//$$ example.cpp Example of use of matrix package #define WANT_STREAM // include.h will get stream fns #define WANT_MATH // include.h will get math fns // newmatap.h will get include.h #include "newmatap.h" // need matrix applications #include "newmatio.h" // need matrix output routines #ifdef use_namespace using namespace NEWMAT; // access NEWMAT namespace #endif // demonstration of matrix package on linear regression problem void test1(Real* y, Real* x1, Real* x2, int nobs, int npred) { cout << "\n\nTest 1 - traditional, bad\n"; // traditional sum of squares and products method of calculation // but not adjusting means; maybe subject to round-off error // make matrix of predictor values with 1s into col 1 of matrix int npred1 = npred+1; // number of cols including col of ones. Matrix X(nobs,npred1); X.Column(1) = 1.0; // load x1 and x2 into X // [use << rather than = when loading arrays] X.Column(2) << x1; X.Column(3) << x2; // vector of Y values ColumnVector Y(nobs); Y << y; // form sum of squares and product matrix // [use << rather than = for copying Matrix into SymmetricMatrix] SymmetricMatrix SSQ; SSQ << X.t() * X; // calculate estimate // [bracket last two terms to force this multiplication first] // [ .i() means inverse, but inverse is not explicity calculated] ColumnVector A = SSQ.i() * (X.t() * Y); // Get variances of estimates from diagonal elements of inverse of SSQ // get inverse of SSQ - we need it for finding D DiagonalMatrix D; D << SSQ.i(); ColumnVector V = D.AsColumn(); // Calculate fitted values and residuals ColumnVector Fitted = X * A; ColumnVector Residual = Y - Fitted; Real ResVar = Residual.SumSquare() / (nobs-npred1); // Get diagonals of Hat matrix (an expensive way of doing this) DiagonalMatrix Hat; Hat << X * (X.t() * X).i() * X.t(); // print out answers cout << "\nEstimates and their standard errors\n\n"; // make vector of standard errors ColumnVector SE(npred1); for (int i=1; i<=npred1; i++) SE(i) = sqrt(V(i)*ResVar); // use concatenation function to form matrix and use matrix print // to get two columns cout << setw(11) << setprecision(5) << (A | SE) << endl; cout << "\nObservations, fitted value, residual value, hat value\n"; // use concatenation again; select only columns 2 to 3 of X cout << setw(9) << setprecision(3) << (X.Columns(2,3) | Y | Fitted | Residual | Hat.AsColumn()); cout << "\n\n"; } void test2(Real* y, Real* x1, Real* x2, int nobs, int npred) { cout << "\n\nTest 2 - traditional, OK\n"; // traditional sum of squares and products method of calculation // with subtraction of means - less subject to round-off error // than test1 // make matrix of predictor values Matrix X(nobs,npred); // load x1 and x2 into X // [use << rather than = when loading arrays] X.Column(1) << x1; X.Column(2) << x2; // vector of Y values ColumnVector Y(nobs); Y << y; // make vector of 1s ColumnVector Ones(nobs); Ones = 1.0; // calculate means (averages) of x1 and x2 [ .t() takes transpose] RowVector M = Ones.t() * X / nobs; // and subtract means from x1 and x1 Matrix XC(nobs,npred); XC = X - Ones * M; // do the same to Y [use Sum to get sum of elements] ColumnVector YC(nobs); Real m = Sum(Y) / nobs; YC = Y - Ones * m; // form sum of squares and product matrix // [use << rather than = for copying Matrix into SymmetricMatrix] SymmetricMatrix SSQ; SSQ << XC.t() * XC; // calculate estimate // [bracket last two terms to force this multiplication first] // [ .i() means inverse, but inverse is not explicity calculated] ColumnVector A = SSQ.i() * (XC.t() * YC); // calculate estimate of constant term // [AsScalar converts 1x1 matrix to Real] Real