z[,4]=Z[,4]-mean(Z[,4]) Z=as.matrix(Z) hh=levels(factor(choiceAtt$id)) nhh=length(hh) lgtdata=NULL for (i in 1:nhh) { y=choiceAtt[choiceAtt[,1]==hh[i],2] nobs=length(y) X=as.matrix(choiceAtt[choiceAtt[,1]==hh[i],c(3:16)]) lgtdata[[i]]=list(y=y,X=X) } cat("Finished Reading data",fill=TRUE) fsh() Data=list(lgtdata=lgtdata,Z=Z) Mcmc=list(R=10000,sbeta=0.2,keep=20) set.seed(66) out=rhierBinLogit(Data=Data,Mcmc=Mcmc) begin=5000/20 end=10000/20 summary(out$Deltadraw,burnin=begin) summary(out$Vbetadraw,burnin=begin) if(0){ ## plotting examples ## plot grand means of random effects distribution (first row of Delta) index=4*c(0:13)+1 matplot(out$Deltadraw[,index],type="l",xlab="Iterations/20",ylab="", main="Average Respondent Part-Worths") ## plot hierarchical coefs plot(out$betadraw) ## plot log-likelihood plot(out$llike,type="l",xlab="Iterations/20",ylab="",main="Log Likelihood") } } } \keyword{datasets} bayesm/man/rmixture.Rd0000644000176000001440000000223712523214072014505 0ustar ripleyusers\name{rmixture} \alias{rmixture} \concept{mixture of normals} \concept{simulation} \title{ Draw from Mixture of Normals } \description{ \code{rmixture} simulates iid draws from a Multivariate Mixture of Normals } \usage{ rmixture(n, pvec, comps) } \arguments{ \item{n}{ number of observations } \item{pvec}{ ncomp x 1 vector of prior probabilities for each mixture component } \item{comps}{ list of mixture component parameters } } \details{ comps is a list of length, ncomp = length(pvec). comps[[j]][[1]] is mean vector for the jth component. comps[[j]][[2]] is the inverse of the cholesky root of \eqn{\Sigma} for that component } \value{ A list containing \ldots \item{x}{ An n x length(comps[[1]][[1]]) array of iid draws } \item{z}{ A n x 1 vector of indicators of which component each draw is taken from } } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rnmixGibbs}} } \keyword{ distribution } \keyword{ multivariate } bayesm/man/rDPGibbs.Rd0000644000176000001440000001771512523212113014263 0ustar ripleyusers\name{rDPGibbs} \alias{rDPGibbs} \concept{bayes} \concept{MCMC} \concept{normal mixtures} \concept{Dirichlet Process} \concept{Gibbs Sampling} \title{ Density Estimation with Dirichlet Process Prior and Normal Base } \description{ \code{rDPGibbs} implements a Gibbs Sampler to draw from the posterior for a normal mixture problem with a Dirichlet Process prior. A natural conjugate base prior is used along with priors on the hyper parameters of this distribution. One interpretation of this model is as a normal mixture with a random number of components that can grow with the sample size. } \usage{ rDPGibbs(Prior, Data, Mcmc) } \arguments{ \item{Prior}{ list(Prioralpha,lambda_hyper) } \item{Data}{ list(y) } \item{Mcmc}{ list(R,keep,nprint,maxuniq,SCALE,gridsize) } } \details{ Model: \cr \eqn{y_i} \eqn{\sim}{~} \eqn{N(\mu_i,\Sigma_i)}. \cr Priors:\cr \eqn{\theta_i=(\mu_i,\Sigma_i)} \eqn{\sim}{~} \eqn{DP(G_0(\lambda),alpha)}\cr \eqn{G_0(\lambda):}\cr \eqn{\mu_i | \Sigma_i} \eqn{\sim}{~} \eqn{N(0,\Sigma_i (x) a^{-1})}\cr \eqn{\Sigma_i} \eqn{\sim}{~} \eqn{IW(nu,nu*v*I)} \eqn{\lambda(a,nu,v):}\cr \eqn{a} \eqn{\sim}{~} uniform on grid[alim[1],alimb[2]]\cr \eqn{nu} \eqn{\sim}{~} uniform on grid[dim(data)-1 + exp(nulim[1]),dim(data)-1 +exp(nulim[2])]\cr \eqn{v} \eqn{\sim}{~} uniform on grid[vlim[1],vlim[2]] \eqn{alpha} \eqn{\sim}{~} \eqn{(1-(\alpha-alphamin)/(alphamax-alphamin))^{power}} \cr \eqn{alpha}= alphamin then expected number of components = Istarmin \cr \eqn{alpha}= alphamax then expected number of components = Istarmax \cr List arguments contain: Data:\cr \itemize{ \item{\code{y}}{N x k matrix of observations on k dimensional data} } Prioralpha:\cr \itemize{ \item{\code{Istarmin}}{ expected number of components at lower bound of support of alpha (def: 1)} \item{\code{Istarmax}}{ expected number of components at upper bound of support of alpha} \item{\code{power}}{ power parameter for alpha prior (def: .8)} } lambda_hyper:\cr \itemize{ \item{\code{alim}}{ defines support of a distribution (def: (.01,10))} \item{\code{nulim}}{ defines