prep_cv: Prepare for Cross validation bandwidth selection

Description

Implements the calculation of the hqm estimator on cross validation data sets. This is a preparation for the cross validation bandwidth selection technique for future conditional hazard rate estimation based on marker information data.

Usage

prep_cv(data, data.id, marker_name, event_time_name = 'years',
        time_name = 'year',event_name = 'status2', n, I, b)

Value

A list of matrices for every cross validation data set with $\hat{h}_x(t)$ for all $x$ on the marker grid and $t$ on the time grid.

Arguments

data: A data frame of time dependent data points. Missing values are allowed.
data.id: An id data frame obtained from to_id.
marker_name: The column name of the marker values in the data frame data.
event_time_name: The column name of the event times in the data frame data.
time_name: The column name of the times the marker values were observed in the data frame data.
event_name: The column name of the events in the data frame data.
n: Number of individuals.
I: Number of observations leave out for a K cross validation.
b: Bandwidth.

Details

The function splits the data set via dataset_split and calculates for every splitted data set the hqm estimator $$\hat{h}_x(t) = \frac{\sum_{i=1}^n \int_0^T\hat{\alpha}_i(X_i(t+s))Z_i(t+s)Z_i(s)K_{b}(x-X_i(s))\mathrm {d}s}{\sum_{i=1}^n\int_0^TZ_i(t+s)Z_i(s)K_{b}(x-X_i(s))\mathrm {d}s},$$ for all $x$ on the marker grid and $t$ on the time grid, where $X$ is the marker, $Z$ is the exposure and $\alpha(z)$ is the marker-only hazard, see get_alpha for more details.

Examples

Run this code

# \donttest{
pbc2_id = to_id(pbc2)
n = max(as.numeric(pbc2$id))
b = 1.5
I = 26
h_xt_mat_list = prep_cv(pbc2, pbc2_id, 'serBilir', 'years', 'year', 'status2', n, I, b)
# }