a = m - (M * A).AsScalar(); // Get variances of estimates from diagonal elements of inverse of SSQ // [ we are taking inverse of SSQ - we need it for finding D ] Matrix ISSQ = SSQ.i(); DiagonalMatrix D; D << ISSQ; ColumnVector V = D.AsColumn(); Real v = 1.0/nobs + (M * ISSQ * M.t()).AsScalar(); // for calc variance of const // Calculate fitted values and residuals int npred1 = npred+1; ColumnVector Fitted = X * A + a; ColumnVector Residual = Y - Fitted; Real ResVar = Residual.SumSquare() / (nobs-npred1); // Get diagonals of Hat matrix (an expensive way of doing this) Matrix X1(nobs,npred1); X1.Column(1)<<Ones; X1.Columns(2,npred1)<<X; DiagonalMatrix Hat; Hat << X1 * (X1.t() * X1).i() * X1.t(); // print out answers cout << "\nEstimates and their standard errors\n\n"; cout.setf(ios::fixed, ios::floatfield); cout << setw(11) << setprecision(5) << a << " "; cout << setw(11) << setprecision(5) << sqrt(v*ResVar) << endl; // make vector of standard errors ColumnVector SE(npred); for (int i=1; i<=npred; i++) SE(i) = sqrt(V(i)*ResVar); // use concatenation function to form matrix and use matrix print // to get two columns cout << setw(11) << setprecision(5) << (A | SE) << endl; cout << "\nObservations, fitted value, residual value, hat value\n"; cout << setw(9) << setprecision(3) << (X | Y | Fitted | Residual | Hat.AsColumn()); cout << "\n\n"; } void test3(Real* y, Real* x1, Real* x2, int nobs, int npred) { cout << "\n\nTest 3 - Cholesky\n"; // traditional sum of squares and products method of calculation // with subtraction of means - using Cholesky decomposition Matrix X(nobs,npred); X.Column(1) << x1; X.Column(2) << x2; ColumnVector Y(nobs); Y << y; ColumnVector Ones(nobs); Ones = 1.0; RowVector M = Ones.t() * X / nobs; Matrix XC(nobs,npred); XC = X - Ones * M; ColumnVector YC(nobs); Real m = Sum(Y) / nobs; YC = Y - Ones * m; SymmetricMatrix SSQ; SSQ << XC.t() * XC; // Cholesky decomposition of SSQ LowerTriangularMatrix L = Cholesky(SSQ); // calculate estimate ColumnVector A = L.t().i() * (L.i() * (XC.t() * YC)); // calculate estimate of constant term Real a = m - (M * A).AsScalar(); // Get variances of estimates from diagonal elements of invoice of SSQ DiagonalMatrix D; D << L.t().i() * L.i(); ColumnVector V = D.AsColumn(); Real v = 1.0/nobs + (L.i() * M.t()).SumSquare(); // Calculate fitted values and residuals int npred1 = npred+1; ColumnVector Fitted = X * A + a; ColumnVector Residual = Y - Fitted; Real ResVar = Residual.SumSquare() / (nobs-npred1); // Get diagonals of Hat matrix (an expensive way of doing this) Matrix X1(nobs,npred1); X1.Column(1)<<Ones; X1.Columns(2,npred1)<<X; DiagonalMatrix Hat; Hat << X1 * (X1.t() * X1).i() * X1.t(); // print out answers cout << "\nEstimates and their standard errors\n\n"; cout.setf(ios::fixed, ios::floatfield); cout << setw(11) << setprecision(5) << a << " "; cout << setw(11) << setprecision(5) << sqrt(v*ResVar) << endl; ColumnVector SE(npred); for (int i=1; i<=npred; i++) SE(i) = sqrt(V(i)*ResVar); cout << setw(11) << setprecision(5) << (A | SE) << endl; cout << "\nObservations, fitted value, residual value, hat value\n"; cout << setw(9) << setprecision(3) << (X | Y | Fitted | Residual | Hat.AsColumn()); cout << "\n\n"; } void test4(Real* y, Real* x1, Real* x2, int nobs, int npred) { cout << "\n\nTest 4 - QR triangularisation\n"; // QR triangularisation method // load data - 1s into col 1 of matrix int npred1 = npred+1; Matrix X(nobs,npred1); ColumnVector Y(nobs); X.Column(1) = 1.0; X.Column(2) << x1; X.Column(3) << x2; Y << y; // do Householder triangularisation // no need to deal with constant term separately