Batched

The time domain of observation will first be standardized into [0,1]. Then [0,1] will be divided into S equally spaced intervals as described in Jiang et al.(2021, Biometrics).
Then those intervals slice the dataset to S batches of data.

Implements the dynamic logistic state space model for binary outcome data proposed by Jiang et al. (2021) <doi:10.1111/biom.13593>.
It provides a computationally efficient way to update the prediction whenever new data becomes available.
It allows for both time-varying and time-invariant coefficients, and use cubic smoothing splines to model varying coefficients.
The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at pre-specified time intervals.

Jiakun Jiang

DLSSM

Dynamic Logistic State Space Prediction Model

Wei Yang

Wensheng Guo

Batched function

<dl><dt>formula</dt>
<dd>An object of class "formula" (or one that can be coerced to that class): a symbolic description of response and covariates in the model.</dd>
<dt>data</dt>
<dd>Dataset matrix containing the observations (one rows is a sample).</dd>
<dt>time</dt>
<dd>The time variable in the dataset. The varying coefficient functions are assumed to be smooth functions of this variable.</dd>
<dt>S</dt>
<dd>Number of batches</dd></dl>

Arguments

Jiakun Jiang, Wei Yang, Wensheng Guo

Author

Combine data into Batched data — Batched

<dl>

<dt>formula</dt>
<dd>An object of class "formula" (or one that can be coerced to that class): a symbolic description of response and covariates in the model.</dd>


<dt>data</dt>
<dd>Dataset matrix containing the observations (one rows is a sample).</dd>


<dt>time</dt>
<dd>The time variable in the dataset. The varying coefficient functions are assumed to be smooth functions of this variable.</dd>


<dt>S</dt>
<dd>Number of batches</dd>

</dl>

`batched`	List of batched data, the element of list is matrix with each row representing a sample

`gap.len`	interval length 1/S

Batched: Combine data into Batched data

Description

Usage

Value

Arguments

Author