b_selection_prep_g: Preparations for bandwidth selection

Description

Calculates an intermediate part for the K-fold cross validation.

Usage

b_selection_prep_g(h_mat, int_X, size_X_grid, n, Yi)

Value

A matrix with $\hat{g}^{-I_j}_i(t)$ for all individuals i and time grid points t.

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.

Examples

Run this code

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)