Matrix X1 = X; // Want copy of matrix ColumnVector Y1 = Y; UpperTriangularMatrix U; ColumnVector M; QRZ(X1, U); QRZ(X1, Y1, M); // Y1 now contains resids ColumnVector A = U.i() * M; ColumnVector Fitted = X * A; Real ResVar = Y1.SumSquare() / (nobs-npred1); // get variances of estimates U = U.i(); DiagonalMatrix D; D << U * U.t(); // Get diagonals of Hat matrix DiagonalMatrix Hat; Hat << X1 * X1.t(); // print out answers cout << "\nEstimates and their standard errors\n\n"; ColumnVector SE(npred1); for (int i=1; i<=npred1; i++) SE(i) = sqrt(D(i)*ResVar); cout << setw(11) << setprecision(5) << (A | SE) << endl; cout << "\nObservations, fitted value, residual value, hat value\n"; cout << setw(9) << setprecision(3) << (X.Columns(2,3) | Y | Fitted | Y1 | Hat.AsColumn()); cout << "\n\n"; } void test5(Real* y, Real* x1, Real* x2, int nobs, int npred) { cout << "\n\nTest 5 - singular value\n"; // Singular value decomposition method // load data - 1s into col 1 of matrix int npred1 = npred+1; Matrix X(nobs,npred1); ColumnVector Y(nobs); X.Column(1) = 1.0; X.Column(2) << x1; X.Column(3) << x2; Y << y; // do SVD Matrix U, V; DiagonalMatrix D; SVD(X,D,U,V); // X = U * D * V.t() ColumnVector Fitted = U.t() * Y; ColumnVector A = V * ( D.i() * Fitted ); Fitted = U * Fitted; ColumnVector Residual = Y - Fitted; Real ResVar = Residual.SumSquare() / (nobs-npred1); // get variances of estimates D << V * (D * D).i() * V.t(); // Get diagonals of Hat matrix DiagonalMatrix Hat; Hat << U * U.t(); // print out answers cout << "\nEstimates and their standard errors\n\n"; ColumnVector SE(npred1); for (int i=1; i<=npred1; i++) SE(i) = sqrt(D(i)*ResVar); cout << setw(11) << setprecision(5) << (A | SE) << endl; cout << "\nObservations, fitted value, residual value, hat value\n"; cout << setw(9) << setprecision(3) << (X.Columns(2,3) | Y | Fitted | Residual | Hat.AsColumn()); cout << "\n\n"; } int main() { cout << "\nDemonstration of Matrix package\n"; cout << "\nPrint a real number (may help lost memory test): " << 3.14159265 << "\n"; // Test for any memory not deallocated after running this program Real* s1; { ColumnVector A(8000); s1 = A.Store(); } { // the data #ifndef ATandT Real y[] = { 8.3, 5.5, 8.0, 8.5, 5.7, 4.4, 6.3, 7.9, 9.1 }; Real x1[] = { 2.4, 1.8, 2.4, 3.0, 2.0, 1.2, 2.0, 2.7, 3.6 }; Real x2[] = { 1.7, 0.9, 1.6, 1.9, 0.5, 0.6, 1.1, 1.0, 0.5 }; #else // for compilers that do not understand aggregrates Real y[9], x1[9], x2[9]; y[0]=8.3; y[1]=5.5; y[2]=8.0; y[3]=8.5; y[4]=5.7; y[5]=4.4; y[6]=6.3; y[7]=7.9; y[8]=9.1; x1[0]=2.4; x1[1]=1.8; x1[2]=2.4; x1[3]=3.0; x1[4]=2.0; x1[5]=1.2; x1[6]=2.0; x1[7]=2.7; x1[8]=3.6; x2[0]=1.7; x2[1]=0.9; x2[2]=1.6; x2[3]=1.9; x2[4]=0.5; x2[5]=0.6; x2[6]=1.1; x2[7]=1.0; x2[8]=0.5; #endif int nobs = 9; // number of observations int npred = 2; // number of predictor values // we want to find the values of a,a1,a2 to give the best // fit of y[i] with a0 + a1*x1[i] + a2*x2[i] // Also print diagonal elements of hat matrix, X*(X.t()*X).i()*X.t() // this example demonstrates five methods of calculation Try { test1(y, x1, x2, nobs, npred); test2(y, x1, x2, nobs, npred); test3(y, x1, x2, nobs, npred); test4(y, x1, x2, nobs, npred); test5(y, x1, x2, nobs, npred); } CatchAll { cout << BaseException::what(); } } #ifdef DO_FREE_CHECK FreeCheck::Status(); #endif Real* s2; { ColumnVector A(8000); s2 = A.Store(); } cout << "\n\nThe following test does not work with all compilers - see documentation\n"; cout << "Checking for lost memory: " << (unsigned long)s1 << " " << (unsigned long)s2 << " "; if (s1 != s2) cout << " - error\n"; else cout << " - ok\n"; return 0; }