java实现的多元线性回归分析.docVIP

/** * */ package com.augurit.eddy.math; /** * @author Administrator * */ public class SPT { /** * 一元线性回归分析 * * @param x[n] * 存放自变量x的n个取值 * @param y[n] * 存放与自变量x的n个取值相对应的随机变量y的观察值 * @param n * 观察点数 * @param a[2] * a(0) 返回回归系数b ,a(1)返回回归系数a * @param dt[6] * dt(0) 返回偏差平方和q ,dt(1)返回平均标准偏差s ,dt(2)返回回归平方和p, * dt(3)返回最大偏差umax,dt(4)返回最小偏差umin,dt(5)返回偏差平均值u */ public static void SPT1(double[] x, double[] y, int n, double[] a, double[] dt) // double x[],y[],a[2],dt[6]; { int i; double xx, yy, e, f, q, u, p, umax, umin, s; xx = 0.0; yy = 0.0; for (i = 0; i = n - 1; i++) { xx = xx + x[i] / n; yy = yy + y[i] / n; } e = 0.0; f = 0.0; for (i = 0; i = n - 1; i++) { q = x[i] - xx; e = e + q * q; f = f + q * (y[i] - yy); } a[1] = f / e; a[0] = yy - a[1] * xx; q = 0.0; u = 0.0; p = 0.0; umax = 0.0; umin = 1.0e+30; for (i = 0; i = n - 1; i++) { s = a[1] * x[i] + a[0]; q = q + (y[i] - s) * (y[i] - s); p = p + (s - yy) * (s - yy); e = Math.abs(y[i] - s); if (e umax) umax = e; if (e umin) umin = e; u = u + e / n; } dt[1] = Math.sqrt(q / n); dt[0] = q; dt[2] = p; dt[3] = umax; dt[4] = umin; dt[5] = u; } /** * 多元线性回归分析 * * @param x[m][n] * 每一列存放m个自变量的观察值 * @param y[n] * 存放随即变量y的n个观察值 * @param m * 自变量的个数 * @param n * 观察数据的组数 * @param a * 返回回归系数a0,...,am * @param dt[4] * dt[0]偏差平方和q,dt[1] 平均标准偏差s dt[2]返回复相关系数r dt[3]返回回归平方和u * @param v[m] * 返回m个自变量的偏相关系数 */ public static void sqt2(double[][] x, double[] y, int m, int n, double[] a, double[] dt, double[] v) { int i, j, k, mm; double q, e, u, p, yy, s, r, pp; double[] b = new double[(m + 1) * (m + 1)]; mm = m + 1; b[mm * mm - 1] = n; for (j = 0; j = m - 1; j++) { p = 0.0; for (i = 0; i = n - 1; i++) p = p +