support of nu distribution (def: (.01,3))} \item{\code{vlim}}{ defines support of v distribution (def: (.1,4))} } Mcmc:\cr \itemize{ \item{\code{R}}{ number of mcmc draws} \item{\code{keep}}{ thinning parm, keep every keepth draw} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} \item{\code{maxuniq}}{ storage constraint on the number of unique components (def: 200)} \item{\code{SCALE}}{ should data be scaled by mean,std deviation before posterior draws, (def: TRUE)} \item{\code{gridsize}}{ number of discrete points for hyperparameter priors,def: 20} } the basic output are draws from the predictive distribution of the data in the object, \code{nmix}. The average of these draws is the Bayesian analogue of a density estimate. nmix:\cr \itemize{ \item{\code{probdraw}}{ R/keep x 1 matrix of 1s} \item{\code{zdraw}}{ R/keep x N matrix of draws of indicators of which component each obs is assigned to} \item{\code{compdraw}}{ R/keep list of draws of normals} } Output of the components is in the form of a list of lists. \cr compdraw[[i]] is ith draw -- list of lists. \cr compdraw[[i]][[1]] is list of parms for a draw from predictive. \cr compdraw[[i]][1]][[1]] is the mean vector. compdraw[[i]][[1]][[2]] is the inverse of Cholesky root. \eqn{\Sigma} = t(R)\%*\%R, \eqn{R^{-1}} = compdraw[[i]][[1]][[2]]. } \note{ we parameterize the prior on \eqn{\Sigma_i} such that \eqn{mode(\Sigma)= nu/(nu+2) vI}. The support of nu enforces valid IW density; \eqn{nulim[1] > 0} We use the structure for \code{nmix} that is compatible with the \code{bayesm} routines for finite mixtures of normals. This allows us to use the same summary and plotting methods. The default choices of alim,nulim, and vlim determine the location and approximate size of candidate "atoms" or possible normal components. The defaults are sensible given that we scale the data. Without scaling, you want to insure that alim is set for a wide enough range of values (remember a is a precision parameter) and the v is big enough to propose Sigma matrices wide enough to cover the data range. A careful analyst should look at the posterior distribution of a, nu, v to make sure that the support is set correctly in alim, nulim, vlim. In other words, if we see the posterior bunched up at one end of these support ranges, we should widen the range and rerun. If you want to force the procedure to use many small atoms, then set nulim to consider only large values and set vlim to consider only small scaling constants. Set Istarmax to a large number. This will create a very "lumpy" density estimate somewhat like the classical Kernel density estimates. Of course, this is not advised if you have a prior belief that densities are relatively smooth. } \value{ \item{nmix}{ a list containing: probdraw,zdraw,compdraw} \item{alphadraw}{ vector of draws of DP process tightness parameter} \item{nudraw}{ vector of draws of base prior hyperparameter} \item{adraw}{ vector of draws of base prior hyperparameter} \item{vdraw}{ vector of draws of base prior hyperparameter} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rnmixGibbs}},\code{\link{rmixture}}, \code{\link{rmixGibbs}} , \code{\link{eMixMargDen}}, \code{\link{momMix}}, \code{\link{mixDen}}, \code{\link{mixDenBi}}} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} ## simulate univariate data from Chi-Sq set.seed(66) N=200 chisqdf=8; y1=as.matrix(rchisq(N,df=chisqdf)) ## set arguments for rDPGibbs Data1=list(y=y1) Prioralpha=list(Istarmin=1,Istarmax=10,power=.8) Prior1=list(Prioralpha=Prioralpha) Mcmc=list(R=R,keep=1,maxuniq=200) out1=rDPGibbs(Prior=Prior1,Data=Data1,Mcmc) if(0){ ## plotting examples rgi=c(0,20); grid=matrix(seq(from=rgi[1],to=rgi[2],length.out=50),ncol=1) deltax=(rgi[2]-rgi[1])/nrow(grid) plot(out1$nmix,Grid=grid,Data=y1) ## plot true density with historgram plot(range(grid[,1]),1.5*range(dchisq(grid[,1],df=chisqdf)), type="n",xlab=paste("Chisq ; ",N," obs",sep=""), ylab="") hist(y1,xlim=rgi,freq=FALSE,col="yellow",breaks=20,add=TRUE) lines(grid[,1],dchisq(grid[,1],df=chisqdf)/ (sum(dchisq(grid[,1],df=chisqdf))*deltax),col="blue",lwd=2) } ## simulate bivariate data from the "Banana" distribution (Meng and Barnard) banana=function(A,B,C1,C2,N,keep=10,init=10) { R=init*keep+N*keep x1=x2=0 bimat=matrix(double(2*N),ncol=2) for (r in 1:R) { x1=rnorm(1,mean=(B*x2+C1)/(A*(x2^2)+1),sd=sqrt(1/(A*(x2^2)+1))) x2=rnorm(1,mean=(B*x2+C2)/(A*(x1^2)+1),sd=sqrt(1/(A*(x1^2)+1))) if (r>init*keep && r\%\%keep==0) {mkeep=r/keep; bimat[mkeep-init,]=c(x1,x2)} } return(bimat) } set.seed(66) nvar2=2 A=0.5; B=0; C1=C2=3 y2=banana(A=A,B=B,C1=C1,C2=C2,1000) Data2=list(y=y2) Prioralpha=list(Istarmin=1,Istarmax=10,power=.8) Prior2=list(Prioralpha=Prioralpha) Mcmc=list(R=R,keep=1,maxuniq=200) out2=rDPGibbs(Prior=Prior2,Data=Data2,Mcmc) if(0){ ## plotting examples rx1=range(y2[,1]); rx2=range(y2[,2]) x1=seq(from=rx1[1],to=rx1[2],length.out=50) x2=seq(from=rx2[1],to=rx2[2],length.out=50) grid=cbind(x1,x2) plot(out2$nmix,Grid=grid,Data=y2) ## plot true bivariate density tden=matrix(double(50*50),ncol=50) for (i in 1:50){ for (j in 1:50) {tden[i,j]=exp(-0.5*(A*(x1[i]^2)*(x2[j]^2)+ (x1[i]^2)+(x2[j]^2)-2*B*x1[i]*x2[j]-2*C1*x1[i]-2*C2*x2[j]))} } tden=tden/sum(tden) image(x1,x2,tden,col=terrain.colors(100),xlab="",ylab="") contour(x1,x2,tden,add=TRUE,drawlabels=FALSE) title("True Density") } } \keyword{ multivariate } bayesm/man/createX.Rd0000755000176000001440000000424511566563253014243 0ustar ripleyusers\name{createX} \alias{createX} \concept{multinomial logit} \concept{multinomial probit} \title{ Create X Matrix for Use in Multinomial Logit and Probit Routines } \description{ \code{createX} makes up an X matrix in the form expected by Multinomial Logit (\code{\link{rmnlIndepMetrop}} and \code{\link{rhierMnlRwMixture}}) and Probit (\code{\link{rmnpGibbs}} and \code{\link{rmvpGibbs}}) routines. Requires an array of alternative specific variables and/or an array of "demographics" or variables constant across alternatives which may vary across choice occasions. } \usage{ createX(p, na, nd, Xa, Xd, INT = TRUE, DIFF = FALSE, base = p) } \arguments{ \item{p}{ integer - number of choice alternatives } \item{na}{ integer - number of alternative-specific vars in Xa } \item{nd}{ integer - number of non-alternative specific vars } \item{Xa}{ n x p*na matrix of alternative-specific vars } \item{Xd}{ n x nd matrix of non-alternative specific vars } \item{INT}{ logical flag for inclusion of intercepts } \item{DIFF}{ logical flag for differencing wrt to base alternative } \item{base}{ integer - index of base choice alternative } note: na,nd,Xa,Xd can be NULL to indicate lack of Xa or Xd variables. } \value{ X matrix -- n*(p-DIFF) x [(INT+nd)*(p-1) + na] matrix. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \note{ \code{\link{rmnpGibbs}} assumes that the base alternative is the default. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rmnlIndepMetrop}}, \code{\link{rmnpGibbs}} } \examples{ na=2; nd=1; p=3 vec=c(1,1.5,.5,2,3,1,3,4.5,1.5) Xa=matrix(vec,byrow=TRUE,ncol=3) Xa=cbind(Xa,-Xa) Xd=matrix(c(-1,-2,-3),ncol=1) createX(p=p,na=na,nd=nd,Xa=Xa,Xd=Xd) createX(p=p,na=na,nd=nd,Xa=Xa,Xd=Xd,base=1) createX(p=p,na=na,nd=nd,Xa=Xa,Xd=Xd,DIFF=TRUE) createX(p=p,na=na,nd=nd,Xa=Xa,Xd=Xd,DIFF=TRUE,base=2) createX(p=p,na=na,nd=NULL,Xa=Xa,Xd=NULL) createX(p=p,na=NULL,nd=nd,Xa=NULL,Xd=Xd) } \keyword{ array } \keyword{ utilities } bayesm/man/plot.bayesm.nmix.Rd0000644000176000001440000000447212523207756016053 0ustar ripleyusers\name{plot.bayesm.nmix} \alias{plot.bayesm.nmix} \concept{MCMC} \concept{S3 method} \concept{plot} \title{Plot Method for MCMC Draws of Normal Mixtures} \description{ \code{plot.bayesm.nmix} is an S3 method to plot aspects of the fitted density from a list of MCMC draws of normal mixture components. Plots of marginal univariate and bivariate densities are produced. } \usage{ \method{plot}{bayesm.nmix}(x,names,burnin,Grid,bi.sel,nstd,marg,Data,ngrid,ndraw, ...) } \arguments{ \item{x}{ An object of S3 class bayesm.nmix } \item{names}{optional character vector of names for each of the dimensions} \item{burnin}{number of draws to discard for burn-in (def: .1*nrow(X))} \item{Grid}{matrix of grid points for densities, def: mean +/- nstd std deviations (if Data no supplied), range of Data if supplied)} \item{bi.sel}{list of vectors, each giving pairs for bivariate distributions (def: list(c(1,2)))} \item{nstd}{number of standard deviations for default Grid (def: 2)} \item{marg}{logical, if TRUE display marginals (def: TRUE)} \item{Data}{matrix of data points, used to paint histograms on marginals and for grid } \item{ngrid}{number of grid points for density estimates (def: 50)} \item{ndraw}{number of draws to average Mcmc estimates over (def: 200)} \item{...