R: Utility Functions for the "Longitudinal" Data Structure

longitudinal.util {longitudinal} | R Documentation

## Utility Functions for the "Longitudinal" Data Structure

### Description

The above functions are all utility functions for `longitudinal` objects.

`get.time.repeats` returns the measurement design, i.e. the time points and the number of repeats per time point.

`is.equally.spaced` checks whether the distances between subsequent time points are all equal.

`is.regularly.sampled` checks whether the number of measurements are identical across time points.

`has.repeated.measurements` checks whether any time point as been measured more than once.

`combine.longitudinal` combines the measurements of two `longitudinal` objects. These objects must have the same (number of) variables.

`condense.longitudinal` condenses the multiple measurements per time point using an arbitrary function (e.g., mean, median, var).

### Usage

    get.time.repeats(x)
    is.equally.spaced(x)
    is.regularly.sampled(x)
    has.repeated.measurements(x)
    combine.longitudinal(x1, x2)
    condense.longitudinal(x, s, func=median)

### Arguments

`x, x1, x2` | `longitudinal` time series objects
`s` | An integer, or a vector of integers, that designate the set of time series (variables) to condense.
`func` | Univariate function used to summarize the multiple measurements per time point.

### Value

`get.time.repeats` returns a list containing two vectors (`time` and `repeats`).

`is.equally.spaced`, `is.regularly.sampled`, and `has.repeated.measurements` return either TRUE or FALSE.

`combine.longitudinal` returns a `longitudinal` object.

`condense.longitudinal` returns a matrix.

### Author(s)

Korbinian Strimmer (http://strimmerlab.org).

### See Also

`longitudinal`, `tcell`.

### Examples

    # load "longitudinal" library
    library("longitudinal")

    # load tcell data set
    data(tcell)
    dim(tcell.34)
    is.longitudinal(tcell.34)
    summary(tcell.34)

    # information
    get.time.repeats(tcell.34)
    is.equally.spaced(tcell.34)
    is.regularly.sampled(tcell.34)
    has.repeated.measurements(tcell.34)

    # compute the mean value at each time point for the first two gene
    condense.longitudinal(tcell.34, 1:2, mean)

    # combine two time series
    m1 <- matrix(rnorm(100), 50, 2)
    m2 <- matrix(rnorm(100), 50, 2)
    z1 <- as.longitudinal(m1, repeats=c(10,5,5,10,20), time=c(2,8,9,15,16))
    z2 <- as.longitudinal(m2, repeats=c(10,5,5,10,20), time=c(1,8,9,15,20))

    z3 <- combine.longitudinal(z1,z2)
    summary(z3)
    get.time.repeats(z3)  # compare with z1 and z2

[Package longitudinal version 1.1.12 Index]