protected void compute() { nny = (int) ni[0].parseInput(); df.writeLine("Number of events is: n = " + nny); y = DatanRandom.standardNormal(nny); out(y); mllg = new MinLogLikeGauss(y); x0 = new DatanVector(2); x0.setElement(0, 1.); x0.setElement(1, 2.); MinSim ms = new MinSim(x0, mllg); x = ms.getMinPosition(); fcont = ms.getMinimum(); df.writeLine("Minimization with MinSim yields x = "); df.writeLine(x.toString()); // covariance matrix MinCov mc = new MinCov(x, mllg); cx = mc.getCovarianceMatrix(1.); df.writeLine("Covariance Matrix cx = "); df.writeLine(cx.toString()); // asymmetric errors fcont = fcont + 0.5; MinAsy ma = new MinAsy(x, cx, mllg); dxasy = ma.getAsymmetricErrors(fcont); DatanMatrix as = new DatanMatrix(dxasy); df.writeLine("Asymmetic errors:"); df.writeLine(as.toString()); df.writeLine("ma.hasConverged() = " + ma.hasConverged()); plotScatterDiagram(); plotParameterPlane(); }