}{ standard graphics parameters } } \details{ Typically, \code{plot.bayesm.nmix} will be invoked by a call to the generic plot function as in \code{plot(object)} where object is of class bayesm.nmix. These objects are lists of three components. The first component is an array of draws of mixture component probabilties. The second component is not used. The third is a lists of lists of lists with draws of each of the normal components. \cr \cr \code{plot.bayesm.nmix} can also be used as a standard function, as in \code{plot.bayesm.nmix(list)}. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rnmixGibbs}}, \code{\link{rhierMnlRwMixture}}, \code{\link{rhierLinearMixture}}, \code{\link{rDPGibbs}}} \examples{ ## ## not run # out=rnmixGibbs(Data,Prior,Mcmc) # plot(out,bi.sel=list(c(1,2),c(3,4),c(1,3))) # # plot bivariate distributions for dimension 1,2; 3,4; and 1,3 # } \keyword{ hplot } bayesm/man/rordprobitGibbs.Rd0000644000176000001440000000705312511331541015762 0ustar ripleyusers\name{rordprobitGibbs} \alias{rordprobitGibbs} \concept{bayes} \concept{MCMC} \concept{probit} \concept{Gibbs Sampling} \title{ Gibbs Sampler for Ordered Probit } \description{ \code{rordprobitGibbs} implements a Gibbs Sampler for the ordered probit model. } \usage{ rordprobitGibbs(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(X, y, k)} \item{Prior}{ list(betabar, A, dstarbar, Ad)} \item{Mcmc}{ list(R, keep, nprint s, change, draw) } } \details{ Model: \eqn{z = X\beta + e}. \eqn{e} \eqn{\sim}{~} \eqn{N(0,I)}. y=1,..,k. cutoff=c( c [1] ,..c [k+1] ). \cr y=k, if c [k] <= z < c [k+1] . Prior: \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})}. \eqn{dstar} \eqn{\sim}{~} \eqn{N(dstarbar,Ad^{-1})}. List arguments contain \describe{ \item{\code{X}}{n x nvar Design Matrix} \item{\code{y}}{n x 1 vector of observations, (1,...,k)} \item{\code{k}}{the largest possible value of y} \item{\code{betabar}}{nvar x 1 prior mean (def: 0)} \item{\code{A}}{nvar x nvar prior precision matrix (def: .01I)} \item{\code{dstarbar}}{ndstar x 1 prior mean, ndstar=k-2 (def: 0)} \item{\code{Ad}}{ndstar x ndstar prior precision matrix (def:I)} \item{\code{s}}{ scaling parm for RW Metropolis (def: 2.93/sqrt(nvar))} \item{\code{R}}{ number of MCMC draws } \item{\code{keep}}{ thinning parameter - keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ \item{betadraw }{R/keep x k matrix of betadraws} \item{cutdraw }{R/keep x (k-1) matrix of cutdraws} \item{dstardraw }{R/keep x (k-2) matrix of dstardraws} \item{accept }{a value of acceptance rate in RW Metropolis} } \note{ set c[1]=-100. c[k+1]=100. c[2] is set to 0 for identification. \cr The relationship between cut-offs and dstar is \cr c[3] = exp(dstar[1]), c[4]=c[3]+exp(dstar[2]),..., c[k] = c[k-1] + exp(datsr[k-2]) Be careful in assessing prior parameter, Ad. .1 is too small for many applications. } \references{ \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch\cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{rbprobitGibbs}} } \examples{ ## ## rordprobitGibbs example ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} ## simulate data for ordered probit model simordprobit=function(X, betas, cutoff){ z = X\%*\%betas + rnorm(nobs) y = cut(z, br = cutoff, right=TRUE, include.lowest = TRUE, labels = FALSE) return(list(y = y, X = X, k=(length(cutoff)-1), betas= betas, cutoff=cutoff )) } set.seed(66) nobs=300 X=cbind(rep(1,nobs),runif(nobs, min=0, max=5),runif(nobs,min=0, max=5)) k=5 betas=c(0.5, 