1d:T7f2,# Preparations for bandwidth selection Calculates an intermediate part for the K-fold cross validation. ```r b_selection_prep_g(h_mat, int_X, size_X_grid, n, Yi) ``` ## Arguments - `h_mat`: A matrix of the estimator for the future conditional hazard rate for all values `x` and `t`. - `int_X`: Vector of the position of the observed marker values in the grid for marker values. - `size_X_grid`: Numeric value indicating the number of grid points for marker values. - `n`: Number of individuals. - `Yi`: A matrix made by `make_Yi` indicating the exposure. ## Details The function `b_selection_prep_g` calculates a key component for the bandwidth selection $$ \hat{g}^{-I_j}_i(t) = \int_0^t Z_i(s) \hat{h}^{-I_j}_{X_i(s)}(t-s) ds, $$ where $\hat{h}^{-I_j}$ is estimated without information from all counting processes $i$ with $i \in I_j$ and $Z$ is the exposure. ## Returns A matrix with $\hat{g}^{-I_j}_i(t)$ for all individuals `i` and time grid points `t`. ## See Also `b_selection` ## Examples ```r pbc2_id = to_id(pbc2) size_s_grid <- size_X_grid <- 100 n = max(as.numeric(pbc2$id)) s = pbc2$year X = pbc2$serBilir XX = pbc2_id$serBilir ss <- pbc2_id$years delta <- pbc2_id$status2 br_s = seq(0, max(s), max(s)/( size_s_grid-1)) br_X = seq(min(X), max(X), (max(X)-min(X))/( size_X_grid-1)) X_lin = lin_interpolate(br_s, pbc2_id$id, pbc2$id, X, s) int_X <- findInterval(X_lin, br_X) int_s = rep(1:length(br_s), n) N <- make_N(pbc2, pbc2_id, breaks_X=br_X, breaks_s=br_s, ss, XX, delta) Y <- make_Y(pbc2, pbc2_id, X_lin, br_X, br_s, size_s_grid, size_X_grid, int_s, int_X, 'years', n) b = 1.7 alpha<-get_alpha(N, Y, b, br_X, K=Epan ) Yi <- make_Yi(pbc2, pbc2_id, X_lin, br_X, br_s, size_s_grid, size_X_grid, int_s, int_X,'years', n) Ni <- make_Ni(breaks_s=br_s, size_s_grid, ss, delta, n) t = 2 h_xt_mat = t(sapply(br_s[1:99], function(si){ h_xt_vec(br_X, br_s, size_s_grid, alpha, t, b, Yi, int_X, n)})) b_selection_prep_g(h_xt_mat, int_X, size_X_grid, n, Yi) ```

b_selection_prep_g function