predict.ellipsoid package:cluster R Documentation _P_r_e_d_i_c_t _M_e_t_h_o_d _f_o_r _E_l_l_i_p_s_o_i_d _O_b_j_e_c_t_s _D_e_s_c_r_i_p_t_i_o_n: Compute points on the ellipsoid boundary, mostly for drawing. _U_s_a_g_e: predict.ellipsoid(object, n.out=201, ...) ## S3 method for class 'ellipsoid': predict(object, n.out=201, ...) ellipsoidPoints(A, d2, loc, n.half = 201) _A_r_g_u_m_e_n_t_s: object: an object of class 'ellipsoid', typically from 'ellipsoidhull()'; alternatively any list-like object with proper components, see details below. n.out, n.half: half the number of points to create. A, d2, loc: arguments of the auxilary 'ellipsoidPoints', see below. ...: passed to and from methods. _D_e_t_a_i_l_s: Note 'ellipsoidPoints' is the workhorse function of 'predict.ellipsoid' a standalone function and method for 'ellipsoid' objects, see 'ellipsoidhull'. The class of 'object' is not checked; it must solely have valid components 'loc' (length p), the p x p matrix 'cov' (corresponding to 'A') and 'd2' for the center, the shape (``covariance'') matrix and the squared average radius (or distance) or 'qchisq(*, p)' quantile. Unfortunately, this is only implemented for p = 2, currently; contributions for p >= 3 are _very welcome_. _V_a_l_u_e: a numeric matrix of dimension '2*n.out' times p. _S_e_e _A_l_s_o: 'ellipsoidhull', 'volume.ellipsoid'. _E_x_a_m_p_l_e_s: ## see also example(ellipsoidhull) ## Robust vs. L.S. covariance matrix set.seed(143) x <- rt(200, df=3) y <- 3*x + rt(200, df=2) plot(x,y, main="non-normal data (N=200)") mtext("with classical and robust cov.matrix ellipsoids") X <- cbind(x,y) C.ls <- cov(X) ; m.ls <- colMeans(X) d2.99 <- qchisq(0.99, df = 2) lines(ellipsoidPoints(C.ls, d2.99, loc=m.ls), col="green") if(require(MASS)) { Cxy <- cov.rob(cbind(x,y)) lines(ellipsoidPoints(Cxy$cov, d2 = d2.99, loc=Cxy$center), col="red") }# MASS