1, -0.5) cutoff=c(-100, 0, 1.0, 1.8, 3.2, 100) simout=simordprobit(X, betas, cutoff) Data=list(X=simout$X,y=simout$y, k=k) ## set Mcmc for ordered probit model Mcmc=list(R=R) out=rordprobitGibbs(Data=Data,Mcmc=Mcmc) cat(" ", fill=TRUE) cat("acceptance rate= ",accept=out$accept,fill=TRUE) ## outputs of betadraw and cut-off draws cat(" Summary of betadraws",fill=TRUE) summary(out$betadraw,tvalues=betas) cat(" Summary of cut-off draws",fill=TRUE) summary(out$cutdraw,tvalues=cutoff[2:k]) if(0){ ## plotting examples plot(out$cutdraw) } } \keyword{ models } bayesm/man/fsh.Rd0000755000176000001440000000052211430343005013377 0ustar ripleyusers\name{fsh} \alias{fsh} \title{ Flush Console Buffer } \description{ Flush contents of console buffer. This function only has an effect on the Windows GUI. } \usage{ fsh() } \value{ No value is returned. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \keyword{ utilities } bayesm/man/numEff.Rd0000755000176000001440000000246611430344715014061 0ustar ripleyusers\name{numEff} \alias{numEff} \concept{numerical efficiency} \title{ Compute Numerical Standard Error and Relative Numerical Efficiency } \description{ \code{numEff} computes the numerical standard error for the mean of a vector of draws as well as the relative numerical efficiency (ratio of variance of mean of this time series process relative to iid sequence). } \usage{ numEff(x, m = as.integer(min(length(x), (100/sqrt(5000)) * sqrt(length(x))))) } \arguments{ \item{x}{ R x 1 vector of draws } \item{m}{ number of lags for autocorrelations } } \details{ default for number of lags is chosen so that if R = 5000, m =100 and increases as the sqrt(R). } \value{ \item{stderr }{standard error of the mean of x} \item{f }{ variance ratio (relative numerical efficiency) } } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \examples{ numEff(rnorm(1000),m=20) numEff(rnorm(1000)) } \keyword{ ts } \keyword{ utilities } bayesm/man/cheese.Rd0000644000176000001440000000377312536435611014100 0ustar ripleyusers\name{cheese} \alias{cheese} \docType{data} \title{ Sliced Cheese Data} \description{ Panel data with sales volume for a package of Borden Sliced Cheese as well as a measure of display activity and price. Weekly data aggregated to the "key" account or retailer/market level. } \usage{data(cheese)} \format{ A data frame with 5555 observations on the following 4 variables. \describe{ \item{\code{RETAILER}}{a list of 88 retailers} \item{\code{VOLUME}}{unit sales} \item{\code{DISP}}{a measure of display activity -- per cent ACV on display} \item{\code{PRICE}}{in $} } } \source{ Boatwright et al (1999), "Account-Level Modeling for Trade Promotion," \emph{JASA} 94, 1063-1073. } \references{ Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \examples{ data(cheese) cat(" Quantiles of the Variables ",fill=TRUE) mat=apply(as.matrix(cheese[,2:4]),2,quantile) print(mat) ## ## example of processing for use with rhierLinearModel ## if(0) { retailer=levels(cheese$RETAILER) nreg=length(retailer) nvar=3 regdata=NULL for (reg in 1:nreg) { y=log(cheese$VOLUME[cheese$RETAILER==retailer[reg]]) iota=c(rep(1,length(y))) X=cbind(iota,cheese$DISP[cheese$RETAILER==retailer[reg]], log(cheese$PRICE[cheese$RETAILER==retailer[reg]])) regdata[[reg]]=list(y=y,X=X) } Z=matrix(c(rep(1,nreg)),ncol=1) nz=ncol(Z) ## ## run each individual regression and store results ## lscoef=matrix(double(nreg*nvar),ncol=nvar) for (reg in 1:nreg) { coef=lsfit(regdata[[reg]]$X,regdata[[reg]]$y,intercept=FALSE)$coef if (var(regdata[[reg]]$X[,2])==0) { lscoef[reg,1]=coef[1]; lscoef[reg,3]=coef[2]} else {lscoef[reg,]=coef } } R=2000 Data=list(regdata=regdata,Z=Z) Mcmc=list(R=R,keep=1) set.seed(66) out=rhierLinearModel(Data=Data,Mcmc=Mcmc) cat("Summary of Delta Draws",fill=TRUE) summary(out$Deltadraw) cat("Summary of Vbeta Draws",fill=TRUE) summary(out$Vbetadraw) if(0){ # # plot hier coefs plot(out$betadraw) } } } \keyword{datasets} bayesm/man/detailing.Rd0000644000176000001440000000655712523033617014603 0ustar ripleyusers\name{detailing} \alias{detailing} \docType{data} \title{ Physician Detailing Data from Manchanda et al (2004)} \description{ Monthly data on detailing (sales calls) on 1000 physicians. 