repdata

Longitudinal observation on single variable at different timepoints. Observations arranged in a column as the patient with corresponding column of ID.

datasets

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC).
Currently support mixed effect regression with or without missing observations by considering
covariance structures. It provides estimates by missing at random and missing not at random assumptions.
In this R package, we present Bayesian approaches that statisticians and clinical
researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by
Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

Atanu Bhattacharjee

longit

High Dimensional Longitudinal Data Analysis Using MCMC

Akash Pawar

Bhrigu Kumar Rajbongshi

repdata function

A <code>tibble</code> with 7 columns which are :<dl class="dl-horizontal">
<dt>Subject</dt><dd>Patient ID</dd>
<dt>Gender</dt><dd>Categorical numeric variable, 1 if Males and 0 if female</dd>
<dt>Age</dt><dd>Time or age at which observations were taken from every subjects</dd>
<dt>x1,...,x4</dt><dd>Columns stating number of observations at age 18,10,12 and 14</dd></dl>

Format

longitudinal data — repdata

A <code>tibble</code> with 7 columns which are :<dl class='dl-horizontal'>
<dt>Subject</dt><dd>Patient ID</dd>
<dt>Gender</dt><dd>Categorical numeric variable, 1 if Males and 0 if female</dd>
<dt>Age</dt><dd>Time or age at which observations were taken from every subjects</dd>
<dt>x1,...,x4</dt><dd>Columns stating number of observations at age 18,10,12 and 14</dd></dl>

repdata: longitudinal data

Description

Usage

Arguments

Format