magic.post.proc package:mgcv R Documentation _A_u_x_i_l_l_i_a_r_y _i_n_f_o_r_m_a_t_i_o_n _f_r_o_m _m_a_g_i_c _f_i_t _D_e_s_c_r_i_p_t_i_o_n: Obtains Bayesian parameter covariance matrix, frequentist parameter estimator covariance matrix, estimated degrees of freedom for each parameter and leading diagonal of influence/hat matrix, for a penalized regression estimated by 'magic'. _U_s_a_g_e: magic.post.proc(X,object,w) _A_r_g_u_m_e_n_t_s: X: is the model matrix. object: is the list returned by 'magic' after fitting the model with model matrix 'X'. w: is the weight vector used in fitting, or the weight matrix used in fitting (i.e. supplied to 'magic', if one was.). If 'w' is a vector then its elements are typically proportional to reciprocal variances (but could even be negative). If 'w' is a matrix then 't(w)%*%w' should typically give the inverse of the covariance matrix of the response data supplied to 'magic'. _D_e_t_a_i_l_s: 'object' contains 'rV' (V, say), and 'scale' (s, say) which can be used to obtain the require quantities as follows. The Bayesian covariance matrix of the parameters is VV's. The vector of estimated degrees of freedom for each parameter is the leading diagonal of VV'X'W'WX where W is either the weight matrix 'w' or the matrix 'diag(w)'. The hat/influence matrix is given by WXVV'X'W' . The frequentist parameter estimator covariance matrix is VV'X'W'WXVV's: it is sometimes useful for testing terms for equality to zero. _V_a_l_u_e: A list with three items: Vb: the Bayesian covariance matrix of the model parameters. Ve: the frequentist covariance matrix for the parameter estimators. hat: the leading diagonal of the hat (influence) matrix. edf: the array giving the estimated degrees of freedom associated with each parameter. _A_u_t_h_o_r(_s): Simon N. Wood simon.wood@r-project.org _S_e_e _A_l_s_o: 'magic'