23 mos of data for each physician. Includes physician covariates. Dependent variable (\code{scripts}) is the number of new prescriptions ordered by the physician for the drug detailed. } \usage{data(detailing)} \format{ This R object is a list of two data frames, list(counts,demo). List of 2: $ counts:`data.frame': 23000 obs. of 4 variables:\cr \ldots$ id : int [1:23000] 1 1 1 1 1 1 1 1 1 1 \cr \ldots$ scripts : int [1:23000] 3 12 3 6 5 2 5 1 5 3 \cr \ldots$ detailing : int [1:23000] 1 1 1 2 1 0 2 2 1 1 \cr \ldots$ lagged_scripts: int [1:23000] 4 3 12 3 6 5 2 5 1 5 $ demo :`data.frame': 1000 obs. of 4 variables:\cr \ldots$ id : int [1:1000] 1 2 3 4 5 6 7 8 9 10 \cr \ldots$ generalphys : int [1:1000] 1 0 1 1 0 1 1 1 1 1 \cr \ldots$ specialist: int [1:1000] 0 1 0 0 1 0 0 0 0 0 \cr \ldots$ mean_samples: num [1:1000] 0.722 0.491 0.339 3.196 0.348 } \details{ generalphys is dummy for if doctor is a "general practitioner," specialist is dummy for if the physician is a specialist in the theraputic class for which the drug is intended, mean_samples is the mean number of free drug samples given the doctor over the sample. } \source{ Manchanda, P., P. K. Chintagunta and P. E. Rossi (2004), "Response Modeling with Non-Random Marketing Mix Variables," \emph{Journal of Marketing Research} 41, 467-478. } \examples{ data(detailing) cat(" table of Counts Dep Var", fill=TRUE) print(table(detailing$counts[,2])) cat(" means of Demographic Variables",fill=TRUE) mat=apply(as.matrix(detailing$demo[,2:4]),2,mean) print(mat) ## ## example of processing for use with rhierNegbinRw ## if(0) { data(detailing) counts = detailing$counts Z = detailing$demo # Construct the Z matrix Z[,1] = 1 Z[,2]=Z[,2]-mean(Z[,2]) Z[,3]=Z[,3]-mean(Z[,3]) Z[,4]=Z[,4]-mean(Z[,4]) Z=as.matrix(Z) id=levels(factor(counts$id)) nreg=length(id) nobs = nrow(counts$id) regdata=NULL for (i in 1:nreg) { X = counts[counts[,1] == id[i],c(3:4)] X = cbind(rep(1,nrow(X)),X) y = counts[counts[,1] == id[i],2] X = as.matrix(X) regdata[[i]]=list(X=X, y=y) } nvar=ncol(X) # Number of X variables nz=ncol(Z) # Number of Z variables rm(detailing,counts) cat("Finished Reading data",fill=TRUE) fsh() Data = list(regdata=regdata, Z=Z) deltabar = matrix(rep(0,nvar*nz),nrow=nz) Vdelta = 0.01 * diag(nz) nu = nvar+3 V = 0.01*diag(nvar) a = 0.5 b = 0.1 Prior = list(deltabar=deltabar, Vdelta=Vdelta, nu=nu, V=V, a=a, b=b) R = 10000 keep =1 s_beta=2.93/sqrt(nvar) s_alpha=2.93 c=2 Mcmc = list(R=R, keep = keep, s_beta=s_beta, s_alpha=s_alpha, c=c) out = rhierNegbinRw(Data, Prior, Mcmc) # Unit level mean beta parameters Mbeta = matrix(rep(0,nreg*nvar),nrow=nreg) ndraws = length(out$alphadraw) for (i in 1:nreg) { Mbeta[i,] = rowSums(out$Betadraw[i, , ])/ndraws } cat(" Deltadraws ",fill=TRUE) summary(out$Deltadraw) cat(" Vbetadraws ",fill=TRUE) summary(out$Vbetadraw) cat(" alphadraws ",fill=TRUE) summary(out$alphadraw) if(0){ ## plotting examples plot(out$betadraw) plot(out$alphadraw) plot(out$Deltadraw) } } } \keyword{datasets} bayesm/man/runiregGibbs.Rd0000644000176000001440000000433112523217355015254 0ustar ripleyusers\name{runiregGibbs} \alias{runiregGibbs} \concept{bayes} \concept{Gibbs Sampler} \concept{regression} \concept{MCMC} \title{ Gibbs Sampler for Univariate Regression } \description{ \code{runiregGibbs} implements a Gibbs Sampler to draw from posterior of a univariate regression with a conditionally conjugate prior. } \usage{ runiregGibbs(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(y,X)} \item{Prior}{ list(betabar,A, nu, ssq) } \item{Mcmc}{ list(sigmasq,R,keep,nprint)} } \details{ Model: \eqn{y = X\beta + e}. \eqn{e} \eqn{\sim}{~} \eqn{N(0,\sigma^2)}. \cr Priors: \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})}. \eqn{\sigma^2} \eqn{\sim}{~} \eqn{(nu*ssq)/\chi^2_{nu}}. List arguments contain \itemize{ \item{\code{X}}{n x k Design Matrix} \item{\code{y}}{n x 1 vector of observations} \item{\code{betabar}}{k x 1 prior mean (def: 0)} \item{\code{A}}{k x k