R: Dynamical Scale, Moments, and Weights

dyn.scale {longitudinal} | R Documentation

## Dynamical Scale, Moments, and Weights

### Description

`time2weights` computes weights corresponding to time points

`dyn.weights` computes these weights for a given `longitudinal` matrix.

`dyn.moments` computes means and variances for the variables in a `longitudinal` object.

`dyn.scale` centers and standardizes a `longitudinal` matrix.

### Usage

      time2weights(t)
      dyn.weights(x)
      dyn.moments(x)
      dyn.scale(x, center=TRUE, scale=TRUE)

### Arguments

`t` | a vector with time points
`x` | a `longitudinal` object, or a matrix
`center` | logical value
`scale` | logical value

### Details

The dynamical weights are computed assuming a linear spline - see Opgen-Rhein and Strimmer (2006a,b). The dynamical mean and variance etc. are then simply weighted versions of the usual empirical estimators.

### Value

A vector with weights (`time2weights` and `dyn.weights`), a list containing the column means and variances (`dyn.moments`), or a rescaled longitudinal matrix (`dyn.scale`).

### Author(s)

Rainer Opgen-Rhein and Korbinian Strimmer (http://strimmerlab.org).

### References

Opgen-Rhein, R., and K. Strimmer. 2006a. Inferring gene dependency networks from genomic longitudinal data: a functional data approach. REVSTAT 4:53-65. (http://www.ine.pt/revstat/pdf/rs060103.pdf)

Opgen-Rhein, R., and K. Strimmer. 2006b. Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data. The 4th International Workshop on Computational Systems Biology, WCSB 2006 (June 12-13, 2006, Tampere, Finland). (http://www.cs.tut.fi/wcsb06/)

### See Also

`wt.scale`.

### Examples

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

    # weights of for the data points in tcell data
    data(tcell)
    dyn.weights(tcell.34)

    # dynamical moments
    dyn.moments(tcell.34)

[Package longitudinal version 1.1.12 Index]