| (1) % rdgpred Efficient prediction using ridge regression. |
| (2) % rdgpred(Y,A,A21,L) returns ridge regression predictions in test set. |
| (3) % Vector Y contains outcome training set, matrix A similarity measures |
| (4) % in training set (e.g., A=XX′ for N-by-P input matrix X), matrix A21 |
| (5) % similarity individuals in test set (rows) and training set (columns), |
| (6) % and vector L the penalty values to consider. |
| (7) % |
| (8) % rdgpred(Y,A,A21,L,Z,Z2) first corrects A and A21 for confounders. |
| (9) % Matrix Z contains confounders in traing set and Z2 those in test set. |
| (10) % |
| (11) % Author : R de Vlaming and PJF Groenen |
| (12) % Institute: Erasmus School of Economics Date: November 25, 2014 |
| (13) function Y2 = rdgpred(Y,A,A21,L,Z,Z2) |
| (14) |
| (15) P = numel(L); % find size of set of penalties |
| (16) N = numel(Y); % find size of training set |
| (17) N2 = size(A21,1); % find size of test set |
| (18) |
| (19) if nargin > 5 % correct similarities if confounders present |
| (20) M = eye(N) − Zinv(Z′Z)Z′; % anti projection matrix of Z |
| (21) M2 = eye(N2) - Z2inv(Z2′Z2)Z2′; % anti projection matrix of Z2 |
| (22) A = MAM; % adjust similarties A |
| (23) A21 = M2A21M; % adjust similarties A21 |
| (24) end |
| (25) |
| (26) [Q,D] = eig(A); D = diag(D); % obtain eigenvecs Q and eigenvalues D of A |
| (27) |
| (28) % for each eigenvalue E (rows) and lambda S (cols) find 1/(E+S) |
| (29) D = 1./(repmat(D,1,P) + repmat(L(:)′,N,1)); |
| (30) |
| (31) % predict for observations in test set (rows) for each lambda (cols) |
| (32) QTY = repmat((Y′Q)′,1,P); |
| (33) Y2 = A21(Q(D.QTY)); |
| (34) Y2 = real(Y2); % remove imaginary part due to numerical imprecession |
| (35) |
| (36) end |