prior precision matrix (def: .01I)} \item{\code{nu}}{ d.f. parm for Inverted Chi-square prior (def: 3)} \item{\code{ssq}}{ scale parm for Inverted Chi-square prior (def:var(y))} \item{\code{R}}{ number of MCMC draws } \item{\code{keep}}{ thinning parameter - keep every keepth draw } \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} } } \value{ list of MCMC draws \item{betadraw }{ R x k array of betadraws } \item{sigmasqdraw }{ R vector of sigma-sq draws} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \seealso{ \code{\link{runireg}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10} set.seed(66) n=100 X=cbind(rep(1,n),runif(n)); beta=c(1,2); sigsq=.25 y=X\%*\%beta+rnorm(n,sd=sqrt(sigsq)) Data1=list(y=y,X=X); Mcmc1=list(R=R) out=runiregGibbs(Data=Data1,Mcmc=Mcmc1) cat("Summary of beta and Sigma draws",fill=TRUE) summary(out$betadraw,tvalues=beta) summary(out$sigmasqdraw,tvalues=sigsq) if(0){ ## plotting examples plot(out$betadraw) } } \keyword{ regression } bayesm/man/rwishart.Rd0000644000176000001440000000254412523217603014475 0ustar ripleyusers\name{rwishart} \alias{rwishart} \concept{Wishart distribution} \concept{Inverted Wishart} \concept{simulation} \title{ Draw from Wishart and Inverted Wishart Distribution } \description{ \code{rwishart} draws from the Wishart and Inverted Wishart distributions. } \usage{ rwishart(nu, V) } \arguments{ \item{nu}{ d.f. parameter} \item{V}{ pds location matrix} } \details{ In the parameterization used here, \eqn{W} \eqn{\sim}{~} \eqn{W(nu,V)}, \eqn{E[W]=nuV}. \cr If you want to use an Inverted Wishart prior, you \emph{must invert the location matrix} before calling \code{rwishart}, e.g. \cr \eqn{\Sigma} \eqn{\sim}{~} IW(nu,V); \eqn{\Sigma^{-1}} \eqn{\sim}{~} \eqn{W(nu,V^{-1})}. } \value{ \item{W}{ Wishart draw } \item{IW }{Inverted Wishart draw} \item{C }{ Upper tri root of W} \item{CI }{ inv(C), \eqn{W^{-1}} = CICI'} } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \examples{ ## set.seed(66) rwishart(5,diag(3))$IW } \keyword{ multivariate } bayesm/man/rivDP.Rd0000644000176000001440000001463112523213610013650 0ustar ripleyusers\name{rivDP} \alias{rivDP} \concept{Instrumental Variables} \concept{Gibbs Sampler} \concept{Dirichlet Process} \concept{bayes} \concept{endogeneity} \concept{simultaneity} \concept{MCMC} \title{ Linear "IV" Model with DP Process Prior for Errors} \description{ \code{rivDP} is a Gibbs Sampler for a linear structural equation with an arbitrary number of instruments. \code{rivDP} uses a mixture of normals for the structural and reduced form equation implemented with a Dirichlet Process Prior. } \usage{ rivDP(Data, Prior, Mcmc) } \arguments{ \item{Data}{ list(z,w,x,y) } \item{Prior}{ list(md,Ad,mbg,Abg,lambda,Prioralpha,lambda_hyper) (optional) } \item{Mcmc}{ list(R,keep,nprint,maxuniq,SCALE,gridsize) (R required) } } \details{ Model:\cr \eqn{x=z'\delta + e1}. \cr \eqn{y=\beta*x + w'\gamma + e2}. \cr \eqn{e1,e2} \eqn{\sim}{~} \eqn{N(\theta_{i})}. \eqn{\theta_{i}} represents \eqn{\mu_{i},\Sigma_{i}} Note: Error terms have non-zero means. DO NOT include intercepts in the z or w matrices. This is different from \code{rivGibbs} which requires intercepts to be included explicitly. Priors:\cr \eqn{\delta} \eqn{\sim}{~} \eqn{N(md,Ad^{-1})}. \eqn{vec(\beta,\gamma)} \eqn{\sim}{~} \eqn{N(mbg,Abg^{-1})} \cr \eqn{\theta_{i}} \eqn{\sim}{~} \eqn{G} \cr \eqn{G} \eqn{\sim}{~} \eqn{DP(alpha,G_{0})} \cr \eqn{G_{0}} is the natural conjugate prior for \eqn{(\mu,\Sigma)}: \cr \eqn{\Sigma} \eqn{\sim}{~} \eqn{IW(nu,vI)} and \eqn{\mu|\Sigma} \eqn{\sim}{~} \eqn{N(0,\Sigma (x) a^{-1})} \cr These parameters are collected together in the list \eqn{\lambda}. It is highly recommended that you use the default settings for these hyper-parameters.