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- _S_i_n_g_u_l_a_r _V_a_l_u_e _D_e_c_o_m_p_o_s_i_t_i_o_n _o_f _a _M_a_t_r_i_x
-
- svd(x, nu=min(n,p), nv=min(n,p))
-
- _A_r_g_u_m_e_n_t_s:
-
- x : a matrix whose SVD decomposition is to be
- computed.
-
- nu : the number of left eigenvectors to be com-
- puted. This must be one of 0, nrow(x) and
- ncol(x).
-
- nv : the number of right eigenvectors to be com-
- puted. This must be one of 0, and ncol(x).
-
- _D_e_s_c_r_i_p_t_i_o_n:
-
- svd provides an interface to the LINPACK routine DSVDC.
- The singular value decompostion plays an important role
- in many statistical techniques.
-
- _V_a_l_u_e_s:
-
- The SVD decomposition of the matrix as computed by LIN-
- PACK. The components in the returned value correspond
- directly to the values returned by DSVDC.
-
- d : a vector containing the singular values of x.
-
- u : a matrix whose columns contain the left eigenvec-
- tors of x.
-
- v : a matrix whose columns contain the right eigenvec-
- tors of x.
-
- _R_e_f_e_r_e_n_c_e_s:
-
- Dongarra, J. J., J. R. Bunch, C. B. Moler and G. W.
- Stewart (1978). LINPACK Users Guide, SIAM Publica-
- tions, Philadelphia.
-
- _S_e_e _A_l_s_o:
-
- qr.
-
-