timepoint_process {microbiomeDASim} | R Documentation

## Function for processing and checking the inputed timepoints

### Description

To allow for increased flexibility the user may specify the number of timepoints as either a single value or separately for each individual. There is also an added option about whether to draw the timepoints evenly spaced across the interval of interest or whether to randomly draw them.

### Usage

    timepoint_process(
      num_timepoints,
      t_interval,
      n,
      asynch_time,
      missing_per_subject
    )

### Arguments

`num_timepoints` | either an integer value specifying the number of timepoints per subject or a vector of timepoints for each subject. If supplying a vector the lenght of the vector must equal the total number of subjects.
`t_interval` | numeric vector of length two specifying the interval of time from which to draw observatoins [t_1, t_q]. Assumed to be equally spaced over the interval unless `asynch_time` is set to TRUE.
`n` | numeric value representing the total number of obserations
`asynch_time` | logical indicator designed to randomly sample timepoints over a specified interval if set to TRUE.

### Details

It is assummed that there is a known time interval of interest over which samples will be collected longitudinally on subjects. This interval is specified as [t_1, t_q]. All subjects are assumed to have baseline observations, i.e., t_1.

Over this study interval each subject can have a potentially different number of measurements taken. In the most simple case we assume that all subjects will have the same number of measurements and can specify `num_timepoints` as a single scalar value. Otherwise, we must specify how many timepoints will be collected for each individual. In this latter case `num_timepoints` must have the same length as the number of subjects.

Finally, we can select whether we want the timepoints to be drawn at equal spaces over our study interal, or whether we want to randomly sample asynchronous timepoints. In the asynchronous case we randomly draw from a uniform distribution over the study interval with the restriction that the first observation must occur at t_1.

### Value

Returns a list of the number of timepoints and the times for each unit

[Package microbiomeDASim version 1.1.7 Index]