\cr \eqn{\lambda(a,nu,v):}\cr \eqn{a} \eqn{\sim}{~} uniform[alim[1],alimb[2]]\cr \eqn{nu} \eqn{\sim}{~} dim(data)-1 + exp(z) \cr \eqn{z} \eqn{\sim}{~} uniform[dim(data)-1+nulim[1],nulim[2]]\cr \eqn{v} \eqn{\sim}{~} uniform[vlim[1],vlim[2]] \eqn{alpha} \eqn{\sim}{~} \eqn{(1-(alpha-alpha_{min})/(alpha_{max}-alpha{min}))^{power}} \cr where \eqn{alpha_{min}} and \eqn{alpha_{max}} are set using the arguments in the reference below. It is highly recommended that you use the default values for the hyperparameters of the prior on alpha List arguments contain: Data:\cr \itemize{ \item{\code{z}}{ matrix of obs on instruments} \item{\code{y}}{ vector of obs on lhs var in structural equation} \item{\code{x}}{ "endogenous" var in structural eqn} \item{\code{w}}{ matrix of obs on "exogenous" vars in the structural eqn}} Prior:\cr \itemize{ \item{\code{md}}{ prior mean of delta (def: 0)} \item{\code{Ad}}{ pds prior prec for prior on delta (def: .01I)} \item{\code{mbg}}{ prior mean vector for prior on beta,gamma (def: 0)} \item{\code{Abg}}{ pds prior prec for prior on beta,gamma (def: .01I)}} Prioralpha:\cr \itemize{ \item{\code{Istarmin}}{ expected number of components at lower bound of support of alpha (def: 1)} \item{\code{Istarmax}}{ expected number of components at upper bound of support of alpha} \item{\code{power}}{ power parameter for alpha prior (def: .8)} } lambda_hyper:\cr \itemize{ \item{\code{alim}}{ defines support of a distribution,def:c(.01,10) } \item{\code{nulim}}{ defines support of nu distribution, def:c(.01,3)} \item{\code{vlim}}{ defines support of v distribution, def:c(.1,4)} } MCMC:\cr \itemize{ \item{\code{R}}{ number of MCMC draws} \item{\code{keep}}{ MCMC thinning parm: keep every keepth draw (def: 1)} \item{\code{nprint}}{ print the estimated time remaining for every nprint'th draw (def: 100)} \item{\code{maxuniq}}{ storage constraint on the number of unique components (def: 200)} \item{\code{SCALE}}{ scale data (def: TRUE)} \item{\code{gridsize}}{ gridsize parm for alpha draws (def: 20)} } output includes object \code{nmix} of class "bayesm.nmix" which contains draws of predictive distribution of errors (a Bayesian analogue of a density estimate for the error terms).\cr nmix:\cr \itemize{ \item{\code{probdraw}}{ not used} \item{\code{zdraw}}{ not used} \item{\code{compdraw}}{ list R/keep of draws from bivariate predictive for the errors} } note: in compdraw list, there is only one component per draw } \value{ a list containing: \item{deltadraw}{R/keep x dim(delta) array of delta draws} \item{betadraw}{R/keep x 1 vector of beta draws} \item{gammadraw}{R/keep x dim(gamma) array of gamma draws } \item{Istardraw}{R/keep x 1 array of drawsi of the number of unique normal components} \item{alphadraw}{R/keep x 1 array of draws of Dirichlet Process tightness parameter} \item{nmix}{R/keep x list of draws for predictive distribution of errors} } \references{ For further discussion, see "A Semi-Parametric Bayesian Approach to the Instrumental Variable Problem," by Conley, Hansen, McCulloch and Rossi, Journal of Econometrics (2008).\cr } \seealso{\code{rivGibbs}} \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}. } \examples{ ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} ## ## simulate scaled log-normal errors and run ## set.seed(66) k=10 delta=1.5 Sigma=matrix(c(1,.6,.6,1),ncol=2) N=1000 tbeta=4 set.seed(66) scalefactor=.6 root=chol(scalefactor*Sigma) mu=c(1,1) ## ## compute interquartile ranges ## ninterq=qnorm(.75)-qnorm(.25) error=matrix(rnorm(100000*2),ncol=2)%*%root error=t(t(error)+mu) Err=t(t(exp(error))-exp(mu+.5*scalefactor*diag(Sigma))) lnNinterq=quantile(Err[,1],prob=.75)-quantile(Err[,1],prob=.25) ## ## simulate data ## error=matrix(rnorm(N*2),ncol=2)\%*\%root error=t(t(error)+mu) Err=t(t(exp(error))-exp(mu+.5*scalefactor*diag(Sigma))) # # scale appropriately Err[,1]=Err[,1]*ninterq/lnNinterq Err[,2]=Err[,2]*ninterq/lnNinterq z=matrix(runif(k*N),ncol=k) x=z\%*\%(delta*c(rep(1,k)))+Err[,1] y=x*tbeta+Err[,2] # set intial values for MCMC Data = list(); Mcmc=list() Data$z = z; Data$x=x; Data$y=y # start MCMC and keep results Mcmc$maxuniq=100 Mcmc$R=R end=Mcmc$R begin=100 out=rivDP(Data=Data,Mcmc=Mcmc) cat("Summary of Beta draws",fill=TRUE) summary(out$betadraw,tvalues=tbeta) if(0){ ## plotting examples plot(out$betadraw,tvalues=tbeta) plot(out$nmix) ## plot "fitted" density of the errors ## } } \keyword{ models }